SAG_Twitter_MEME_Still_Growing_Dec16.jpgMaster Data Management (MDM) is becoming slowly but steadily viewed as Big Data’s data quality (DQ) backbone for the enterprise; giving it a new opportunity for recognition and growth.

But MDM still has a lot of explaining to do. Not only does it compete for attention within Software AG’s Digital Business Platform, but it also has to explain why it’s not the other MDM (Mobile Data Management).

Of course, we are still trying to figure out what it means to master IoT, or sensor data.  For example, the raw sensor data emitted from a customer’s smartphone can potentially be harnessed to provide additional information about customer behavior in terms of website activity, interaction with in-store systems (loyalty sign-ups, etc.), and even intra-departmental preferences within one store location.  But we’re really not ready to deploy an automated DQ process for sensor data, or one that that would merge, match and cleanse sensor data as if it were standard customer, product and location master data.

But, back to adoption; surveys, including one conducted by Andy Haylor of the Information Group, suggest it’s unclear whether MDM was adopted to help manage Big Data, or if companies  embraced Big Data because of preexisting MDM implementations that would ensure superior, Big Data quality.

Haylor’s survey of 209 companies  with Big Data initiatives found “live MDM implementations in 56% of the cases, with a further 14% imminent.”  More telling, 59% of those respondents considered MDM and Big Data “linked.”

Another interesting point:  “The survey also asked what connection, if any, was perceived between Big Data and existing initiatives around data quality and data governance.  The response was clear: a resounding 94% felt data governance was either “important” or “essential” to big data. It was almost as clear with data quality, with 80% saying data quality was of “key importance” to Big Data projects.”

In other words, not every company views MDM as being the first or last word on achieving enterprise data quality.

Considering the over 10-plus years of steady (if slow) industry maturation, MDM’s adoption tends to be linked with particular trends and innovations.

Jakki Geiger’s excellent article in Computer World (“Turn BigData into Big Value with MDM”), pointedly lists (and expands upon), innovation drivers in relation to MDM’s adoption history:

  • 2005: MDM for Decision Support/reporting. Fueling the Data Warehouse With Great Data for Reporting
  • 2007-2008: MDM for Operational Use. Fueling Business Applications With Great Data In Real Time
  • 2014: MDM for BigData Analytics. Connecting the Dots for Big Data Analytics
  • 2015: Hadoop/Data Lakes – and beyond. Following Data to New Insights

Clearly, in order to ensure adoption, MDM has evolved. As companies better understand MDM’s purpose and promise (particularly on the process-driven side of the tool), MDM will be required to show even more flexibility and a greater ability to adapt to business churn and other new technologies.





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