These questions are related to what previously known as Siebel Analytics is now known as OBIEE i.e Oracle Business Intelligence Enterprise Edition.

” Define repository in terms of Siebel Analytics
o Repository stores the Meta data information. Siebel repository is a file system ,extension of the repository file. rpd.
o META DATA REPOSITORY
o With Siebel Analytics Server, all the rules needed for security, data modeling, aggregate navigation, caching, and connectivity is stored in metadata repositories.
o Each metadata repository can store multiple business models. Siebel Analytics Server can access multiple repositories

” What is the end to end life cycle of Siebel Analytics?
o Siebel Analytics life cycle
1. Gather Business Requirements
2. Identify source systems
3. Design ETL to load to a DW if source data doesn’t exist.
4. Build a repository
5. Build dashboard or use answers for reporting.
6. Define security (LDAP or External table…)
7. Based on performance, decide on aggregations and/or caching mechanism.
8. Testing and QA.

” What were you schemas? How does Siebel Architecture works? Explain the three layers. How do you import sources?
o There are five parts of Siebel Architecture.
1. Clients
2. Siebel analytics Web Server
3. Siebel analytics server
4. Siebel analytics scheduler
5. data sorces
o Metadata that represents the analytical Model Is created using the siebel Analytics Administration tool.
o Repository divided into three layer
1. Physical - Represents the data Sources
2. Business - models the Data sources into Facts And Dimension
3. Presentation - Specifies the users view of the model;rendered in Siebel answer

” If you have 3 facts and 4 dimension and you need to join would you recommend joining fact with fact? If no than what is the option? Why you won’t join fact to fact?
o In the BMM layer, create one logical table (fact) and add the 3 fact table as logical table source

” What is connection pool and how many connection pools did you have in your last project?
o connection pool is needed for every physical database.
o It contains information about the connection to the database, not the database itself.
o Can use either shared user accounts or can use pass-through accounts -Use: USER and PASSWORD for pass through .
o We can have multiple connection pools for each group to avoid waitin

” Purpose of Alias Tables
o An Alias table (Alias) is a physical table with the type of Alias. It is a reference to a logical table source, and inherits all its column definitions and some properties from the logical table source. A logical table source shows how the logical objects are mapped to the physical layer and can be mapped to physical tables, stored procedures, and select statements. An alias table can be a reference to any of these logical table source types.
o Alias Tables can be an important part of designing a physical layer. The following is a list of the main reasons to create an alias table:
” To reuse an existing table more than once in your physical layer (without having to import it several times)
” To set up multiple alias tables, each with different keys, names, or joins
o To help you design sophisticated star or snowflake structures in the business model layer. Alias tables are critical in the process of converting ER Schemas to Dimensional Schemas.

” How do you define the relationship between facts and dimensions in BMM layer?
o Using complex join ,we can define relationship between facts and dimentions in BMM layer.

” What is time series wizard? When and how do you use it?
o We can do comparison for certain measures ( revenue.,sales etc.. ) for current year vs previous year, we can do for month or week and day also
o Identify the time periods need to be compared and then period table keys to the previous time period.
o The period table needs to contain a column that will contain “Year Ago” information.
o The fact tables needs to have year ago totals.
o To use the “Time series wizard”. After creating your business model right click the business model and click on “Time Series Wizard”.
o The Time Series Wizard prompts you to create names for the comparison measures that it adds to the business model.
o The Time Series Wizard prompts you to select the period table used for the comparison measures
o Select the column in the period table that provides the key to the comparison period. This column would be the column containing “Year Ago” information in the period table.
o Select the measures you want to compare and then Select the calculations you want to generate. For ex: Measure: Total Dollars and calculations are Change and Percent change.
o Once the Time series wizard is run the output will be:
a) Aliases for the fact tables (in the physical layer)
b) Joins between period table and alias fact tables
c) Comparison measures
d) Logical table sources
o In the General tab of the Logical table source etc you can find “Generated by Time Series Wizard” in the description section
o Then you can add these comparision measures to the presentation layer for your reports.
o Ex: Total sales of current qtr vs previous qtr vs same qtr year ago

” Did you create any new logical column in BMM layer, how?
o Yes. We can create new logical column in BMM layer.
o Example: Right click on fact table -new lgical column-give name for new logical column like Total cost.
o Now in fact table source,we have one option column mapping, in that we can do all calculation for that new column.

” Can you use physical join in BMM layer?
o yes we can use physical join in BMM layer.when there is SCD type 2 we need complex join in BMM layer.

” Can you use outer join in BMM layer?
o yes we can.When we are doing complex join in BMM layer ,there is one option type,outer join is there.

” What are other ways of improving summary query reports other than Aggregate Navigation and Cache Management
” Indexes
” Join algorithm
” Mat/view query rewrite
” Web proper report design its optimal by making sure that it is not getting any addition column or rows

” What is level-base matrics?
o Leval-base matrics means, having a measure pinned at a certain level of the dimension. For Example, if you have a measure called “Dollars”, you can create a “Level Based Measure” called “Yearly Dollars” which (you guessed it) is Dollars for a Year. This measure will always return the value for the year even if you drill down to a lower level like quarter, month… etc. To create a level based measure, create a new logical column based on the original measure (like Dollars in the example above). Drag and drop the new logical column to the appropriate level in the Dimension hierarchy (in the above example you will drag and drop it to Year in Time Dim
o A LBM is a metric that is defined for a specific level or intersection of levels.
o Monthly Total Sales or Quarterly Sales are the examples.
o You can compare monthly sales with quarterly sales. You can compare customer orders this quarter to orders this year

” What is logging level?Where can you set logging levels?
o You can enable logging level for individual users; you cannot configure a logging level for a group.
o Set the logging level based on the amount of logging you want to do. In normal operations, logging is generally disabled (the logging level is set to 0). If you decide to enable logging, choose a logging
o level of 1 or 2. These two levels are designed for use by Siebel Analytics Server administrators.
o Set Logging Level
1. In the Administration Tool, select Manage > Security.
2. The Security Manager dialog box appears.
3. Double-click the user.s user ID.
4. The User dialog box appears.
5. Set the logging level by clicking the Up or Down arrows next to the Logging Level field

” What is variable in sieble?
o You can use variables in a repository to streamline administrative tasks and modify metadata content dynamically to adjust to a chainging data environment.The Administration Tool includes a Variable Manager for defining variables

” What is system variable and non system variable?
o System variables
o System variables are session variables that the Siebel Analytics Server and Siebel Analytics Web use for specific purposes. System variables have reserved names, which cannot be used for other kinds of variables (such as static or dynamic repository variables, or for nonsystem session variables).
o When using these variables in the Web,preface their names with NQ_SESSION. For example, to filter a column on the value of the variable LOGLEVEL set the filter to the Variable NQ_SESSION.LOGLEVEL.
o Nonsystem variables.
o A common use for nonsystem session variables is setting user filters. For example, you could define a nonsystem variable called SalesRegion that would be initialized to the name of the user.s sales region. You could then set a security filter for all members of a group that would allow them to see only data pertinent to their region.
o When using these variables in the Web, preface their names with NQ_SESSION. For example, to filter a column on the value of the variable SalesRegion set the filter to the Variable NQ_SESSION.SalesRegion.

” What are different types of variables? Explain each.
o There are two classes of variables:
1. Repository variables
2. Session variables.
Repository variables.
A repository variable has a single value at any point in time. There are two types of repository variables:
static : This value persists, and does not change until a Siebel Analytics Server administrator decides to change it.
dynamic:The values are refreshed by data returned from queries. When defining a dynamic repository variable, you will create an initialization block or use a preexisting one that contains a SQL query. You will also set up a schedule that the Siebel Analytics Server will follow to execute the query and periodically refresh the value of the variable.
Session Variables
Session variables are created and assigned a value when each user logs on. There are two types of session variables:
1.system
2.nonsystem.

” What are the cache management? Name all of them and their uses. For Event polling table do u need the table in your physical layer?
o Monitoring and managing the cashe is cache management.There are three ways to do that.
o Disable caching for the system.(INI NQ config file), Cashe persistence time for specified physical tables and Setting event polling table.
o Disable caching for the system.(INI NQ config file :
You can disable caching for the whole system by setting the ENABLE parameter to NO in the NQSConfig.INI file and restarting the Siebel Analytics Server. Disabling caching stops all new cache entries and stops any new queries from using the existing cache. Disabling caching allows you to enable it at a later time without losing any entries already stored in the cache.
o Cashe persistence time for specified physical tables :
You can specify a cachable attribute for each physical table; that is, if queries involving the specified table can be added to the cache to answer future queries. To enable caching for a particular physical table, select the table in the Physical layer of the Administration Tool and select the option Make table cachable in the General tab of the Physical Table properties dialog box. You can also use the Cache Persistence Time settings to specify how long the entries for this table should persist in the query cache. This is useful for OLTP data sources and other data sources that are updated frequently, potentially down to every few seconds.
o Setting event polling table :
Siebel Analytics Server event polling tables store information about updates in the underlying databases. An application (such as an application that loads data into a data mart) could be configured to add rows to an event polling table each time a database table is updated. The Analytics server polls this table at set intervals and invalidates any cache entries corresponding to the updated tables.
o For event polling table ,It is a standalone table and doesn’t require to be joined with other tables in the physical layer

” What is Authentication? How many types of authentication.
o Authentication is the process by which a system verifies, through the use of a user ID and password, that a user has the necessary permissions and authorizations to log in and access data. The Siebel Analytics Server authenticates each connection request it receives.
” Operaing system autentication
” External table authentication
” Database authentication
” LDAP authentication

” What is object level security?
o There are two types of object level security: Repository level and Web level
o Repository level : In presention layar we can set Repository level security by giving permission or deny permission to users/groups to see particular table or column.
o web level:thisprovides security for objects stored in the siebel anlytics web catlog,such as dashboards,dashboards pages,folder,and reportsyou can only view the objects for which you are authorized. For example,a mid level manager may not be granted access to a dashboard containing summary information for an entire department.

” What is data level security?
o This controls the type an amount of data that you can see in a report.When multiple users run the same report the results that are returned to each depend on their access rights and roles in the organization.For example a sales vice president sees results for alll regions, while a sales representative for a particular region sees onlu datafor that region.

” What is the difference between Data Level Security and Object Level Security?
o Data level security controls the type and amount of data that you can see in a reports.Objectlevel security provides security for objects stored in the siebel analytics web catlog, like dashboards,dashboards pages,folder,and reports.

” How do you implement security using External Tables and LDAP?
o Instead of storing user IDs and passwords in a Siebel Analytics Server repository, you can maintain lists of users and their passwords in an external database table and use this table for authentication purposes. The external database table contains user IDs and passwords, and could contain other information, including group membership and display names used for Siebel Analytics Web users. The table could also contain the names of specific database catalogs or schemas to use for each user when querying data
o Instead of storing user IDs and passwords in a Siebel Analytics Server repository, you can have the Siebel Analytics Server pass the user ID and password entered by the user to an LDAP(Lightweight Directory Access Protocol ) server for authentication. The server uses clear text passwords in LDAP authentication. Make sure your LDAP servers are set up to allow this.

” If you have 2 fact and you want to do report on one with quarter level and the other with month level how do you do that with just one time dimension?
o Using levelbase matrics.

” Did you work on a stand alone Siebel system or was it integrated to other platforms?
o Deploying the Siebel analytics platform without other Siebel applications is called Siebel analytics Stand -Alone .If your deployment includes other siebel Analytics Application it called integrated analytics -You can say Stand-Alone siebel analytics

” How to sort columns in rpd and web?
o Sorting on web column, sort in the rpd its sort order column

” If you want to create new logical column where will you create (in repository or dashboard) why?
o I will create new logical column in repository.because if it is in repository,you can use for any report.If you create new logical column in dashboard then it is going to affect on those reports ,which are on that dashboard.you can not use that new logical column for other dashboard(or request)

” What is complex join, and where it is used?
o we can join dimention table and fact table in BMM layer using complex join.when there is SCD type 2 we have to use complex join in Bmm layer.

” If you have dimension table like customer, item, time and fact table like sale and if you want to find out how often a customer comes to store and buys a particular item, what will you do?
o write a query as “SELECT customer_name, item_name, sale_date, sum(qty) FROM customer_dim a, item_dim b, time_dim c, sale_fact d WHERE d.cust_key = a.cust_key AND d.item_key = b.item_key AND d.time_key = c.time_key GROUP BY customer_name, item_name, sale_date”

” You worked on standalone or integrated system?
o Standalone.

” If you want to limit the users by the certain region to access only certain data, what would you do?
o using data level security.
o Siebel Analytics Administrator: go to Manage -> Security in left hand pane u will find the user, groups, LDAP server, Hierarchy
What you can do is select the user and right click and go to properties, you will find two tabs named as users and logon, go to user tab and click at permission button in front of user name you have selected as soon as u click at permission you will get a new window with user group permission having three tabs named as general ,query limits and filter and you can specify your condition at filter tab, in which you can select presentation table ,presentation columns ,logical table and logical columns where you can apply the condition according to your requirement for the selected user or groups.

” If there are 100 users accessing data, and you want to know the logging details of all the users, where can you find that?
o To set a user.s logging level
1. In the Administration Tool, select Manage > Security.
The Security Manager dialog box appears.
2. Double-click the user.s user ID. The User dialog box appears.
3. Set the logging level by clicking the Up or Down arrows next to the Logging Level field

” How do implement event polling table?
o Siebel Analytics Server event polling tables store information about updates in the underlying databases. An application (such as an application that loads data into a data mart) could be configured to add rows to an event polling table each time a database table is updated. The Analytics server polls this table at set intervals and invalidates any cache entries corresponding to the updated tables.
” Can you migrate the presentation layer only to different server
o No we can’t do only presentation layer. And ask him for more information and use one of the above answers
o Create a ODBC connection in the different serve and access the layer.
o Copy the Rpd and migrate it to other server

” Define pipeline. Did you use it in your projects?
o Yes, pipelines are the stages in a particular transaction. assessment, finance etc.

” How do you create filter on repository?
o Where condition on content tab.

” How do you work in a multi user environment? What are the steps?
o Create a shared directory on the network for Multi-user Development (MUD).
o Open the rpd to use in MUD. From Tools->Options, setup the MUD directory to point to the above directory.
o Define projects within the rpd to allow multiple users to develop within their subject area or Facts.
o Save and move the rpd to the shared directory setup in point 1.
o When users work in the MUD mode, they open the admin tool and start with
o MUD ->Checkout to checkout the project they need to work on (not use the File open as you would usually do).
o After completely the development, user checkin the changes back to the network and merge the changes.

” Where are passwords for userid? Ldap,external table authentication stored respectively?
o passwords for userid are in siebel analytics server repository Ldap authentication in Ldap server external database in a table in external database

” Can you bypass siebel analytics server security ?if so how?
o yes you can by-pass by setting authententication type in NQSCONFIG file in the security section as:authentication_type=bypass_nqs.instanceconfig.xml and nqsconfig.ini are the 2 places
” Where can you add new groups and set permissions?
o you can add groups by going to manage>security>add new groups> You can give permissions to a group for query limitation and filter conditions.

” what are the things you can do in the BMM layer?
o Aggrigation navigation,level base matrics,time series wizard,create new logical column,comlex join.

” what is Ragged hierarchy? and how do u manage it
o Ragged Hierarchy is one of the different kinds of hierarchy.
o A hierarchy in which each level has a consistent meaning, but the branches have inconsistent depths because at least one member attribute in a branch level is unpopulated. A ragged hierarchy can represent a geographic hierarchy in which the meaning of each level such as city or country is used consistently, but the depth of the hierarchy varies.
o For example, a geographic hierarchy that has Continent, Country, Province/State, and City levels defined. One branch has North America as the Continent, United States as the Country, California as the Province or State, and San Francisco as the City. However, the hierarchy becomes ragged when one member does not have an entry at all of the levels. For example, another branch has Europe as the Continent, Greece as the Country, and Athens as the City, but has no entry for the Province or State level because this level is not applicable to Greece for the business model in this example. In this example, the Greece and United States branches descend to different depths, creating a ragged hierarchy.

” What is the difference between Single Logical Table Source and Multiple Logical Table Sources?
o If a logical table in BMM layer has only one Table as the source table then it is Single LTS.
o If the logical table in BMM layer has more than one table as the sources to it then it is called Multiple LTS.
o Ex: Usually Fact table has Multiple LTS’, for which sources will be coming from different Physical tables.
” Can you let me know how many aggregate tables you have in your project? On what basis have you created them?
o As per resume justification document

” How do you bring/relate the aggregate tables into the Siebel analytics Logical layer?
o One way of bringing the Aggregate Tables into the BMM layer is by bringing them as Logical Table sources for the corresponding Fact table.
o This is done by dragging and dropping the aggregate table into the corresponding fact table. After doing that establish the column mappings and the set the aggregation levels.

” How do you know which report is hitting which table, either the fact table or the aggregate table?
o After running the report, go to “Administration” tab and go to click on “Manage Sessions”. There you can find the queries that are run and in the “View Log” option in the Session Management you can find which report is hitting which table.

” Suppose I have report which is running for about 3 minutes typically. What is the first step you take to improve the performance of the query?
o Find the sql query of the report in Admin->manage Session-> run the sql query on toad ->read the explain plan output ->modify the SQL based on the explain plan output

” Suppose you have a report which has the option of running on aggregate table. How does the tool know to hit the Aggregate table and for that what the steps you follow to configure them?
o Explain the process of Aggregate navigation
” Have you heard of Implicit Facts? If, so what are they?
o An implicit fact column is a column that will be added to a query when it contains columns from two or more dimension tables and no measures. You will not see the column in the results. It is used to specify a default join path between dimension tables when there are several possible alternatives.
o For example, there might be many star schemas in the database that have the Campaign dimension and the Customer dimension, such as the following stars:
” Campaign History star. Stores customers targeted in campaign.
” Campaign Response star. Stores customer responses to a campaign.
” Order star. Stores customers who placed orders as a result of a campaign.
In this example, because Campaign and Customer information might appear in many segmentation catalogs, users selecting to count customers from the targeted campaigns catalog would be expecting to count customers that have been targeted in specific campaigns.
” To make sure that the join relationship between Customers and Campaigns is through the campaign history fact table, a campaign history implicit fact needs to be specified in Campaign History segmentation catalog. The following guidelines should be followed in creating
” segmentation catalogs:
” Each segmentation catalog should be created so that all columns come from only one physical star.
” Because the Marketing module user interface has special features that allow users to specify their aggregations, level-based measures typically should not be exposed to segmentation users in a segmentation catalog.

” What is aggregate navigation? How do you configure the Aggregate tables in Siebel Analytics?
o Aggregate tables store precomputed results, which are measures that have been aggregated (typically summed) over a set of dimensional attributes. Using aggregate tables is a very popular technique for speeding up query response times in decision support systems.
o If you are writing SQL queries or using a tool that only understands what physical tables exist (and not their meaning), taking advantage of aggregate tables and putting them to good use becomes more difficult as the number of aggregate tables increases. The aggregate navigation capability of the Siebel Analytics Server, however, allows queries to use the information stored in aggregate tables automatically, without query authors or query tools having to specify aggregate tables in their queries. The Siebel Analytics Server allows you to concentrate on asking the right business question; the server decides which tables provide the fastest answers.

” (Assume you are in BMM layer) We have 4 dimension tables, in that, 2 tables need to have hierarchy, then in such a case is it mandatory to create hierarchies for all the dimension tables?
o No, its not mandatory to define hierarchies to other Dimension tables.

” Can you have multiple data sources in Siebel Analytics?
o Yes.

” How do you deal with case statement and expressions in siebel analytics?
o use expression builder to create case when…then.. end statement

” Do you know about Initialization Blocks? Can you give me an example where you used them?
o Init blocks are used for instantiating a session when a user logs in.
o To create dynamic variable you have to create IB to write sql statement.

” what is query repository tool?
o It is utility of Seibel/OBIEE Admin tool
o allows you to examine the repository metadata tool
o for example: search for objects based on name,type.
o Examine relationship between metadata objects like which column in the presentation layer maps to which table in physical layer

” what is JDK and why do we need it?
o Java Development Kit (JDK), A software package that contains the minimal set of tools needed to write, compile, debug, and run Java applets.

” Oracle doesn’t recommend Opaque Views because of performance considerations, so why/when do we use them?
o an opaque view is a physical layer table that consists of select statement. an opaque view should be used only if there is no other solution.

” Can you migrate the presentation layer to a different server.
o No we have to migrate the whole web & rpd files

” How do you identify what are the dimension tables and how do you decide them during the Business/Data modeling?
o Dimension tables contain descriptions that data analysts use as they query the database. For example, the Store table contains store names and addresses; the Product table contains product packaging information; and the Period table contains month, quarter, and year values. Every table contains a primary key that consists of one or more columns; each row in a table is uniquely identified by its primary-key value or values

” Why do we have multiple LTS in BMM layer?What is the purpose?
o to improve the performance and query response time.

” what is the full form of rpd?
o there is no full form for rpd as such, it is just a repository file (Rapidfile Database)

” how do i disable cache for only 2 particular tables?
o in the physical layer, right click on the table there we will have the option which says cacheable

” How do you split a table in the rpd given the condition. ( the condition given was Broker and customer in the same table) Split Broker and customer.
o we need to make an alias table in the physical layer.

” What type of protocol did you use in SAS?
o TCP/IP

These questions are related to what previously known as Siebel Analytics is now known as OBIEE i.e Oracle Business Intelligence Enterprise Edition.

” Define repository in terms of Siebel Analytics
o Repository stores the Meta data information. Siebel repository is a file system ,extension of the repository file. rpd.
o META DATA REPOSITORY
o With Siebel Analytics Server, all the rules needed for security, data modeling, aggregate navigation, caching, and connectivity is stored in metadata repositories.
o Each metadata repository can store multiple business models. Siebel Analytics Server can access multiple repositories

” What is the end to end life cycle of Siebel Analytics?
o Siebel Analytics life cycle
1. Gather Business Requirements
2. Identify source systems
3. Design ETL to load to a DW if source data doesn’t exist.
4. Build a repository
5. Build dashboard or use answers for reporting.
6. Define security (LDAP or External table…)
7. Based on performance, decide on aggregations and/or caching mechanism.
8. Testing and QA.

” What were you schemas? How does Siebel Architecture works? Explain the three layers. How do you import sources?
o There are five parts of Siebel Architecture.
1. Clients
2. Siebel analytics Web Server
3. Siebel analytics server
4. Siebel analytics scheduler
5. data sorces
o Metadata that represents the analytical Model Is created using the siebel Analytics Administration tool.
o Repository divided into three layer
1. Physical - Represents the data Sources
2. Business - models the Data sources into Facts And Dimension
3. Presentation - Specifies the users view of the model;rendered in Siebel answer

” If you have 3 facts and 4 dimension and you need to join would you recommend joining fact with fact? If no than what is the option? Why you won’t join fact to fact?
o In the BMM layer, create one logical table (fact) and add the 3 fact table as logical table source

” What is connection pool and how many connection pools did you have in your last project?
o connection pool is needed for every physical database.
o It contains information about the connection to the database, not the database itself.
o Can use either shared user accounts or can use pass-through accounts -Use: USER and PASSWORD for pass through .
o We can have multiple connection pools for each group to avoid waitin

” Purpose of Alias Tables
o An Alias table (Alias) is a physical table with the type of Alias. It is a reference to a logical table source, and inherits all its column definitions and some properties from the logical table source. A logical table source shows how the logical objects are mapped to the physical layer and can be mapped to physical tables, stored procedures, and select statements. An alias table can be a reference to any of these logical table source types.
o Alias Tables can be an important part of designing a physical layer. The following is a list of the main reasons to create an alias table:
” To reuse an existing table more than once in your physical layer (without having to import it several times)
” To set up multiple alias tables, each with different keys, names, or joins
o To help you design sophisticated star or snowflake structures in the business model layer. Alias tables are critical in the process of converting ER Schemas to Dimensional Schemas.

” How do you define the relationship between facts and dimensions in BMM layer?
o Using complex join ,we can define relationship between facts and dimentions in BMM layer.

” What is time series wizard? When and how do you use it?
o We can do comparison for certain measures ( revenue.,sales etc.. ) for current year vs previous year, we can do for month or week and day also
o Identify the time periods need to be compared and then period table keys to the previous time period.
o The period table needs to contain a column that will contain “Year Ago” information.
o The fact tables needs to have year ago totals.
o To use the “Time series wizard”. After creating your business model right click the business model and click on “Time Series Wizard”.
o The Time Series Wizard prompts you to create names for the comparison measures that it adds to the business model.
o The Time Series Wizard prompts you to select the period table used for the comparison measures
o Select the column in the period table that provides the key to the comparison period. This column would be the column containing “Year Ago” information in the period table.
o Select the measures you want to compare and then Select the calculations you want to generate. For ex: Measure: Total Dollars and calculations are Change and Percent change.
o Once the Time series wizard is run the output will be:
a) Aliases for the fact tables (in the physical layer)
b) Joins between period table and alias fact tables
c) Comparison measures
d) Logical table sources
o In the General tab of the Logical table source etc you can find “Generated by Time Series Wizard” in the description section
o Then you can add these comparision measures to the presentation layer for your reports.
o Ex: Total sales of current qtr vs previous qtr vs same qtr year ago

” Did you create any new logical column in BMM layer, how?
o Yes. We can create new logical column in BMM layer.
o Example: Right click on fact table -new lgical column-give name for new logical column like Total cost.
o Now in fact table source,we have one option column mapping, in that we can do all calculation for that new column.

” Can you use physical join in BMM layer?
o yes we can use physical join in BMM layer.when there is SCD type 2 we need complex join in BMM layer.

” Can you use outer join in BMM layer?
o yes we can.When we are doing complex join in BMM layer ,there is one option type,outer join is there.

” What are other ways of improving summary query reports other than Aggregate Navigation and Cache Management
” Indexes
” Join algorithm
” Mat/view query rewrite
” Web proper report design its optimal by making sure that it is not getting any addition column or rows

” What is level-base matrics?
o Leval-base matrics means, having a measure pinned at a certain level of the dimension. For Example, if you have a measure called “Dollars”, you can create a “Level Based Measure” called “Yearly Dollars” which (you guessed it) is Dollars for a Year. This measure will always return the value for the year even if you drill down to a lower level like quarter, month… etc. To create a level based measure, create a new logical column based on the original measure (like Dollars in the example above). Drag and drop the new logical column to the appropriate level in the Dimension hierarchy (in the above example you will drag and drop it to Year in Time Dim
o A LBM is a metric that is defined for a specific level or intersection of levels.
o Monthly Total Sales or Quarterly Sales are the examples.
o You can compare monthly sales with quarterly sales. You can compare customer orders this quarter to orders this year

” What is logging level?Where can you set logging levels?
o You can enable logging level for individual users; you cannot configure a logging level for a group.
o Set the logging level based on the amount of logging you want to do. In normal operations, logging is generally disabled (the logging level is set to 0). If you decide to enable logging, choose a logging
o level of 1 or 2. These two levels are designed for use by Siebel Analytics Server administrators.
o Set Logging Level
1. In the Administration Tool, select Manage > Security.
2. The Security Manager dialog box appears.
3. Double-click the user.s user ID.
4. The User dialog box appears.
5. Set the logging level by clicking the Up or Down arrows next to the Logging Level field

” What is variable in sieble?
o You can use variables in a repository to streamline administrative tasks and modify metadata content dynamically to adjust to a chainging data environment.The Administration Tool includes a Variable Manager for defining variables

” What is system variable and non system variable?
o System variables
o System variables are session variables that the Siebel Analytics Server and Siebel Analytics Web use for specific purposes. System variables have reserved names, which cannot be used for other kinds of variables (such as static or dynamic repository variables, or for nonsystem session variables).
o When using these variables in the Web,preface their names with NQ_SESSION. For example, to filter a column on the value of the variable LOGLEVEL set the filter to the Variable NQ_SESSION.LOGLEVEL.
o Nonsystem variables.
o A common use for nonsystem session variables is setting user filters. For example, you could define a nonsystem variable called SalesRegion that would be initialized to the name of the user.s sales region. You could then set a security filter for all members of a group that would allow them to see only data pertinent to their region.
o When using these variables in the Web, preface their names with NQ_SESSION. For example, to filter a column on the value of the variable SalesRegion set the filter to the Variable NQ_SESSION.SalesRegion.

” What are different types of variables? Explain each.
o There are two classes of variables:
1. Repository variables
2. Session variables.
Repository variables.
A repository variable has a single value at any point in time. There are two types of repository variables:
static : This value persists, and does not change until a Siebel Analytics Server administrator decides to change it.
dynamic:The values are refreshed by data returned from queries. When defining a dynamic repository variable, you will create an initialization block or use a preexisting one that contains a SQL query. You will also set up a schedule that the Siebel Analytics Server will follow to execute the query and periodically refresh the value of the variable.
Session Variables
Session variables are created and assigned a value when each user logs on. There are two types of session variables:
1.system
2.nonsystem.

” What are the cache management? Name all of them and their uses. For Event polling table do u need the table in your physical layer?
o Monitoring and managing the cashe is cache management.There are three ways to do that.
o Disable caching for the system.(INI NQ config file), Cashe persistence time for specified physical tables and Setting event polling table.
o Disable caching for the system.(INI NQ config file :
You can disable caching for the whole system by setting the ENABLE parameter to NO in the NQSConfig.INI file and restarting the Siebel Analytics Server. Disabling caching stops all new cache entries and stops any new queries from using the existing cache. Disabling caching allows you to enable it at a later time without losing any entries already stored in the cache.
o Cashe persistence time for specified physical tables :
You can specify a cachable attribute for each physical table; that is, if queries involving the specified table can be added to the cache to answer future queries. To enable caching for a particular physical table, select the table in the Physical layer of the Administration Tool and select the option Make table cachable in the General tab of the Physical Table properties dialog box. You can also use the Cache Persistence Time settings to specify how long the entries for this table should persist in the query cache. This is useful for OLTP data sources and other data sources that are updated frequently, potentially down to every few seconds.
o Setting event polling table :
Siebel Analytics Server event polling tables store information about updates in the underlying databases. An application (such as an application that loads data into a data mart) could be configured to add rows to an event polling table each time a database table is updated. The Analytics server polls this table at set intervals and invalidates any cache entries corresponding to the updated tables.
o For event polling table ,It is a standalone table and doesn’t require to be joined with other tables in the physical layer

” What is Authentication? How many types of authentication.
o Authentication is the process by which a system verifies, through the use of a user ID and password, that a user has the necessary permissions and authorizations to log in and access data. The Siebel Analytics Server authenticates each connection request it receives.
” Operaing system autentication
” External table authentication
” Database authentication
” LDAP authentication

” What is object level security?
o There are two types of object level security: Repository level and Web level
o Repository level : In presention layar we can set Repository level security by giving permission or deny permission to users/groups to see particular table or column.
o web level:thisprovides security for objects stored in the siebel anlytics web catlog,such as dashboards,dashboards pages,folder,and reportsyou can only view the objects for which you are authorized. For example,a mid level manager may not be granted access to a dashboard containing summary information for an entire department.

” What is data level security?
o This controls the type an amount of data that you can see in a report.When multiple users run the same report the results that are returned to each depend on their access rights and roles in the organization.For example a sales vice president sees results for alll regions, while a sales representative for a particular region sees onlu datafor that region.

” What is the difference between Data Level Security and Object Level Security?
o Data level security controls the type and amount of data that you can see in a reports.Objectlevel security provides security for objects stored in the siebel analytics web catlog, like dashboards,dashboards pages,folder,and reports.

” How do you implement security using External Tables and LDAP?
o Instead of storing user IDs and passwords in a Siebel Analytics Server repository, you can maintain lists of users and their passwords in an external database table and use this table for authentication purposes. The external database table contains user IDs and passwords, and could contain other information, including group membership and display names used for Siebel Analytics Web users. The table could also contain the names of specific database catalogs or schemas to use for each user when querying data
o Instead of storing user IDs and passwords in a Siebel Analytics Server repository, you can have the Siebel Analytics Server pass the user ID and password entered by the user to an LDAP(Lightweight Directory Access Protocol ) server for authentication. The server uses clear text passwords in LDAP authentication. Make sure your LDAP servers are set up to allow this.

” If you have 2 fact and you want to do report on one with quarter level and the other with month level how do you do that with just one time dimension?
o Using levelbase matrics.

” Did you work on a stand alone Siebel system or was it integrated to other platforms?
o Deploying the Siebel analytics platform without other Siebel applications is called Siebel analytics Stand -Alone .If your deployment includes other siebel Analytics Application it called integrated analytics -You can say Stand-Alone siebel analytics

” How to sort columns in rpd and web?
o Sorting on web column, sort in the rpd its sort order column

” If you want to create new logical column where will you create (in repository or dashboard) why?
o I will create new logical column in repository.because if it is in repository,you can use for any report.If you create new logical column in dashboard then it is going to affect on those reports ,which are on that dashboard.you can not use that new logical column for other dashboard(or request)

” What is complex join, and where it is used?
o we can join dimention table and fact table in BMM layer using complex join.when there is SCD type 2 we have to use complex join in Bmm layer.

” If you have dimension table like customer, item, time and fact table like sale and if you want to find out how often a customer comes to store and buys a particular item, what will you do?
o write a query as “SELECT customer_name, item_name, sale_date, sum(qty) FROM customer_dim a, item_dim b, time_dim c, sale_fact d WHERE d.cust_key = a.cust_key AND d.item_key = b.item_key AND d.time_key = c.time_key GROUP BY customer_name, item_name, sale_date”

” You worked on standalone or integrated system?
o Standalone.

” If you want to limit the users by the certain region to access only certain data, what would you do?
o using data level security.
o Siebel Analytics Administrator: go to Manage -> Security in left hand pane u will find the user, groups, LDAP server, Hierarchy
What you can do is select the user and right click and go to properties, you will find two tabs named as users and logon, go to user tab and click at permission button in front of user name you have selected as soon as u click at permission you will get a new window with user group permission having three tabs named as general ,query limits and filter and you can specify your condition at filter tab, in which you can select presentation table ,presentation columns ,logical table and logical columns where you can apply the condition according to your requirement for the selected user or groups.

” If there are 100 users accessing data, and you want to know the logging details of all the users, where can you find that?
o To set a user.s logging level
1. In the Administration Tool, select Manage > Security.
The Security Manager dialog box appears.
2. Double-click the user.s user ID. The User dialog box appears.
3. Set the logging level by clicking the Up or Down arrows next to the Logging Level field

” How do implement event polling table?
o Siebel Analytics Server event polling tables store information about updates in the underlying databases. An application (such as an application that loads data into a data mart) could be configured to add rows to an event polling table each time a database table is updated. The Analytics server polls this table at set intervals and invalidates any cache entries corresponding to the updated tables.
” Can you migrate the presentation layer only to different server
o No we can’t do only presentation layer. And ask him for more information and use one of the above answers
o Create a ODBC connection in the different serve and access the layer.
o Copy the Rpd and migrate it to other server

” Define pipeline. Did you use it in your projects?
o Yes, pipelines are the stages in a particular transaction. assessment, finance etc.

” How do you create filter on repository?
o Where condition on content tab.

” How do you work in a multi user environment? What are the steps?
o Create a shared directory on the network for Multi-user Development (MUD).
o Open the rpd to use in MUD. From Tools->Options, setup the MUD directory to point to the above directory.
o Define projects within the rpd to allow multiple users to develop within their subject area or Facts.
o Save and move the rpd to the shared directory setup in point 1.
o When users work in the MUD mode, they open the admin tool and start with
o MUD ->Checkout to checkout the project they need to work on (not use the File open as you would usually do).
o After completely the development, user checkin the changes back to the network and merge the changes.

” Where are passwords for userid? Ldap,external table authentication stored respectively?
o passwords for userid are in siebel analytics server repository Ldap authentication in Ldap server external database in a table in external database

” Can you bypass siebel analytics server security ?if so how?
o yes you can by-pass by setting authententication type in NQSCONFIG file in the security section as:authentication_type=bypass_nqs.instanceconfig.xml and nqsconfig.ini are the 2 places
” Where can you add new groups and set permissions?
o you can add groups by going to manage>security>add new groups> You can give permissions to a group for query limitation and filter conditions.

” what are the things you can do in the BMM layer?
o Aggrigation navigation,level base matrics,time series wizard,create new logical column,comlex join.

” what is Ragged hierarchy? and how do u manage it
o Ragged Hierarchy is one of the different kinds of hierarchy.
o A hierarchy in which each level has a consistent meaning, but the branches have inconsistent depths because at least one member attribute in a branch level is unpopulated. A ragged hierarchy can represent a geographic hierarchy in which the meaning of each level such as city or country is used consistently, but the depth of the hierarchy varies.
o For example, a geographic hierarchy that has Continent, Country, Province/State, and City levels defined. One branch has North America as the Continent, United States as the Country, California as the Province or State, and San Francisco as the City. However, the hierarchy becomes ragged when one member does not have an entry at all of the levels. For example, another branch has Europe as the Continent, Greece as the Country, and Athens as the City, but has no entry for the Province or State level because this level is not applicable to Greece for the business model in this example. In this example, the Greece and United States branches descend to different depths, creating a ragged hierarchy.

” What is the difference between Single Logical Table Source and Multiple Logical Table Sources?
o If a logical table in BMM layer has only one Table as the source table then it is Single LTS.
o If the logical table in BMM layer has more than one table as the sources to it then it is called Multiple LTS.
o Ex: Usually Fact table has Multiple LTS’, for which sources will be coming from different Physical tables.
” Can you let me know how many aggregate tables you have in your project? On what basis have you created them?
o As per resume justification document

” How do you bring/relate the aggregate tables into the Siebel analytics Logical layer?
o One way of bringing the Aggregate Tables into the BMM layer is by bringing them as Logical Table sources for the corresponding Fact table.
o This is done by dragging and dropping the aggregate table into the corresponding fact table. After doing that establish the column mappings and the set the aggregation levels.

” How do you know which report is hitting which table, either the fact table or the aggregate table?
o After running the report, go to “Administration” tab and go to click on “Manage Sessions”. There you can find the queries that are run and in the “View Log” option in the Session Management you can find which report is hitting which table.

” Suppose I have report which is running for about 3 minutes typically. What is the first step you take to improve the performance of the query?
o Find the sql query of the report in Admin->manage Session-> run the sql query on toad ->read the explain plan output ->modify the SQL based on the explain plan output

” Suppose you have a report which has the option of running on aggregate table. How does the tool know to hit the Aggregate table and for that what the steps you follow to configure them?
o Explain the process of Aggregate navigation
” Have you heard of Implicit Facts? If, so what are they?
o An implicit fact column is a column that will be added to a query when it contains columns from two or more dimension tables and no measures. You will not see the column in the results. It is used to specify a default join path between dimension tables when there are several possible alternatives.
o For example, there might be many star schemas in the database that have the Campaign dimension and the Customer dimension, such as the following stars:
” Campaign History star. Stores customers targeted in campaign.
” Campaign Response star. Stores customer responses to a campaign.
” Order star. Stores customers who placed orders as a result of a campaign.
In this example, because Campaign and Customer information might appear in many segmentation catalogs, users selecting to count customers from the targeted campaigns catalog would be expecting to count customers that have been targeted in specific campaigns.
” To make sure that the join relationship between Customers and Campaigns is through the campaign history fact table, a campaign history implicit fact needs to be specified in Campaign History segmentation catalog. The following guidelines should be followed in creating
” segmentation catalogs:
” Each segmentation catalog should be created so that all columns come from only one physical star.
” Because the Marketing module user interface has special features that allow users to specify their aggregations, level-based measures typically should not be exposed to segmentation users in a segmentation catalog.

” What is aggregate navigation? How do you configure the Aggregate tables in Siebel Analytics?
o Aggregate tables store precomputed results, which are measures that have been aggregated (typically summed) over a set of dimensional attributes. Using aggregate tables is a very popular technique for speeding up query response times in decision support systems.
o If you are writing SQL queries or using a tool that only understands what physical tables exist (and not their meaning), taking advantage of aggregate tables and putting them to good use becomes more difficult as the number of aggregate tables increases. The aggregate navigation capability of the Siebel Analytics Server, however, allows queries to use the information stored in aggregate tables automatically, without query authors or query tools having to specify aggregate tables in their queries. The Siebel Analytics Server allows you to concentrate on asking the right business question; the server decides which tables provide the fastest answers.

” (Assume you are in BMM layer) We have 4 dimension tables, in that, 2 tables need to have hierarchy, then in such a case is it mandatory to create hierarchies for all the dimension tables?
o No, its not mandatory to define hierarchies to other Dimension tables.

” Can you have multiple data sources in Siebel Analytics?
o Yes.

” How do you deal with case statement and expressions in siebel analytics?
o use expression builder to create case when…then.. end statement

” Do you know about Initialization Blocks? Can you give me an example where you used them?
o Init blocks are used for instantiating a session when a user logs in.
o To create dynamic variable you have to create IB to write sql statement.

” what is query repository tool?
o It is utility of Seibel/OBIEE Admin tool
o allows you to examine the repository metadata tool
o for example: search for objects based on name,type.
o Examine relationship between metadata objects like which column in the presentation layer maps to which table in physical layer

” what is JDK and why do we need it?
o Java Development Kit (JDK), A software package that contains the minimal set of tools needed to write, compile, debug, and run Java applets.

” Oracle doesn’t recommend Opaque Views because of performance considerations, so why/when do we use them?
o an opaque view is a physical layer table that consists of select statement. an opaque view should be used only if there is no other solution.

” Can you migrate the presentation layer to a different server.
o No we have to migrate the whole web & rpd files

” How do you identify what are the dimension tables and how do you decide them during the Business/Data modeling?
o Dimension tables contain descriptions that data analysts use as they query the database. For example, the Store table contains store names and addresses; the Product table contains product packaging information; and the Period table contains month, quarter, and year values. Every table contains a primary key that consists of one or more columns; each row in a table is uniquely identified by its primary-key value or values

” Why do we have multiple LTS in BMM layer?What is the purpose?
o to improve the performance and query response time.

” what is the full form of rpd?
o there is no full form for rpd as such, it is just a repository file (Rapidfile Database)

” how do i disable cache for only 2 particular tables?
o in the physical layer, right click on the table there we will have the option which says cacheable

” How do you split a table in the rpd given the condition. ( the condition given was Broker and customer in the same table) Split Broker and customer.
o we need to make an alias table in the physical layer.

” What type of protocol did you use in SAS?
o TCP/IP

Tags: OBIEE Interview Question and Answers FAQ

I had been away from Blogging since a while ,I mean really long long time.

My Blog title is BI Training,yes coming up with short and quick bites ( I call it BI-Bites )of info into the BI World related to OBIEE and Oracle BI Apps.I want to share this with you and hope you get this info very helpfull in your project and career.

I will post new blog articles specially for Oracle Businees Intelligence related to OBIEE ,BI Apps, Informatica, DAC with some tips and tricks too

Stay tuned !!!

warm regards,

Kamlesh Jethwa

This topic is shared to provide an overview of Data Conversion for Oracle ERP.

Approach used for Data Conversion

1. Conversion Data Mapping ( map to specify the data of legacy system –> data of oracle ERP system )
The data mapping process provides detailed lists of the data sets and data elements that will need to be moved into the Oracle ERP tables during the data conversion.

During this process, some decisions will need to be made with regard to obtaining information needed by the target application that may not be present in the old system(Legacy System ). Decisions like ->>Default settings, user input, and new data entries are some of the issues that must be addressed during this phase.
The output of this activity is data mapping tables that show what is needed for the Oracle target application processing to meet business operational requirements and where these data elements will come from. Based on this mapping, a design of the legacy data extract is defined.

2. Download Programs ( programs to extract data from database and insert into the Flat file )
These programs are used to extract the identified conversion data elements from the current systems in the form of an ASCII flat file. This can be done with a 3rd party tool or programmatically done within the lagacy database systems.carefull planning must be done before the flat file created as the data can be placed in the order its extracted , type of delimeter used( either ,(comma) or | (pipe) or anything other, number of records, etc.

3. ASCII Flat File (Flat file / Text file)
Most database or file systems output data in text form. A comma or space delimited, variable or fixed format data file from the existing system should be generated. If you cannot find a way to produce clean text data, try generating a report to disk, using a text editor to format your data.
One of the outputs of conversion data mapping is to identify the legacy data element data types and precision(example decimals and dates specifics).

Once the conversion data mapping is done, you should know if there are inconsistencies between the legacy data types and the requirements for the Oracle data types. If there are translations that need to take place, these translations can be performed on the legacy system prior to creating the extract or in an interface table. Also, if you are creating a fixed length extract file, you need to take into account the precision level of decimal numbers.

4. Upload Program
Once data has been put into an ASCII EBCDIC flat file and physically moved onto the same computer that has the Oracle RDBMS, the next step is to load the data into a relational database environment.
Programs must be written and run to move data, validate data, and insert/update standard values into default fields. Usually a single loader program is written for each data table being loaded.

5.Interface Tables ( know more about Interface tables here )

a. Description of Interface Table
The detailed technical description of any interface table that the data is placed into from the ASCII flat file is prepared. An interface table that mimics the production table into which the data will eventually be loaded into should be defined. This allows you to manipulate the data as needed before loading the legacy data into the production tables.

b. Creation of Interface Table
Before loading the Oracle Application production tables, the legacy data should first be loaded into temporary or interface tables. The interface tables provide a location for you to manipulate and translate the data as needed before validating the data and loading the application production tables. These temporary interface tables need to be built before you run the loader script to populate these tables. The interface tables may be standard Oracle Application interface tables or may be custom interface tables.

6. Translation Programs
These scripts are developed to translate data from the existing system format into useful data for the Oracle target application. An example of this might be taking the date format that exists in the legacy system and converting it into an Oracle format. There may be several or no translation programs, depending on both the type of data coming across and the format of that data.

7. Interface Programs ( example )
The interface program scripts are used to populate the production database. The purpose of the interface programs is not only to move the data to the target tables but also to validate the data that would be validated by the form in the target application if the data was converted manually.

8. Application Production Table
This is the final production data table where the converted data resides. These tables are identified early on when doing the initial data mapping. These tables drive some of the translation programs that must ultimately ensure that 100% of the information that the target applications require is present in the final data structures.

9. Testing
This test plan has been integrated into the entire conversion process so that, even during the pre-conversion steps, some type of validation reports are generated from the legacy systems, to be compared later with the converted data.
The approach taken is to use as many standard reports as possible that are available in the legacy and target system for the final data validation. If no reports support the validation requirements, then custom scripts will need to be created for specific validation purposes.

10. Write and Perform Conversion Execution Plan
The conversion execution plan is the execution document that is to be followed when performing the actual conversion. This document is specifically tailored for the <Company Short Name> conversion.

Types of business intelligence tools
The general categories of business intelligence tools are:

1.Spreadsheets[1]

2.Reporting and querying software - are tools that extract, sort, summarize, and present selected data (

Open source software

• Eclipse BIRT
• Agata
• ReportProject
• JasperReports
• Pentaho
• GNU_Enterprise (reporting sub-package) Etc…

Commercial software

• Infocaptor
• Alchemex
• Actuate
• Cognos BI
• Crystal Reports
• InetSoft(InetSoft Style Report)
• Information Builders
• FOCUS and WebFOCUS
• LogiXML
• Microstrategy
• Oracle XML Publisher (now known as ‘BI Publisher’ )
• Business Objects
• Telerik Reporting
• SQL Server Reporting Services
• Proclarity
• RepGen.Net Etc…

3. OLAP

There are many vendors providing OLAP tools

4.Digital Dashboards

here I can recommend one very easy and powerful Dashboard tool called Infocaptor .I keep recomending this because its having all of the same power features of reporting and Dashboards tool as any other top (read expensive) tools .

5.Data mining

6.Process mining

A software framework for the evaluation of process mining algorithms has been developed at the Technical University of Eindhoven and is available as an open source toolkit.

7 .Business performance management

what is BPM ?

Business performance management (BPM) (or Corporate performance management, Enterprise performance management, Operational performance management) is a set of processes that help organizations optimize their business performance. It is a framework for organizing, automating and analyzing business methodologies, metrics, processes and systems that drive business performance.

BPM is seen as the next generation of business intelligence (BI). BPM helps businesses make efficient use of their financial, human, material and other resources

read more related topics at http://en.wikipedia.org/wiki/

please comment and let me know if you liked this article.

brief ( and important ) details of WorkFlow tables.

lets first learn some definations.

A process activity is a modelled workflow process, which can be included as an activity in
other processes to represent a sub–process.

A notification activity sends a message to a performer.

A folder activity is not part of a process,it provides a means of grouping activities.

below is the most important tables for Work Flow module.

WF_ACTIVITIES stores the definition of an activity.
Activities can be processes, notifications, functions or folders.

WF_ITEMS is the runtime table for workflow processes
Each row defines one work item within the system.

WF_ITEM_ATTRIBUTES table stores definitions of attributes associated with a process.
Each row includes the sequence in which the attribute is used as well as the format of the attribute data.

WF_NOTIFICATIONS holds the runtime information about a specific instance of a sent message.
A new row is created in the table each time a message is sent.

find more such Quick info about ERP tables at http://www.appsbi.com/

For decades, companies have been spending millions of $$$’s on systems to streamline and automate the execution of the business plan. Over time, these systems have become more and more integrated within a companies operations and between business partners. However, due to the granularity of the data stored in these systems, management has difficulty accurately monitoring how well the business plan is being executed.

The alternative to making decisions based on feelings or assumptions is often spending hours each month “summarizing reports” to get a macro view of their environment. Often the job of monitoring the success of business practices is so tedious that managers often fore-go the process altogether and try to rely on their gut feelings or “biggest daily headaches” to drive their decision making process. When a manager does need hard data to back up their assumptions, they have to wait for weeks for the backlogged IT department to give them a report (hopefully the right one).

Data Warehouseing Advantages

Dimensional Model based data warehousing is an enabling technology that can put real answers in the hands of your management team in an efficient and timely manner.

Data Orientation- Dimensional modeling provides meaningful data to end users by reorganizing transactional data to a dimensional structure, suited for reporting instead of operating efficiency.

Complexity- By applying consistent and intelligent business rules to raw data, information in the dimensional model can be presented in a simple and straightforward manner, instead of the complex structure found in the transaction systems.

Integrity - By putting transactional data through rigorous tests and applying business logic specific to the companies operations, data warehouse users can be certain that they are viewing complete, accurate, and consistently defined information.

Sources and Accessibility – Instead of having to go to the various information brokers in your organization to get information from various subsystems, dimensional modeling allows disparate data from any enterprise application to be brought together and presented in a “one-stop-shop” approach. Many OLAP (On Line Analytical Processing) tools are deployable over an intranet (or extranet), and can be configured to provide an experience that is unique to each user – giving them access to what they need – and cutting out the noise that they don’t.

Focus – With data organized for reporting, analysis tools can be used to efficiently ask questions about the data, providing insight into how the business operations are really doing. By utilizing one of the many powerful OLAP (On Line Analytical Processing) tools, managers can view the information they specifically want to see – and if necessary adjust the level of detail or type of information that is included in the report.

The Data Warehousing Process

At Integration Management, we believe the key to successful decision support application development and maintenance is an effective and efficient data warehousing process. To make the end product useful, specific attention must be given to the configuration of back-end processes. To make these processes more effective over time, it is important to have back-end processes that can evolve as the business model becomes more understood or changes over time. That is why nearly all IHDS utilities were designed to operate from easily configurable metadata.

Figure 1 - Data Warehousing Process

Readying Source Data

The process of transforming transactional data from multiple sources into an integrated, analysis oriented dimensional structure begins with the replication of transactional data in a SQL Server data store. A key element of this step includes determining the frequency in which the SQL server data store is updated with live data.

Business Rule Application

The heart of a good data warehouse is the process of applying business rules to the raw transactional data to better model the business processes. It is in this area that organizational knowledge and business procedures that are kept outside of the transaction systems are applied to the transaction systems data, creating a more complete picture of what is happening.

Once the required data is “local” to the warehouse in the replicated data store, “scrub” business rules can be applied to the data to search and respond to incomplete or invalid data. When the data set is acceptable, business rules are used to categorize and classify objects and events in the transactional data.

An example of a valuable business rule is the ability to enforce referential integrity on the transaction data. Since some managed care applications do not utilize a true relational database structure, the link between important records is often lost. For example, the provider identified on the claim may not even exist in the provider file. Depending on the safeguards used during the data entry or claim adjudication process, this data condition may not be caught. When standard reports are developed for this particular claim, provider information will not be included, and in some situations, claim may not even be displayed.

After the scrubbing and classification process are complete, the process of mapping the data to the dimensional structure and calculating required business measures is executed. The dimensional structure transforms the data from a complex structure keyed and organized according to the processing of transactions to one that is more intuitive to business analysts, representing how they view the business critical data in your organization.

Multi-Dimensional Modeling

Once the data has been organized in the “Star Schema” dimensional model, it is ready to be used for reporting and analysis. The dimensional data can be used as the data source for various reporting applications such as Crystal Reports and Cognos Impromptu. Multi-dimensional cubes can also be generated to take advantage of OLAP tools such as Cognos Powerplay or Proclarity.

The key to dimensional structure is the organization of data into dimensions and facts. Dimensions hold the “qualitative” characteristics of your data, such as

* Members age, group, plan for specific claim,
* Dates like service, claim, post, and check date
* Claim diagnosis or procedure codes or categories
* Relationships such as IPA, PCP

Facts store detailed or summarized qualitative data that is either detail or summarized values of specific numeric or time data associated with your transactional data. Examples include:

* Cost values on transactions (claim paid amount, claim billed amount, capitation paid amount, etc)
* # of occurrences of specific events (admits per 1000, deliveries per 1000, member months)
* time between events (inpatient days, claim receive date to check date)

After the data is organized into facts and dimensions, it is ready for business analysis driven query development and OLAP tool integration. Records are efficiently linked providing short query time even for the largest of data sets.

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True value of Business Inteligence comes from recognizing and harnessing the power of technology to help change the way you do business. After all – unless there are real decisions made with the information provided by a data warehouse, all you have is a data warehouse, not a decision support application.

By utilizing information and insight gained from your data warehouse and analysis tools, The goal should be to help you keep track of the KPI (key performance indicators) in your business, and help you put into action changes that will improve your business.

The Business Intelligence Life Cycle

Raw Data Capture

Instead of using assumptions based on anecdotal feelings or rumors, decision cycle starts with raw data – the piles of data that is captured as part of your day to day operations. As the normal (and sometimes abnormal) processes of your business are being executed, data such as claims, contracts, authorizations and member contacts are generated from within your organization.

Deriving Information from Business Rules

Next, using input from across your management team and our industry experience, we identify key performance indicators that help measure how well your business is executing. While many of these metrics are consistent across the healthcare industry, how they are calculated may differ from company to company based on different regulator requirements, variations in business models, and unique organizational mission.

We understand that what may be critical to one company may not be so important to another. Our goal would be to find the critical 10-15 measures that indicate how your organization is doing. This “dashboard” of measures would help provide a comprehensive view of the effectiveness of the business model and operations. Examples include: Per Member/Per Month calculations, inpatient days, and claim expense.

Once the key measures are identified, as well as an understanding of how they are to be calculated is defined and agreed upon, the IHDS system can be configured to provide them in ways that make most sense to the management team.

Transactional data that is captured in the first stage of the decision cycle can be transformed, aggregated and presented using the IHDS utilities and methodology.

From the information base, questions about the measures can be asked. The resultant answers allow business analysts, managers and executives to look for trends, top performers, bottom performers, segmentations, correlations, etc.

Business User Oriented Analysis

At this point, the key is providing decision makers with the analytical tools necessary to gain insight from the data warehouse information. Trends, anomalies, and the statistical distribution of these measures can be researched and understood. With the summary and detail information from the data warehouse at their fingertips, management can efficiently monitor the performance of the business.

Developing and Executing Smart Initiatives

Making decisions that truly change the ways your business operates is the goal of our Business Intelligence Services. With our understanding of managed care operations and business model, we can help your management team come up with new initiatives or business practices that will clearly affect your performance, and ultimately your bottom line.

As these changes take place, actual results can be monitored as new data is collected from ongoing business operations. With the ability to monitor the results, initiatives can be evaluated, tweaked, and even scrapped as necessary.

Let’s look at a meaningful claims expense example.

Data accumulation during normal business operations. Whether via EDI or manual entry, claims are entered and adjudicated in your managed care transaction system all day and every day. In order to perform the adjudication process correctly, many data elements must be included on the claim as the claim system matches various benefit, contract, provider and other reference information. All this raw data is accumulated during the normal processing of claims, one of the primary business practices of every health plan.

Transformation of raw data into information. Each night or weekend, pertinent claim data, captured above, flows into the data warehouse. During this process, many business rules execute to transform the raw data into an analysis oriented structure.

Data Scrubs: Data scrubs do the work of ensuring the raw data’s integrity. For example, let’s assume the provider on some claims is not in the master provider table. Instead of leaving this field blank, we can assign a value called “provider not found”, which ensures that the claim information is still considered in the final warehouse measures. Another scrub might be for dates of service that are in the future, produced by a typographical error in the claims department. Instead, this date is changed to the current date.

Categorization: From the claim data, data points are added, adjusted, changed, realigned, or deleted to “categorize” claims the way you need to analyze them. For example, you may want to determine whether the claim is in-network, or out-of-network. However, the process to determine subtypes of out-of-network claims is complicated, based on a series of special codes and a vendor or provider list. These rules can be modeled as business rules in the data warehouse, and executed consistently on each claim.

Mapping: Finally, the raw data must be linked together and mapped to the dimensional structure. While your managed care system manages professional, institutional, pharmacy, and dental claims differently, they can all be combined in the data warehouse to provide a complete picture of member claim costs.

Analysis leads to Insight. Once data is in the dimensional structure, powerful On-Line Analytical Processing tools can be used to find trends and seek out anomalies. For example, you may find that your out-of-network providers claim costs for a specific procedure are increasing. Further investigation points to an increase in the incidence of members seeking medical care from a set of providers in a specific sub-specialty not covered by any in-network contracts.

Making decisions to produce change. Using the insight gleaned from the above analysis, the contracts department may decide to review contracts with existing providers to include the sub-specialty getting the increased attention. Alternatively, they may decide to develop contracts with the group of physicians that are currently out-of-network and performing these specific sub-specialty services.

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Companies want more actionable data, and they want more users to have it. But extending the use of business intelligence throughout the organization remains a challenge. One reason: Lack of BI and IT skill sets continue to plague companies interested in taking BI to the next level, according to David Hatch, research director at Aberdeen Group.

Part of the answer lies in providing more user-friendly BI tools. Hatch offers four tips for achieving that goal.

1. Explore new user-friendly business intelligence tools. New ways of delivering BI can help in your quest to extend business intelligence throughout the enterprise. One new method to consider is BI accessed through a third party—for example, software as a service BI or on-demand BI. In addition, look into the availability of BI as an embedded capability within enterprise applications such as ERP and CRM. Since users are already familiar with your enterprise applications, they may find BI products offered through these providers easier to learn.

2. Find ways to integrate Web 2.0 information into BI. Web 2.0 data sources and other unstructured data do not obviate the need for traditional structured data, says Hatch, but they can be used to boost BI efforts. Amassing large sets of historical data reveals trends, performance metrics and specific business calculations: These are the foundation of most BI efforts. But the ability to enhance that historical data with relevant and timely information found in blogs, comments and competitors’ websites is becoming more important for delivering actionable information throughout the enterprise.

3. Give users BI tools that they can be trained to use autonomously. Employees are more likely to use and embrace business intelligence tools that they can use independently. To create an environment of business intelligence self-sufficiency, establish a group composed of both business users and IT representatives to collaborate on prioritizing user needs and choosing or developing BI tools. Hatch also advises being attuned to inflated vendor claims and involving vendors in proof-of-concept and pilot projects.

4. Consider operational business intelligence. New BI offerings that automate data collection, assembly and delivery processes are one of the most promising areas of business intelligence. To figure out if they’re right for you, look for data generated by business processes that lend themselves to automated analysis and even actions taken on the basis of that analysis. For example, some financial service organizations use applications that automatically analyze fluctuations in currency rates, and that automatically initiate trades based on those decisions. In many manufacturing organizations, data analysis is done automatically on the progress of chemical interactions—temperature, viscosity and color of a mixture, for example—and changes to the mixture are automatically made at the back end before it ever reaches people on the production line.

originally posted by Diann Daniel
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Ask Dennis Hernreich, COO and CFO of Casual Male Retail Group, what his life was like before he switched to an on-demand business intelligence reporting application, and he remembers the frustration all too easily.

Casual Male Retail Group, a specialty retailer of big and tall men’s apparel with $464 million in annual sales, was using a legacy on-premise reporting application for its catalog operations. (The company also has 520 retail outlets and e-commerce operations.) But the reporting features built into the system were “extremely poor,” as Hernreich describes them: “Visibility to the business? Terrible. Real-time information? Doesn’t exist. How are we doing with certain styles by size? Don’t know.”

“It was unacceptable,” Hernreich says. And viewing those “canned” BI reports (which lacked features such as exception reporting) could happen only with making a trip to the printer for a stack of printouts. “It was hundreds of pages,” he recalls. “That’s just not how you operate today.”

It’s not like Casual Male didn’t have all this information; it just didn’t have an intuitive and easy way to get at its catalog business’s sales and inventory trends in real-time. But that changed in 2004, when Casual Male began using a on-demand BI tool from vendor Oco, which takes all of Casual Male’s data, builds and maintains a data warehouse for it offsite, and creates “responsive, real-time reporting dashboards that give us and our business users information at their fingertips,” Hernreich says.

Today, Hernreich and Casual Male’s merchandise planners and buyers have access to easy-to-consume dashboards chock-full of catalog data: “What styles are selling today. How much inventory are we selling today. Where are we short. Where do we need to order. How are we selling by size. What are we out of stock in,” he says. “All of these basic questions, in terms of running the business—that’s what we’re learning every day from these reports.”

And those annoying trips to the printer have ended.

On-Demand Fears Linger
Casual Male Retail Group is part of the small (but growing) percentage of businesses using on-demand or software-as-a-service (SaaS) BI tools, which can be deployed at a much faster pace and with much less initial cost than traditional on-premise software installations.

“To go from nothing to a fully automated system in a matter of weeks is an incredible sell for any company—large or small,” says Scott Cohenford, a senior analyst at RapidAdvance, a provider of cash advances to small and midsize businesses, who led his company’s efforts to purchase Business Object’s OnDemand platform.

Note that Cohenford is not an IT person by title or pedigree (he has an accounting background): ease of use (setup, integration, training) is a major selling point to on-demand BI customers. “I was tasked with reviewing the different options out there, seeing how quickly we could move forward with the solutions and do so at a low cost and automate as much as possible,” Cohenford says. “And that’s what pushed me into the SaaS world’s BI tools.”

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continued from <<

Finding 1
The BI Learning Curve Has Just Begun
Business intelligence use grows considerably as companies gain experience with this technology. Nearly 90 percent of our respondents use BI, and most others will probably start doing so in the next two years. Still, there are relatively few BI veterans out there—companies with more than half a decade of experience using business intelligence to analyze data on customers, competitors, internal processes or financials. BI veterans use BI for more reasons and provide BI tools to staff more than BI novices do. In particular, many more veterans use BI to analyze customer data. Projecting forward, that means BI usage will grow broader—there’ll be more kinds of BI applications—and deeper within organizations in the coming years. Analysts who predict the “democratization” of BI appear correct.

Finding 2
Effectiveness Is a Sometimes Thing
BI brings benefits, but many goals go unmet. In general, IT executives are bullish on BI; nearly three-quarters say it’s doing a good job of contributing to the bottom line and pleasing users. Newcomers to BI will be glad to know the more experience companies have with BI, the happier they are with the results. But drill down to specific goals and benefits, and the track record is spotty. BI is good at analyzing financial data and internal operations. However, it’s not as effective at analyzing marketing campaigns, or cross-selling and upselling to customers. That’s a problem, especially since these are likely to be big growth areas for BI, judging by how companies with extensive BI experience use them.

Finding 3
BI Success Depends on Execution
IT organizations must focus on data management, ease of use and providing real-time information.To achieve their top business goals for BI—improving processes, sales and marketing compliance and so on—CIOs cite three priorities: improving information quality, improving management’s ability to use information, and offering more kinds of information to more people. We asked respondents how they plan to accomplish these goals. No. 1, by a longshot, is improving data management; next, in priority order are improving ease of use, improving generation of real-time information and embedding BI into business processes and workflows. These tactics should be high priorities for any IT organization implementing BI. Unless they’re achieved, companies will be hard-pressed to generate much value from the tools they use to obtain business intelligence.

Finding 4
Garbage In, Bad Intelligence Out
Good data quality and satisfaction with BI information go hand in hand. But unfortunately, many companies report data quality problems reduce the ROI on business intelligence, even after five years’ experience with these systems. CIOs who invest in establishing data quality processes will be rewarded by increased user satisfaction with the information provided by their BI systems. In addition, about half these companies have undertaken a “master data management” initiative. There’s a correlation between master data management and BI’s impact on the bottom line, but it’s not clear if it’s the result of the MDM initiative alone or other efforts to improve data quality as well.

Finding 5
Weak Links in the BI Chain
In addition to data management and quality issues, CIOs must be concerned about strategic vision, integration, usability and weak points in the BI cycle. It’s startling to see that fewer than 40 percent of companies know what they hope to achieve with BI and how they plan to use it. This sets up BI for alignment problems, such as defining BI goals, and clearly affects the contribution BI makes. Lack of integration affects more than one BI user out of three and—no surprise here—it’s especially a problem with companies in which data quality is lacking. Companies are much better at collecting and organizing data than making it usable or putting it to work. No wonder 58 percent of respondents say users don’t know how to analyze or interpret BI data.

read more here

What are the most effective ways to increase the value your company gets from its business intelligence systems?
N=215

1)Better align our business intelligence systems with our business strategy [50% ]

2)Improve data quality [47% ]

3)Better integrate our business intelligence systems with other systems such as CRM or ERP [41% ]

4)Better understand end-user needs and requirements [39%]

5)Improve user training [27 %]

Finding 1: The BI Learning Curve Has Just Begun

Finding 2: Effectiveness Is a Sometimes Thing

Finding 3: BI Success Depends on Execution

Finding 4: Garbage In, Bad Intelligence Out

Finding 5: Weak Links in the BI Chain

Next >> Finding 1: The BI Learning Curve Has Just Begun

Just 26 percent of companies using business intelligence have five or more years’ experience with the technology. Those new to BI should be encouraged by the success of those who’ve gone before.
Overall, business intelligence is an IT success story: Our most recent IT spending survey found that BI spending is increasing more quickly than spending on any other application. This month’s survey reveals that 72 percent of users say their BI efforts have had a major and measurable impact on their companies’ bottom lines. But not all business intelligence goals are being met, and there are problems beneath the veneer of satisfaction.

An overarching question in this year’s BI survey is: Are companies learning how to use BI better? The good news is that experience counts. The longer a company uses BI, the wider BI usage spreads within the organization, the more the organization tries to do with the technology and the bigger the technology’s contribution to the bottom line. That’s just what CIOs would hope to hear. This year’s survey also looks into the practices companies follow to achieve their BI goals.

We see a correlation between experience with BI, data quality and development of a clear strategy and vision for business intelligence use. Still, many long-time BI users report problems. Issues of integration, accessibility and bringing users up to speed on how to analyze and use BI data dog this technology. The value of BI increases with experience, but even veteran BI users see room for improvement, and opportunities to gain more value from BI.

read more >> Top 5 Ways To Increase BI Value

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“We were a very different university about one year ago,” says Keith Werosh, registrar at the National University of Health Sciences (Ill.). He is referring to the operation pre-BI. NUHS installed BI from Business Objects a year ago. “Prior to having this system, we were departmentalized. Information was kept on spreadsheets throughout the university,” he says. The Registrar would have relevant data, but not related information. “Paper touched hands a number of times. Other offices would even rekey the information,” explains Werosh. As a result, NUHS officials wouldn’t have a picture of the enrollment process until weeks into a term.

A BI solution was in order. Even so, NUHS didn’t rush into deploying one. “During a two-year time frame, we discussed what we needed and how we were going to achieve it,” adds Werosh, who was the executive on campus tapped to “get the ball rolling” because much usage would be centered around the registrar’s office.

After the investigative period, NUHS invested approximately $200,000 in a BI system supplied by Business Objects.

The benefits have been multifold. Today the registration process is clearly viewed instantly. “We can process information on the spot,” Werosh reports. NUHS can even determine the peak registration time during the day. For example, most online student traffic falls between 9 a.m. and 10 a.m. “We added additional staff at that time to handle the traffic. Student workers were added to key areas and computer labs since more online use would lead to more questions. Others worked the telephones and were able to call back immediately to those students who had contacted online, but who wanted follow-up,” he says.

Florida State University adopted BI several years earlier, according to Rick Burnett, director of student information management. “We saw data as the low-hanging fruit and wanted to start using it to figure out the best students to target.” To that end, the university, which enrolls 40,000, uses a BI system that links to its CRM system from Talisma, its databases, and other resources, to determine which classes are filling up the fastest. “An alert goes out when a course is 80 percent full.” This, in turn, affects room scheduling, and gives fodder for studying other course offerings.

BI has also added a layer of sophistication to the admissions strategy. Officials at FSU, which receives 55,000 applications each year, once had to cull through each file and hand-code information. If at the end of the decision cycle there was a change in the admissions criteria (such as the need for slightly higher GPA requirements), decision-makers would have to retouch all the files again.

Setting a BI query avoids all that. A new list of qualified applicants can be produced within 30 minutes.

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A new technology can analyze data at amazing speeds. So why is higher ed slow to adopt?

Listen to higher ed futurists and you’ll be convinced that business intelligence is the wave of the future. It is the next “it” thing that will connect every part of the enterprise. Best yet, BI will link all data, allowing disparate software programs, provided by numerous and sometimes-competing vendors, to “talk” to each other.

Admissions files will connect to financial aid information will connect to student services files will connect to academic records, and so on. The end result will be data that can actually predict what that student might do next, and even calculate financial aid needs and shifting interests.

It sounds wonderful, and BI is already fulfilling its promise at some colleges and universities. Trouble is, the BI’s potential is so vast that it remains hard to get a simple understanding of it. Each vendor has its own definition of BI. Some claim that systems such as CRM (constituent/customer relationship management) and ERP (enterprise resource planning) are BI technology. Others say that data warehousing, decision support systems, online analytical processing, knowledge management, and web personalization are all part of BI.

“Everyone and their uncle is calling their solution ‘business intelligence.’ ” -Nicole Engelbert, Datamonitor
So, which is it? The answer may be a little bit of all of the above and then some.

“Everyone and their uncle is calling their solution ‘business intelligence,’ ” notes Nicole Engelbert, senior analyst, public sector technology, at Datamonitor. “Institutions are asking, ‘What does this all mean to me?’ “

Most BI systems have been around for about eight years, and most of the early adopters were in the retail sector.

Engelbert offers her own clarification. ERP, for example, provides transactional reports, she explains. “ERP will give you how many students enrolled on Tuesday, or will tell you if Organic Chemistry 101 is full.” A BI system will actually digest this information, and other data, and tell the user if Organic Chem is being offered at a good time, or if the class is losing students to the Biology 101 class offered at the same time, or if the college needs to offer additional course sections.

Comparatively, CRM systems are more about transaction and functions. They will manage recruitment campaigns, alumni appeals, and other processes. They capture the contacts of those who have visited online, and then drive the related mailing of the viewbook. But while CRM systems will certainly tally who replied and which part of the campaign delivered results, it is BI that will offer a deeper analysis. BI can predict which ZIP codes will be the most promising for a mailing, and which geographic areas will have students with the least or most need for financial aid.

Many ERP vendors in the higher education sector claim to have BI capabilities. Engelbert notes that some have formed partnerships with companies that offer true BI. Datatel’s new partnerships with Business Objects and SAS, both BI providers, are good examples. Similarly, Intelliworks, a CRM provider, has a partnership with Microstrategy.

In mid-2006, Campus Management added two BI modules, thanks to partnerships with iStrategy Solutions and Noetix. One, called CampusQuery, provides analysis at a glance. The other, CampusAnalytics, can do predictive modeling.

BI systems are most noted for their speed and convenience tools that display metrics with spotlight indicators, or dashboards. These dashboards provide quick peeks on trends in real time-and do so in a format the average user can read.

read more here

Let’s first discuss traditional database queries. In the BI environment, a BI tool or application issues a query against a database system. The query is in a formalized database language such as SQL (note that S stands for structured). A GUI may be used to hide the complexities of SQL. The results of the query can be browsed, reported on, analyzed, etc. The emphasis of this style of processing is on the analysis of structured data. It is not designed for the ad hoc browsing of information from a bunch of unrelated data sources.

Typically database query processing is formalized and standardized both in terms of the query language itself and the database structures accessed. There are natural language interfaces for doing this type of processing, but they simply convert the natural language requests into database language statements.

With database query processing you have to have some knowledge (i.e., metadata) about the structure of the data before you can access it. There has been a move to relax the structured approach of structured database processing by adding database structure and language support for accessing and analyzing semi-structured data such as XML.

When it comes to unstructured data there are two options. The first is to transform all or some of the unstructured data into a structured or semi-structured format, and store the transformed data in a database system (together with any remaining unstructured data that has not been converted). The transformed data can be associated with existing structured data in the database. This transformed data can then be processed by database queries, and any associated unstructured data retrieved as a part of the result set.

A second option for unstructured data is to access it directly using a search tool. With a search tool information is accessed using search queries. As with database languages, these queries can be generated from a GUI. The results from search queries can be browsed to find the data of interest. The search results can also be passed to an analysis tool for further processing.

Search languages are designed for accessing freeform unstructured data. Of course search queries can also access both structured and unstructured data. Search query languages are less complex than database query languages, because, unlike languages such as SQL, they are not designed for complex data retrieval and analysis.

To improve the accuracy of search queries, metadata can be extracted from the unstructured data using utilities supplied with the search tool, or by third-party vendors who offer taxonomy and information exploration tools. The better the metadata, the more accurate the search results are likely to be. The metadata adds semantic meaning to the unstructured data. In some cases, the metadata can be used to build faceted or taxonomy-driven search interfaces that use the metadata to filter the search results. The metadata can also be used to aid in the transformation of unstructured data into a semi-structured or structured format. It is these types of capability that separate enterprise search tools from internet search tools.

The bottom line is that the database query and the search query approaches are starting to come together. However, search queries are designed for the browsing of less formalized and unstructured information, whereas database queries are intended for the analysis of structured and semi-structured data. As discussed, unstructured information can be transformed into a semi-structured format, and search results can be further analyzed by analysis tools. Both approaches use some form of query language.

Originally posted by Collin White read more >>

This point is brought home by the recent announcement by Vertica of a strategic relationship with Talend, an open-source data integration vendor. Vertica markets the column-oriented Vertica Database, and has a relationship with HP and Red Hat to offer a bundled hardware and software analytical database solution. Given that Talend also has relationship with open source BI tool vendor Jaspersoft, it means the combination of Vertica, Talend, JasperSoft and Red Hat software on top of an HP hardware platform provides a cost-effective and open source data warehouse and BI environment. If this package was offered as a single solution, and supported by a single vendor, it would represent the ideal data warehouse and BI appliance solution.

In my opinion, the appliance vendors must move toward offering these types of packaged software environments if they are to survive. Simply offering better price/performance is not a viable long-term strategy. The database machine vendors discovered this. In fact, I believe the vendors need to go one step further and provide application appliances that provide a complete business solution. Although it had a proprietary architecture, the IBM AS/400 was a tremendous success because it offered a complete application solution to business users.

This is something which will help the newbee for their familiarity of Oracle apps terms.Also can be found on Oracle download site.

This combined glossary defines terms we use in the Assets, Cash Management, General Ledger, Payables, Projects, and Receivables User’s Guides and in various country-specific and region-specific manuals.

4-4-5 calendar
A depreciation calendar with 12 uneven periods: four cycles of a four-week period, followed by a four-week period, followed by a five-week period. Depreciation is usually divided by days for a 4-4-5 calendar. Since a 4-4-5 calendar has 364 days per year, it has different start and end dates for the fiscal year each year.

1099 form
The forms the Internal Revenue Service supplies to record a particular category of payment or receipt.

1099 number
The tax identification number for a supplier. According to IRS rules in the United States, lack of a valid tax identification number may result in tax withholding.
Oracle Applications stores the tax identification number for each supplier. Oracle Applications also enables you to enter a withholding status for each supplier.

1099 types
A 1099 classification scheme used in the United States for types of payments. Each 1099 form has one or more payment types. A 1099 supplier may receive payments from more than one type. The 1099-MISC form has the following types: rents, royalties, prizes and awards, federal income tax withheld, fishing boat proceeds, medical and health care payments, non-employee compensation, and substitute payments in lieu of dividends or interest.
Oracle Applications records 1099 payments by type so that you can report them according to IRS requirements.

2-way matching
The process of verifying that purchase order and invoice information matches within accepted tolerance levels. Oracle Applications uses the following criteria to verify two-way matching:
Invoice price <= Order price
Quantity billed <= Quantity ordered

24-hour format
A time format that uses a 24 hour clock instead of am and pm, so that 3:30 would be 3:30 am, 16:15 would be 4:15 pm, 19:42 would be 7:42 pm, etc.

3-way matching
The process of verifying that purchase order, invoice, and receiving information matches within accepted tolerance levels. Oracle Applications uses the following criteria to verify three-way matching:
Invoice price <= Purchase Order price
Quantity billed <= Quantity ordered
Quantity billed <= Quantity received

4-way matching
The process of verifying that purchase order, invoice, and receiving information matches within accepted tolerance levels. Oracle Applications uses the following criteria to verify four-way matching:
Invoice price <= Order price
Quantity billed <= Quantity ordered
Quantity billed <= Quantity received
Quantity billed <= Quantity accepted

Business Process Architecture (BP)
BP.010 Define Business and Process Strategy
BP.020 Catalog and Analyze Potential Changes
BP.030 Determine Data Gathering Requirements
BP.040 Develop Current Process Model
BP.050 Review Leading Practices
BP.060 Develop High-Level Process Vision
BP.070 Develop High-Level Process Design
BP.080 Develop Future Process Model
BP.090 Document Business Procedure

Business Requirements Definition (RD)
RD.010 Identify Current Financial and Operating Structure
RD.020 Conduct Current Business Baseline
RD.030 Establish Process and Mapping Summary
RD.040 Gather Business Volumes and Metrics
RD.050 Gather Business Requirements
RD.060 Determine Audit and Control Requirements
RD.070 Identify Business Availability Requirements
RD.080 Identify Reporting and Information Access Requirements

Business Requirements Mapping
BR.010 Analyze High-Level Gaps
BR.020 Prepare mapping environment
BR.030 Map Business requirements
BR.040 Map Business Data
BR.050 Conduct Integration Fit Analysis
BR.060 Create Information Model
BR.070 Create Reporting Fit Analysis
BR.080 Test Business Solutions
BR.090 Confirm Integrated Business Solutions
BR.100 Define Applications Setup
BR.110 Define security Profiles

Application and Technical Architecture (TA)
TA.010 Define Architecture Requirements and Strategy
TA.020 Identify Current Technical Architecture
TA.030 Develop Preliminary Conceptual Architecture
TA.040 Define Application Architecture
TA.050 Define System Availability Strategy
TA.060 Define Reporting and Information Access Strategy
TA.070 Revise Conceptual Architecture
TA.080 Define Application Security Architecture
TA.090 Define Application and Database Server Architecture
TA.100 Define and Propose Architecture Subsystems
TA.110 Define System Capacity Plan
TA.120 Define Platform and Network Architecture
TA.130 Define Application Deployment Plan
TA.140 Assess Performance Risks
TA.150 Define System Management Procedures

Module Design and Build (MD)
MD.010 Define Application Extension Strategy
MD.020 Define and estimate application extensions
MD.030 Define design standards
MD.040 Define Build Standards
MD.050 Create Application extensions functional design
MD.060 Design Database extensions
MD.070 Create Application extensions technical design
MD.080 Review functional and Technical designs
MD.090 Prepare Development environment
MD.100 Create Database extensions
MD.110 Create Application extension modules
MD.120 Create Installation routines

Data Conversion (CV)
CV.010 Define data conversion requirements and strategy
CV.020 Define Conversion standards
CV.030 Prepare conversion environment
CV.040 Perform conversion data mapping
CV.050 Define manual conversion procedures
CV.060 Design conversion programs
CV.070 Prepare conversion test plans
CV.080 Develop conversion programs
CV.090 Perform conversion unit tests
CV.100 Perform conversion business objects
CV.110 Perform conversion validation tests
CV.120 Install conversion programs
CV.130 Convert and verify data

Documentation (DO)
DO.010 Define documentation requirements and strategy
DO.020 Define Documentation standards and procedures
DO.030 Prepare glossary
DO.040 Prepare documentation environment
DO.050 Produce documentation prototypes and templates
DO.060 Publish user reference manual
DO.070 Publish user guide
DO.080 Publish technical reference manual
DO.090 Publish system management guide

Business System Testing (TE)
TE.010 Define testing requirements and strategy
TE.020 Develop unit test script
TE.030 Develop link test script
TE.040 Develop system test script
TE.050 Develop systems integration test script
TE.060 Prepare testing environments
TE.070 Perform unit test
TE.080 Perform link test
TE.090 perform installation test
TE.100 Prepare key users for testing
TE.110 Perform system test
TE.120 Perform systems integration test
TE.130 Perform Acceptance test

PERFORMACE TESTING(PT)
PT.010 - Define Performance Testing Strategy
PT.020 - Identify Performance Test Scenarios
PT.030 - Identify Performance Test Transaction
PT.040 - Create Performance Test Scripts
PT.050 - Design Performance Test Transaction Programs
PT.060 - Design Performance Test Data
PT.070 - Design Test Database Load Programs
PT.080 - Create Performance Test TransactionPrograms
PT.090 - Create Test Database Load Programs
PT.100 - Construct Performance Test Database
PT.110 - Prepare Performance Test Environment
PT.120 - Execute Performance Test

Adoption and Learning (AP)
AP.010 - Define Executive Project Strategy
AP.020 - Conduct Initial Project Team Orientation
AP.030 - Develop Project Team Learning Plan
AP.040 - Prepare Project Team Learning Environment
AP.050 - Conduct Project Team Learning Events
AP.060 - Develop Business Unit Managers’Readiness Plan
AP.070 - Develop Project Readiness Roadmap
AP.080 - Develop and Execute CommunicationCampaign
AP.090 - Develop Managers’ Readiness Plan
AP.100 - Identify Business Process Impact onOrganization
AP.110 - Align Human Performance SupportSystems
AP.120 - Align Information Technology Groups
AP.130 - Conduct User Learning Needs Analysis
AP.140 - Develop User Learning Plan
AP.150 - Develop User Learningware
AP.160 - Prepare User Learning Environment
AP.170 - Conduct User Learning Events
AP.180 - Conduct Effectiveness Assessment

Production Migration (PM)
PM.010 - Define Transition Strategy
PM.020 - Design Production Support Infrastructure
PM.030 - Develop Transition and Contingency Plan
PM.040 - Prepare Production Environment
PM.050 - Set Up Applications
PM.060 - Implement Production Support Infrastructure
PM.070 - Verify Production Readiness
PM.080 - Begin Production
PM.090 - Measure System Performance
PM.100 - Maintain System
PM.110 - Refine Production System
PM.120 - Decommission Former Systems
PM.130 - Propose Future Business Direction
PM.140 - Propose Future Technical Direction