Five Best Practices for Building a Data Warehouse

Five Best Practices for Building a Data Warehouse

Five Best Practices for Building a Data Warehouse
Ever tried to cook in a kitchen of a vacation rental? The spatulas are over there, the knives are somewhere else and the cheese grater apparently doesn’t exist. No one person can figure out a holistic view of the situation. That was us at Verizon Digital Media Services, before our Master Data Management (MDM) system came along. We had a solution for everything—but the problem was that we had too many solutions.

We had Netsuite for customers’ billing and invoice and usage details. We had a proprietary system that managed customer configurations. We had systems for customer complaint cases; we had systems that only Sales knew how to use.

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But what we needed was a master system—one that would show every bit of information about every customer, all in one place. So we decided to build a Data Management Strategy, aggregating our best practices as we went along, helping us move toward our ultimate goal: functional, coherent data, all in one place.

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Before we started out, we gave ourselves an ambitious requirement: to deliver value to our stakeholders as soon as possible. This was tough because we didn’t have anyone working full-time on the product. But we told the stakeholders that we’d have something for them in three months: a system that could aggregate some of our Transactional Data by customer name and date.

This approach fit nicely with Edwin Locke’s revered Goal-Setting Theory: that specific and challenging goals lead to far better results than easy, vague goals. Instead of giving ourselves a Sisyphean task, or simply lobbing a softball at our stakeholders (“We’ll try to get you something generally useful within the next 18 months!”), we kept things small and precise.

Opt for Immediate Value

To provide our stakeholders with value and to buy ourselves time to further implement our data strategy down the line, we built a system that would be immediately useful for everyone: a Federated Data Warehouse that gave everyone access to descriptive analytics only.

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We had some existing APIs and borrowed a few excellent engineers to build others. With this, we were able to put together a straightforward interface that provided the enterprise with the ability to see “what happened,” so to speak. This approach enabled us to adhere to the agile best practice of delivering working software regularly. Any user of this warehouse could access simple aggregations and historical information simply by plugging in the appropriate customer name. The value here was immediate—and it benefited everyone.

If you’re familiar with the world of Data Management, you know that a standard “best practice” in MDM efforts is to not start with the customer. In fact, we’ve seen a lot of MDM efforts die off because of the complexity of the effort of starting with the customer.

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