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How is Social MDM different?

Intriguing post by Henrik Sørensen on Data Quality: “Every organization should very carefully assess if they are good at maintaining different aspects of their internal master data (Hint: Many aren’t).”

In a recent interview with yours truly on the Fliptop blog I had the chance to answer a question about how Social MDM is different from traditional MDM (Master Data Management). Check out the interview here.

As said in the interview I think that:

“The main difference between MDM as it has been practiced until now and Social MDM is that traditional MDM has been around handling internal master data and Social MDM will be more around exploiting external reference data and sharing those data.”

This is in line with a take away from the MDM Summit Europe 2013 as reported in the post Adding 180 Degrees to MDM.

But, as asked by a member of the Social MDM group on LinkedIn:

What is the industry or analysts’ consensus on the meaning of Social MDM? Is it just gathering Master Data from social sources? Not really MDM…

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Data Visualization At Its Best! – A View From The “Design Cockpit!”

How many of our daily business decisions can be attributed to the use of true quality information? Where are you getting your information from and how are you analyzing it? Ask yourself: “Am I willing  to make mission-critical decisions with data that is not entirely intact to begin with?”  It happens more than you would expect.  The Data Warehousing Institute (TDWI) estimates that data quality problems associated with customer contact data alone cost U.S. businesses more than $600 billion per year.

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John Tukey, the famous American mathematician once said: “The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data”  Hard to swallow for many, but truly a profound statement.  In order to achieve success in business, we must learn how to (1) wait until we have established a ‘quality database’ before making decisions from it, and (2) maximize the effectiveness of how we are visualizing and interpreting this information (uber important!).

Take for instance Procter & Gamble’s institutionalized approach to data visualization – The groundbreaking “Business Sphere” environment, and “Business Sufficiency Analytic Models.” – were both built to improve productivity and collaboration seamlessly so that P&G can focus on innovating for the consumer.  In his recent Harvard Business Review article, Tom Davenport sheds additional light into the intricate steps P&G is taking to ensure decision-makers are equipped with actionable data. “P&G has placed visual displays of key information on desktops – over 50,000 P&G employees now have access to a “Decision Cockpit“, which provides decision makers with dynamic and powerful decision making tools all in one place.  In addition to the desktop approach, P&G has created meeting spaces that it calls “Business Spheres” & implemented them in over 50 locations where management information is showcased for review.  This strategically enables the framework for data visualization to always be kept top of mind throughout the entire organization.

The most important attribute of successful data visualization is in the simplicity of the message portrayed – in whether the decision makers “understand quickly what’s going on in the business, and decide what to do about it.”  If organizational leaders are forced to spend too much time making sense of problem areas, the issues may never be resolved.

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