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Master Data management:The Three Pillars

by Ike Ononogbu

November 8, 2010

In the recent past the topic has been broached in all data-related events: SAP TechEd 2010 in Berlin and Las Vegas, Informatica World 2010 in Washington DC, Data Management 2010 in London. And this trend is likely to continue. Organisers just can’t get enough of it, and rightly so.

Picture this typical scenario.
An insight into the business has exposed a worrying picture. Core business entities - Master Data - reside in many applications resulting in multiple representations of events. On the backdrop of this, an IT-driven business strategy has been drawn-up. Now, how will this strategy be implemented?

Step in Master Data Management (MDM). The primary reason businesses look towards MDM solutions in their bid to solve this issue is because it offers one “single version of the truth” for Master Data which includes: Customer, Vendor, Product and employee. Furthermore, centralising core business entities ensures data can be viewed and used company-wide and more importantly decisions are made based on the same data set. In effect, MDM allows for accurate reporting, operational efficiency and effective decision making.

To have a reliable MDM capability, three vital processes ought to be implemented: Data Profiling, Data Integration and Data Quality. These steps are aptly referred to as 'The Pillars of Master Data Management'.

Data Profiling
This process involves statistically examining data available in ex isting data sources. By profiling your source data your business can have a better understanding of data patterns and formats. This understanding will pave the way for smooth data integration.

Data Integration
In a lot of companies data is stored in different formats and places. Maintaining similar data in different locations gives rise to different business units interpreting data, though similar in requirement, differently. To achieve your goal you have to amalgamate data coming from these disparate sources into a single repository.

Data Quality
For data in MDM to serve its purpose, it has to be consistent, accurate and valid. To achieve this, data has to be cleansed and validated. In effect, Data Quality ensures data stored reflects the true nature of the business.

It is worth pointing out that all phases are equally important, though the amount of work, time and resources invested may vary from phase to phase, and no one process should be underestimated.

In the final analysis, for MDM to be successful, like any well executed business strategy, IT has to be an integral part of business. The seamless fusion of IT and Business will maximise the value, in business terms, of the company.

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