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The first wave of Master Data Management (MDM) started in the early 2000s. Early adopters from a few industries which included Banking, Insurance, CPG, and Retail started implementing MDM for customers, products, and a few other master data domains. Over time, MDM became mainstream as companies from all industries realized the need for MDM as part of their data strategy to resolve data inconsistencies across multiple systems and establish a single version of the truth. Over the years, there have been many successes but plenty of failures as well, due to the lack of skills and a proper understanding and positioning of MDM solution versus other systems.

Our clients frequently complain that their understanding of their business is hampered by the fact that much of their vital data resides as ‘dark data’ in silos that don’t reside on traditional IT infrastructure (system data).

Communication is a key skill in any field. Without communication, we would be hard-pressed to drive change in an organization. We need to communicate ideas well to ensure that the people that help us do what we do, can help us do what we do. One of the key functions of any data scientist is to encourage data-driven change to effect some positive collective outcomes (increasing revenues, reducing costs, improving resiliency). If you can’t communicate the work you’ve done in a concise, meaningful, contextualized way,…you may fail in making your work actionable. The right people responsible for driving change won’t get to onboard your train, and your data science will go nowhere. No person is an island. We need the help of thousands of people to do what we do effectively, and to do that, communication is an absolute key.

“I cannot live without brain-work. What else is there to live for?” – Sherlock Holmes

I saw this challenge (minus the solution) being passed around in a WhatsApp message, and after solving it, it struck me as a fantastic way to illustrate the difference between data, information, knowledge, and insights, under the pretext of not getting shot.

Let’s get started, shall we?

Many people think that Data Science looks like this:

In many meetings, I’ve been asked by customers, “How do you demonstrate an ROI for an MDM solution to manage customer data.”   They all get the merits of what MDM can do - a single source of truth for customer data that is the foundation of many customer-centric business initiatives.

But how do you create a business case for this?

To start, we need some basic metric assumptions that will be useful for our calculation: