Mdm

MDM - Siperian Info

Master Your Data, Master Your Business

Informatica MDM is designed to make your master data management (MDM) initiative successful. Its
flexible multidomain MDM technology and unique trust framework enable rapid deployments and
extensibility across the enterprise. With a proven track record of success in tackling any data-driven
business problem in any industry, Informatica MDM maximizes business value by delivering
comprehensive, unified, open, and economical MDM on a single platform.

1. Maximize account penetration in MDM by improving cross-sell and up-sell offers with a single or
an extended customer view.

2. Easily comply with regulatory initiatives in MDM by improving the tracking, transparency, and
auditing of financial data.

3. Make decisions around MDM more confidently across the global enterprise through increased
accuracy, reliability, and timeliness of business-critical data.
The new version of Business Data Director significantly improves the overall productivity of business
users by allowing them to customize the look and feel of the application to suit their preferences.
Other new enhancements include Business Data Director's ability to integrate with external applications
and provide "custom action" capabilities, which allow users to extend the data to other applications.
Using advanced data federation features, line of business users can see critical transactional data
within Business Data Director that enables them to perform more complex, real-time business
processes such as order to cash and cross-sell and up-sell of additional products to existing customers.
There are different component of Informatica MDM Siperian HUBmentioned below.

1. MRM
2. BDD
3. HR
4. Console Manager.
5. SIF
Start up of informatica Siperian HUB :-
1. Make sure Oracle services are started on your virtual/Hosted machine.
2. Open command window and enter the following commands to start the JBOSS application
server
3. CD C:\Jboss*\bin
4. Run bat –b 0.0.0.0
5. Open the informatica MDM multidomain Edition Console, by opening internet explorer and go
to http://infa-server : 8080/cmx and click the launch button on the page.
6. Login with user name and Password
7. Select the target database [TR_LAB].
Define Data Model
1. Create target base table object. [Ex ADDRESS table]
2. Enable History while creating staging table.
3. Define the relation ship of base table object with other base table base object [It’s like you
define the schema for base object.]
4. There could be multiple source system for same base table object [like SALES and CRM]
5. Define target data model.
6. Metadata manager option helps in importing a data model from outside Informatica HUB. It can
come from 3rd party vendor tool.
7. Naming convention should be correct for all the Landing, Staging and Target tables to maintain
the metadata relation.
8. “Contains Full Dataset” Property related with Delta detection while loading in landing table.
WORKSHOP MENU [Configuration -> Metadata manager]
There are 2 types of schema in MDM Hub which is

1. System schema: - we do not create these; it got created at the time of installation of the
siperian].
2. ORS schema [we use these schema]
Metadata manager is different than metadata from powercenter.
For importing any table from metadata manager, first we need to validate the ORS schema[database]
then after you will be able to import table from different ORS.

Some Random Info about Siperian Process :-

1. Define the trust level.
2. Dynamic CELL LEVEL Survivorship.
3. IDD is how we consume the clean records from MDM Hub or create message queues.
4. We can use stored procedure or ETL to populate the landing table.
5. ETL must truncate the landing table before loading the landing table.
6. Duplicate records in the landing table will be rejected during the staging process.
(REJECT/RAW tables)
7. There should be provision of auditing data lineage.
8. Base table = Normal Columns + System generated columns.
9. Data Model Elements
10. Provide functionality for matching and merging data. There are different tables which got
created in the the HUB which helps in merging and matching records ex. OPL, MTCH, MGE,
HREF, XREF tables
11. Built in Lineage (Cross-Ref tables) and history.
12. Support trust and validations rules.
13. Supports matching and merging.
14. ROWID_OBJECT is automatically generated and managed. [Port of base table]
15. Each base object has underlying cross-reference (XREF) table and it is created and managed by
the system. Purpose is to cross reference with other base tables.
16. XREF is link between source key and base Object Key.
17. All the system tables start with C_REPO*.
About Staging Table
1. Intermediate work table.
2. Belongs to specific source system.
3. Staging table columns are a selected subset of user-defined columns in base object.
4. Staging table automatically has following columns
 Last_update_Date
 Source key
 SRC_ROWID
5. Staging table will have only one source and one target.
6. Staging table has reject table. It can also have history table.
Relationship type: - you can create only virtual relationship. It can be one to many or many to many.
(In many to many type relations, you need to create a intersection point)
Look Up: - When loading happens from staging to target base object table. Then Look up process also
starts parallel. In other words look up in siperian is a translation of source’s primary or foreign key
value into the corresponding base object value. This specifies the relationship between two different
base objects. Siperian look up is different than informatica powercenter lookup. Hub system
automatically handles look ups for primary key on base objects records, by using the base object’s X-
REF. if no value is found, then the new ROWID_OBJECT value will be generated.

ROW_ID :- System generate their own row_id and connect it to the primary key which is coming from
the source system and store this relationship in the XREF table (can have multiple records from
different source system with some ROW_ID object[Logically both records will be same])

About Siperian:- Siperian acquired DELLOF, and then Informatica acquired Siperian.

MDM Intro

Why MDM is in so much Glory?
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In last few years, big product Line Company realized, that ERP and Data ware house are not able to help much in
tackling the issue such as, data redundancy, Duplicity, inaccurate or inconsistent data. Data ware housing is simple
and straight solution. But it will take lot more, to manage business data, the data which is flowing into the
business, or different line of business. It will take lot more data rules, process rules, Trust rule to consume the data
in an efficient way. And once you do all this, you prepare something worth, and then those records need to flow in
the Business, those line of business, from where it actually got collected.
MDM is state of art design. It is way much bigger concept than data ware housing. You can say that, it is reverse of
data warehousing at least in one way [m sure... :)]. It’s a big topic to discuss that, what is difference between Data
ware housing and MDM… and I are not going to fight on this
We keep history in Data ware house, but in MDM hub. We discard history; keep only single copy of fact.

What is Master Data Management?
--------------------------------------------------
MDM is a tool that removes duplicates and creates an authoritative source of master data.
Master data are the products, accounts and parties for which the business transactions are completed.
The root cause problem stems from business unit and product line segmentation, in which the same customer will
be serviced by different product lines, with redundant data being entered about the customer (aka party in the
role of customer) and account in order to process the transaction.
The redundancy of party and account data is compounded in the front to back office life cycle, where the
authoritative single source for the party, account and product data is needed but is often once again redundantly
entered or augmented.
MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, and quality-
assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the
ongoing maintenance and application use of this information.

What is master data management exactly? It is a combination of processes and technology that help us to
manage such data in a better way.

Processes: - Data Stewards/governance groups, business rules.

Technologies: - master data repository, data integration tools and data Quality tools and many more other
application.

Predecessors of MDM: - I can say that before 2003. This kind of data approach Were handled by custom built
application [processes spreadsheet] and also Using data dictionaries kind of things.
But now we talk about MDM is now generally taken to be the Term that encompasses all of
these approaches regardless of the domain (Customer, product, sales etc). But it is also depends on the design
approach. It can be for the single domain like customer or can be built using all the
Domain.


Vendors: - Numerous vendors are competing for MDM solution, they claim they have
All the necessary functionality to support the MDM in their tool. But really, if we decide by our self, what are the
basic criteria, on which we can decide, which tool we should go for. in my opinion, if i have to select some tool for
MDM solution, I will look for below parameters.

 Governance support -- the creation, update and the retirement of master data definitions
Come down to business processes. There should be capabilities in the tool that will
Keep track of all the data flow, through various stages.

 Business rule deployment -- Business rules are something that drive the update
Of master data can themselves be stored in a repository and may include.
 Rules around Business Processes.
 Derived Data
 Business hierarchies.

 Data rules implementation -- Data rules are different than the business rules that could be following
 Validation rules like value should be integer or character.
 Dependency rules like if Oil well type is exploration, then fields can be 'DRY' or 'Success'
 Matching rules [to identify potential duplicate]
 Naming standards or Internationalization of names or attributes.
 Stats about data.

Above is the different way of applying the data rules. But the Primary goal for all above speech is
Data should be accurate, correct, current, complete and relevant. There is no. of tools that provide data quality
capabilities. But before that, we also need to know the discrepancies, at least we will be able to decide, that what
data rules, we need.

 Data Provision -- this aspect of functionality within an MDM application covers how master data is to be accessed
by the people. This includes.
 Reporting
 Search
 Browsing
 Security [Diff roles for different level access]
 publication for open access

 Data profiling capabilities: - this is also one of the major criteria, which we should look for in the tool. Data profiling
gives us the clear picture of what is the data stats, like how many NULL value, MAX, MIN, UNIQUE and many more
kind of stats. These Stats helps us in the next level to cleansing of the data.

 Master data Storage: - at the heart of many MDM projects is a repository or database in which golden copy master
data is held. There is need of understanding that why a data warehouse is not a true MDM repository, and vice
versa. A data warehouse, because its purpose is to produce reliable information, should have only 'Clean'
consolidated data stored within it. Yet an MDM repository should be able to keep track of such incomplete data
and track it through the stages of it becoming golden, ideally retaining an audit trail of the steps involved.

 Data movement and Synchronization. :- data movement and synchronization is about how the data’s coming into
the HUB and how we are maintaining the whole Publish and subscribe policy among all the connected application,
be it ERP downstream application or legacy application.