Role of Analytics in Master Data Management

As published at Intellitide — Despite the many benefits of a Master Data Management (MDM) system, one historically overlooked advantage is the use of analytics to extract insights from this vast trove of master data from the MDM systems or analytics performed by the MDM systems themselves. The traditional use of MDM systems leans heavily for operational purposes including –

  • Provide a single source of truth repository for master data
  • Improve the quality of an enterprise’s master data
  • Accelerate the time of product introduction from conception to sell (product domain) or quick on-boarding of vendors or customers (vendor/customer domain)

Note that the topic of this blog is not traditional reporting, visualization or dash-boarding which every MDM platform supports usually out-of-box. However, more complex analytics that involve prediction, diagnosis, optimization and machine learning techniques for classification, regression and clustering are rarely employed and that is the focus of this blog.

Importance of Master Data for Analytics

So why is holistic master data important to analytics? Because comprehensive 360-degree view of enterprise data and critical insights to improve productivity, efficiency and positive financial outcomes cannot be achieved without the deep involvement (and understanding) of master data. Thus, analytics initiatives in an enterprise should consider a deeper involvement of master data and the MDM system supporting it. Even, systems that merely consume master data but have analytics interests should consult the MDM implementers and acquire an understanding of master data.

Analytics Use Cases Using Master Data

Here is a sampling of the analytics use cases that involve master data:

  • Order-To-Cash: comprehensive product and sales analytics to determine sales vs profitability using master data domains including product, customer and vendor
  • Population Health Management: detect underperforming revenue parameters and predictively determine claim denial probability, readmissions, aging of payments, in-patient infections etc. by mining and analyzing master data elements from EHR, clinical and claims/billing data
  • Data cleansing using ML: support Match/Merge and De-Duplication scenarios
  • Data Enrichment using ML: Automatic SKU/Product classification and discovery of schemas and data types
  • Image recognition using AI: Digital Asset Management (DAM) is a feature/add-on of many MDM products. Images in DAM can be fed to image recognition tools to automatically identify the product make, model and other attributes

The Front Row Seat!

MDM solution vendors and MDM system integrators have a front row seat to the data and are in a perfect position to analyze the data once the data management solution is implemented. MDM implementers have intricate knowledge of their customers master data since they know about all the following:

  • Data sources,
  • The data elements
  • Entities
  • Attributes
  • Categories
  • Hierarchies and taxonomies
  • Relationships
  • Business processes and workflows
  • Governance and much more

With this extensive knowledge of data, it is fair to state that MDM implementers over the course of the implementation gain a much better understanding of the data compared to the customer’s business users and data architects. So, with this intricate knowledge of the data one would think MDM solutions would have been more involved in analytics. Unfortunately, that has rarely been the case.

Why Analytics is Underplayed in Master Data Management

Water water everywhere but not a drop to drink! So much analytics could have been leveraged with so much data but so little is done. Here are some of the reasons we think that MDM solutions have had a minimal role in an enterprise’s analytics initiatives.

Historically focus was on operational aspects of MDM

We have already discussed this in the introduction of this blog.

Master data is operated in a sandbox

Once an MDM solution is implemented, master data tends to get siloed. Any reporting or business intelligence on the data is performed within this siloed data set and that too to achieve only operational reporting goals like tracking an item in a workflow or plotting the flow of items syndicated to downstream systems over time.

Analytics relegated to consuming systems (and market intelligence vendors)

Even in enterprises which deploy MDM systems, any meaningful analytics is usually performed by consuming systems that are recipients of the master data from MDM. Often this is for the benefit of the marketing or business intelligence teams and is performed without consulting the MDM implementation team or understanding of master data. Whatever the motivation, analytics performed with data from consuming systems is on subsets of data received by that system to address its specific needs and tend to focus on transactional data interpolated with narrow slices of master data.

But… Times Are Changing!

The barren lands of analytics in the MDM world will look a lot more fertile in the future. MDM vendors and customer are beginning to realize the potential of analytics on master data and analytical solutions either as add-on features or tightly integrated with MDM platforms. Why this sudden change of heart? We can think of at least four reasons:

  • MDM players are finally beginning to unleash the power of the data goldmine. MDM vendors are also looking to expand their portfolio and tout the analytical features of their platforms as a competitive advantage
  • Customers are now more forthcoming in demanding analysis of master data – in real time. This is especially true for customers whose MDM implementations have attained operational maturity and are considering analytics as the next opportunity to squeeze ROI
  • The value of analytics from data onboarding into an MDM system all the way to data syndication is now being recognized. This analytics within the MDM box will augment the analytics being performed at the destination by consuming systems
  • Though we need to see more of this, the collaboration between the BI/data science and business teams with the MDM implementation group is increasing leading to more comprehensive insights from a company’s master data

Analytics and AI is creeping into every facet of life and MDM is no exception!

Ramesh Prabhala

Ramesh Prabhala

Ramesh Prabhala is the founder of IntelliTide – an analytics Platforms and Services company which uses the power of Data Science, Data management, AI and Cloud to improve efficiency, productivity and financial outcomes. IntelliTide knows every data has treasures and it’s mission is to find those hidden gems for its customers. Prior to founding IntelliTide, he worked in various technical management, engineering and consulting roles for companies like Microsoft and HP.

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