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IoE/IoT & MDM - An Inflection Point or a Good Case for Convergence?

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Long live Acronyms! Am sure you agree it is fun to throw around a couple of them in a conversation and see the 'awe' around. Well, you could give yourself a bit of a back-pat for the time spent on the internet. It is not so bad after all. When the first show of the Nolan conceptualized 'Inception' was seen, shock, awe and gasps took over from theatricals. Whoever thought of a dream within a dream within a dream and how complex the human brain was. Or in Interstellar where the fifth dimension of time was made a physical entity that could be controlled. While these are futuristic in nature, we also did think that the Back to the Future cars & Minority Reports type of actions weren't possible years back. We are now in an era of changing face of technology coupled with strong innovation and that gap is fast closing and movies and reality might be much closer than we think.

Hence, IoT - Internet of Things gave way to IoE - Internet of Everything. (Acronyms rule us!). Hence, this makes the other acronym MDM wanting to give way to something else. It is time that change happened and for the good. MDM solutions that use graphDBs and use noSQL DBs with cloud storage and have a lighter code with a single platform for understanding the interconnects of the various domains and making sense of the relationships amongst them, will be highly sought after. Scalability, performance and optimized utilization of business time will be of paramount importance. So when smart meters, ingested capsules with sensors, temperature/weather/motion/ocean sensors and connected devices start producing and giving valuable data, it is time for a convergence in the technology space. It is truly an inflection point that reflects the current need of society and humans to have a great personal experience. That can only be driven by data and accurate data at that!

Hence, when an IoT architecture is in place and let's say Amazon's Alexa is connected to multiple families. In this use case, there is going to be a lot of personal information that Amazon can mine and provide a better experience. It can't do that without accurate customer/product/location/asset data. Hence, traditional MDM needs to use new techniques for matching streaming data. This has to be based on NLP and ML to make sense of the vast amount of data being fed in. A self-learning algorithm that will predict and adjust the weights of critical data elements & move thresholds for business inputs will be critical. This is where true interlock needs to happen. Product vendors today are yet to latch on to this convergence called MLDM. There can be a use case where a retailer today looking at SKUs and managing items using codes will suddenly see a burgeoning number of products to handle. This will require a traditional PIM solution to have a different architecture. Current solutions might not be able to handle large number of items and equally a large amount of customer streaming data.

It is important in this convergence era to also have a strong set of data standards to be adhere to and a strong control framework. While that is another discussion, the underlying data current needs to change to support this massive 'data revolution'. What has been your experience? Any thoughts on how this idea can be further taken up - do share and we could have a fruitful discussion.

About Author

Venkataraman Ramanathan- Senior Architect, Analytics Practice, Wipro, Ltd.

Venkataraman 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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