Achieving Proactive MDM – Assessing the Scene

A common maxim in the business world goes: If a problem is found in the design phase, it may cost $1 to fix, but if it is found once the product is released and in the hands of customers, it may cost $100 – or much, much more – to fix.

Over the course of 18 to 24 months, the quality of important data about an individual significantly degrades or becomes invalid – people move, change jobs, change email addresses and so on. Certain types of data, like email addresses, can have an incredibly short “half-life.” Organizations invest countless resources, technologies, strategies and hours trying to clean and fix exabytes of bad data that have infected the business environment, but most of these efforts focus on alleviating the symptoms rather than fixing the problem at or close to the source. Remediating data issues downstream is of much greater cost to the business than fixing the problem upstream. Proactive Master Data Management (MDM) efficiently and effectively addresses the root cause of the bad data problem, reducing costs and risks of downstream issues.

Proactive MDM establishes a continuous process of data quality evaluation with the end goal of ensuring the incoming stream of new data is always up to date.  As in health care, prevention and early detection are critical to long-term health. If you avoid the problem, you avoid the thrash, domino-effect impact and expense. If you fix the problem early its impact will be far less pervasive in your organization.

A proven place to start

Step number one for getting to a state of proactive MDM is assessing where the business is today, identifying the business processes and systems that are prone to data degradation. If your business depends on valid customer contact information, this phenomenon is a major issue. Constantly degrading customer data increases your marketing efforts and costs, and impedes cross-sell and upsell opportunities, which can negatively affect your bottom line. When it comes to data in your organization this scenario could take the form of a major customer outreach campaign that contains a high percentage of inaccurate emails. Identifying emails that need to be updated previous to kicking off the campaign will save time and money, vs. fixing the mistake post-campaign.

Consider the following to determine the quality of your current data, and the scope and depth of the problem in your business.

    1. Map a data set you have and profile against it. Understanding how old the data is will give you a good idea of how valid it is. Be sure to select a data domain that is of high importance to your organization.
    2. Once you are ready to start cleaning that bad data and harmonizing it across your systems, you have two options:
        • Fix it at its source
        • Fix it after it is replicated in other downstream systems.

      The earlier and closer to the source you address it, the more efficient your effort and the easier it is to prevent bad data from infecting the business environment.

    3. Repeat the process with one or two more data sets and you will quickly extrapolate the pervasiveness of bad data in your organization

The ultimate goal of a proactive approach to MDM is to create a firewall of internal processes and checks that ensures bad data remains at bay. Matching, cleansing and harmonizing data at the source of entry is critical, as this allows the business processes that keep costs down and enable better business results to flourish.proactice_mdm_image-1

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