Master Data in the age of Internet of Things (IoT)

“Space: the final frontier...” – said Captain Kirk while on his voyage around the universe in the Starship enterprise. Well, can we say that about data in an enterprise? Let’s take the small tour.

Enterprises today are looking at seamless integration and customers’ on the other hand are looking at seamless connectivity of their devices, with other devices, sensors, and meters. With such developments taking shape, Internet of Things (IoT) gains more prominence and becomes central to the data that is fed into the business. Fast data – data that is consistent in arrival at an increasing speed, and data that is varied and changes frequently is fed across the enterprise. “I want all my sales personnel connected and feed customer data real time, irrespective of the mode of data entry!” – Does this resonate? Well, every such seamless data integration exercises needs to have a foundation and that needs to be driven with a strategic vision using tactical solutions to address the current gaps.

IoT is enabling enterprises to think about SMAC (Social, Mobility, Analytics, and Cloud) and beyond. Let us cut to the chase now on how Master Data Management (MDM) fits in.

“Garbage in is Garbage out” – this adage couldn’t be more true in this era of IoT and SMAC. This is what MDM has been doing all along; that is, sort out the garbage; and indeed it will continue to do so. Hence, the drive to strategically think and also have a handle of how the MDM program will roll out with in an enterprise becomes critical. When you also think of the levers of Data Governance, Data Quality, Core MDM services, and architectural designs, then the phased roll out of MDM becomes important. As enterprises move to create a global workforce that is efficient in driving revenues, providing customers critical information as well as developing a collaborative ecosystem for optimizing existing business/ revenues – this phased implementation of the levers forms the foundation on which the Fast Data from IoT will help drive each of these business outcomes.

While the importance of master data and its strategic nature is becoming pervasive, experience still shows the use of manual and traditional methods of management of this critical data piece in the enterprise. In the current era of SMAC and IoT, the most important factors are how Fast Data is being supplemented with more data to make it contextual and drive the business benefits we spoke of earlier.

One example could be that of sensor data from GPS and location tracking devices. The data from the GPS and location can be used to suggest better travel tips to a customer. But rather than suggesting a place he/ she has already visited, if the master data profile at the back end can tie all the transactions of the customer then the front end analytics can accurately provide analysis. The Marketing team can then use this data for targeted campaigns. It can be more complex when you add machine data to bridge the contextual gaps. MDM helps in governing this data – location related via the sensors – for efficient management of delivery, route optimizations and others.

The application of MDM has become even more relevant now with the advent of the SMAC and IoT era. Governing the master data becomes all the more critical and maintaining the quality of master data is even more essential. It is also going to be the principal lever to drive more efficient analytics and usage of critical and sensitive enterprise data - like medical device IDs, information, logistics, location data, customer preference data, smart grid efficiencies, to manage and predict customer behavior.

MDM, IoT & SMAC – are these the three musketeers you should be looking for? What has been your organization’s experience on these and do you have a MDM strategy? Let’s discuss.

About the Author

Venkataraman Ramanathan has more than 15 years of experience in the areas of MDM, DQ, Data Governance, CRM, BI and Java. He has been involved in various consulting and engagements across geographies for a variety of Fortune 500 companies. He is involved in the areas of customer re-engineering, machine learning, data quality, data governance, sales and service efficiency, business strategy and business analytics/intelligence.

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