Re-imagining Master Data Management (MDM) in the age of 'Disruption'

On a recent weekend conversation with my 10 year old, he asked why all the phones that we have now are called SMART? What did they look like, what did they exactly do, were they less intelligent than now? As I answered, I realized, the distance and the speed we have traversed in terms of technologies getting smarter.

In a similar vein Master Data Management (MDM) as a discipline has also been evolving over the years - from providing a single cohesive & authoritative view of critical business entities to now powering many of the critical enterprise applications in real time with trusted data & insights. As we get ready to welcome 2017 in less than a month, below are some perspectives which will see increased traction as enterprises continue to embrace new technologies and deliver on the 'digital' promise.

Breaking the Silos: By definition, MDM is supposed to break the data silos by harmonizing disparate applications. However, large enterprises today have also siloed MDM implementations to address the needs around legacy enterprise applications, business units, geographies, use cases (Operational Vs Analytical) and data domains with enterprises still struggling to get that 'elusive' single trusted enterprise level view of critical business entities. This will call for critical considerations being made for efforts towards a single consolidated MDM applications by breaking the MDM silos

Tap to Build Apps: Current generation of MDM implementations have served very well to power the analytics and insight platforms albeit externally, but as the focus shift from 'system of record' to system of engagement, on-boarding of new data sources increases, insights are required in real time, the MDM system would be required to deliver a complete 'trusted view' - not just based on master data but associated interaction and transaction data packaged together leading to the increased adoption of MDM Apps - Such as 'X' 360 Apps (or shall we call it 720?),where 'x' may be a Customer, Asset, Vendor; so that a single apps have the ability to deliver both analytical and operational use cases combined with insights such as customer analytics, customer on-boarding, sales and customer support

MDM in Data Lake: No data management discussion is complete today unless good amount of time is spent on Big Data, Hadoop, MapR, NoSQL, Machine Learning and all the cool stuff. There are few large enterprises today who are contemplating to build MDM system as a part of their enterprise data lake initiative especially for customer facing programs such as customer experience & customer insight program with the promise of delivering all kinds of data in one place. Though this is not a secular trend yet, there will be a requirement for solutions which can support analytical MDM use cases leveraging big data ecosystem.

Cloud Ready: Adoption of cloud based software technologies by enterprises continue to increase and last couple of years have seen many MDM vendors making their software cloud ready. This has already benefited few enterprises. However, as vendors mature their cloud offerings lot of emphasis would require to be given on areas such as multi-tenancy, data security & data governance, transition from on premise world to cloud world.

Data Stewardship Process Automation (DPA): Data Stewards are an important stakeholder in managing and coordinating any MDM program. As Artificial Intelligence (AI) and Machine learning (ML) technologies are making inroads into critical business processes, day to day data stewardship activities are likely to get automated through the usage of AI & ML in coming years freeing up their time to take up core business process management and insight related tasks

May be the time has arrived to re-christen MDM to SmartMDM as they have really come a long way like our own smart phone that we cannot live without.

About the Author

Amit Jha has over 19 years of Business and IT experience in the areas of Data Management and Analytics solutions for global organizations. In his current role, Amit is the Global Practice Head for the Data Management practice in Wipro’s Analytics Business Unit. He is Responsible for practice vision and strategy, GTM (Go-To-Market) definition, Customer Advisory, Competency development, Thought leadership and nurturing of emerging trends and partner ecosystem management in the areas of MDM/next-Gen Data Management, Data Governance, and Data Quality & Application Data Management. Amit is also the co-founder of Wipro MDM Leadership Council, a unique thought leadership initiative by Wipro in the MDM space.

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