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Data and Analytics are one of the hottest areas of focus for organizations. If you are not mining your data, consultants will tell you that your model is broken and in serious need of an overhaul. And by all accounts, they are right. We have recognized the value data can bring to an organization — increased revenue, decreased operational costs, and increased efficiencies — all by managing and mining a by-product of your company’s existence.
Banking, insurance, manufacturing, and service companies produce goods and services. In the past, that is all these organizations focused on. But now, the data that helps drive these companies has become valuable in of itself. Some companies are packaging this “by-product” and selling it to others. They are doing this with highly skilled Data Management and analytics professionals.
The question I have is: Why are so many companies putting both those functions into one pillar and one position like Vice President? Many believe the two subject areas are similar, if not the same. Invariably, there are job postings for a VP of Data and Analytics or a VP of Data Strategy and Analytics. There have been countless times executives would ask me specific questions about analytics, tools for analytics, and the best approach to analytic team-building. This was never a problem because I have managed both Data Management and analytics teams. However, it is my belief that these two areas are very different and provide the organization with very different skill sets. Both are equally important for executive decision-making.
Data or Data Management is about managing data at the enterprise level — to make sure data is available to everyone in the organization who needs it to make decisions. This could be the Executive Vice President of Operations who wants to look at their printed PDF daily at 9 AM or the sales executive who wants their weekly sales figures, by division, by 7 AM on Monday morning. This could be the finance executive who wants to close the books and publish the financial statements by the fourth business day of the month, or finally, those employees who need data for major analytical projects like creating customer profitability segments for their division.
Data Management professionals focus on areas of the data supply chain that analytics teams typically don’t. They make sure that there is good Data Quality through operational processes, with a well-thought-out Data Quality framework that is applied at the enterprise layer. They make sure there is a sound and effective data glossary so that all business teams can understand what the data means. They also make sure there are business people that own the data and data stewards that can be relied upon to provide insightful perspectives on the data.
You need good Data Quality and a good metadata catalog with business references. A solid data integration strategy must be in place because the goal is for you to create your data product once and have multiple people use that as an input into their needs.
on the other hand, are trying to solve a very real but very different problem.
In analytic exercises, the data scientist is trying to solve a business
problem. What is the next likely product our customer will buy from us? How do
we best segment our customers in order to sell more services or products to
them? They are not worried about potentially reusing their data or about enterprise
metadata or leveragability for the enterprise. They are focused on completing
the analytical exercise they were asked to complete in order to drive out value
— increasing sales, reducing costs, or increasing efficiencies of the sales
A good data scientist will explore the data that is at his/her fingertips and
then ask to see a business glossary and Data Quality dashboards if they exist. Why?
Because it helps them understand what the data is about and how reliable the
data is. They will try to make sure their code is reusable, but depending on
the time constraints of the request, they may not have that luxury. But for the
here and now, they want to solve a problem that has an important outcome for
So, you see, the data
team and the analytics team are both looking at the same data, but they are
looking at it through different lenses. So, should these different lenses be combined
under one leader? That is the question our data industry needs to answer.
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