Master Data Management

 

Many organisations think of setting up a Master Data Management in terms of acquiring MDM software and spend considerable time on the entire process of selection and implementation. While implementation is important, the area that is left unattended to is the data itself. This causes failure of a bulk of MDM projects.

Andy Haler from ‘Information Difference’ puts it rather well and says “The first lesson is that MDM projects are inherently dependent on business leaders taking ownership of their data. As long as issues with data are seen as “an IT problem”, it will be hard to make progress. All competing customer master systems have an owner, and none of them want to give up control of it.”

A PWC global study on MDM in 2012 gave particular attention was given to data governance and the organization it subsequently entails, as this aspect will create the organizational framework for each of the other MDM areas. The survey supported the hypothesis that the use of IT only partially solved data management issues, if at all. Just 27% of the respondents considered the implementation of a state-of-the-art MDM solution to be a success factor.

Ixsight’s data quality solutions can form the bedrock of an MDM initiative. For the MDM solution to deliver business value – the following are essentials:

  • Good-quality data – defined as standard, consistent, validated, updated
  • Establishing a single view of Customer
  • Consistent definitions of Master Data across the organization
  • Ongoing “Management of Data Quality”
  • Embedding a culture of Data Quality

How can Ixsight's solutions help Master Data Management?

  • Data quality profiling

    Audit, score and provide metrics on various dimensions that indicate current quality of data supported by data-capture – systems-audit and walk-throughs.
  • Data scrubbing

    Cleanse the data by parsing, standardizing, correcting, populating and validating to ensure – high quality, accurate and uniform data.
  • Data deduplication

    Identify duplicates among individuals, households, legal entities and provide measures of match to reduce validation time and load back into system.
  • Survivorship

    Rule based systems to create a golden copy – to run in automated or manual mode.
  • Data quality practice

    Put in place systems to ensure ongoing best-practices and data-governance.