Big Data is a complex science growing ever more complex. And that’s true. But that doesn’t mean it can’t be set up to deliver more accurate results with much less work. To compete at any level, businesses need good data.
The new digital world is a constant, incessant fire hose of data spraying in every direction at once. There is simply no possible way to capture all that data, much less catalog and monetize that data without the computing power of modern big data science. But not every business can build and manage its own internal big data apparatus.
That’s where the benefits of outsourcing the “grunt work” become clear. Businesses can speak to the specifications and enjoy the benefits without doing all the tough sledding. But what should that data engine look like, and how can a business know it’s getting its money’s worth?
You need to have a solid understanding of the basic components. A first-class workable big data system should consist of a high-performance, analytical data store. You need a system that can quickly and consistently find the most common data sets and structure them for properly for most efficient cataloging and connectivity. It’s important that this system not only supports known data but also is able to recognize and categorize new data in an exploratory program that can predict and manage any unplanned scenarios.
Your big data program should also contain a “semantic” apparatus that converts raw data sets into business lingo in order to better communicate the values to the end user – business leaders. In addition to being convenient and time-saving, this aspect allows business leaders investing in big data to “see” the direct benefit without having to understand everything that’s happening. It’s like offering them a bottom line memo report with key numbers rather than a time-sucking comprehensive report of everything they may or may not care about. Business leaders like bottom lines. A semantic layer gets you closer to delivering one without losing any of the functionality big data offers.
Some examples of semantic settings include defining and identifying long-term customers, high-value consumers, and the most loyal customers. Each of these settings can be programmed in, and their behavior parsed and studied. If the semantics is set up properly, the business leader will never see all the work the system does or the files it accesses to deliver the data, but they don’t want to see any of that anyway. They just want the numbers and the prognosis. And that’s a simple way for big data to make business easier, even if you outsource the key system architecture and management.
David Milberg is an investment banker who hails from NYC.
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