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?

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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.

è 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.

è 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.

è 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.

è 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

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è 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.

è 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.

è 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.