With self-service data prep, organizations are able to reinvent the way they use and analyze Big Data to drive business value.
The tech industry has long talked about how Big Data can help us solve complicated problems that require close analysis of vast amounts of data. Big Data, we were told, would help us make better, more informed decisions. No more guesswork. No more hunches. Business leaders could make choices based on thousands of data points, analyzed in numerous ways, giving them a full picture including all factors and variables involved.
However, after years of the Big Data era, we seem to find ourselves only part of the way to that idealistic picture.
Why Are We Still Panning for Gold?
As I visit with companies around the world, it feels like we are continually “panning for gold” rather than mining for insights. We have created impressive new capabilities to store and process data, and therefore correlate it, but I often get the feeling that we are not any closer to surfacing new insights. Correlation does not necessarily imply that we are getting better at understanding causation.
While new mass storage and processing technologies have made us more comfortable with storing large amounts of data, we now often experience analysis paralysis. We feel compelled to scrutinize every single bit of data available to us in efforts to uncover insights, which leads to a very long and convoluted process before discovering insights – if we even do.
Fast Data: The Need for Speed
The organizational need to analyze Big Data quickly in business is an often-discussed theme. An executive wants to make quick decisions about sales or accounting or hiring, for example. They cannot wait six months for an analytic report; they want that report within weeks, days even.
The need for speed is indeed imperative for businesses, but consider that need for other types of organizations. Take the presidential candidates for 2016 and how they deliver ideas around the economy, education, spending and immigration to the curious masses. Often times these policy ideas are short on detail, particularly on supporting data. Think about how much deeper and more meaningful these ideas could be if policy advisers were able to provide users with real-time data snapshots. Imagine a candidate using new, up-to-the-minute information at each campaign stop. They could give voters precise updates of economic change or spending, and contrast that with how his or her plan would improve upon those numbers. That could be possible through self-service data preparation.
Or consider how faster data analysis could change the recommendation engine on Netflix. It currently provides recommendations based on single movies or television shows you’ve watched. You watched Skyfall, so you may also like X movie. You watched The West Wing, so you may like Y television show. Imagine if the recommendations were not based on your viewings of a single program, but based on all your viewings in composite – and they changed in real-time. You could get recommendations based on your viewing of both Skyfall and The West Wing. Then you watch The Big Lebowski, and immediately after, your recommendations are updated based on Skyfall, The West Wing and The Big Lebowski as a group.
Using Self-Service Data Prep for Fast Data
Industry analyst firm Gartner recently predicted that by 2017, “most business users and analysts in organizations will have access to self-service data prep tools to prepare data for analysis.” These self-service data preparation tools are the key to moving from “panning for gold” to more productive strategies for uncovering insights because they provide context to the data. Contextual knowledge typically resides not with the IT staff or data scientist who created an analytic app, but with the business user or analyst who was charged with determining an organizational result.
With self-service data preparation, organizations are able to reinvent the way they prepare data, assemble data, author analytics and operationalize them. Companies can now bring the business user more directly in touch with the data, allowing them to gather, design, test, debug and operationalize the analysis for themselves, removing the bottleneck of having to wait for the data, or not being able to consume the data due to lack of programming skills. In turn, this allows them to accelerate the analytic supply chain from identifying a business problem to achieving a business result, in hours and not days or weeks.
This kind of adoption shows that self-service data prep has the potential to completely disrupt the analytic supply chain, rapidly speeding time to insight and empowering business users to see new opportunities to problem solving by way of their data. To avoid being outmaneuvered by the pace of data creation, we must continue to improve the whole analytic process, and make accelerating the time to actionable insights a priority.
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