Big data is a big deal. According to Forbes, enterprises are willing to spend on big data app development to handle the sheer volume of information created by day-to-day business operations. Almost 40 percent of these apps are customer facing, with companies focused on capturing more data and leveraging unstructured data to produce actionable insight. Yet the road from adoption to action isn’t always a straight line: Here is a look at four common big data management mistakes and what companies can do to avoid them.
Too Big, Too Fast
Once companies see the potential power of big data, there is a common response: invest. While choosing a robust, customizable solution that allows you to mine new data sources and drives new insight is certainly an ideal candidate for IT spend, many businesses make the mistake of going all in up front and blowing budgets on big data solutions. The problem? It takes time and effort to shift company focus from traditional decision-making frameworks to those incorporating big data as a result, over spending on data technology means some systems are underused or completely ignored, until corporate culture is up to speed.
Avoiding this problem means starting small: Invest in technologies that fill specific business needs rather than trying to make big data the answer to every question.
On A Wing And A Prayer
As noted by Infinitdatum, many companies take on big data without a plan in mind. Lacking a plan, however, big data solutions face the same problem as their cloud counterparts: sprawl. With sprawl comes a lack of focus, in turn reducing ROI. Suddenly, the big data solution meant to catapult a business is holding it back.
The alternative? Start by recognizing the inherent challenge in big data. Insights and answers aren’t simply produced out of thin air when the right solution is used companies need to build a big data framework from the ground up that addresses how specific business units will use tools and insights to inform decision making. The plan also needs to cover specific goals: What do you want to achieve with big data technologies in a year? Five years? Ten? By mapping specific goals and defining metrics that measure overall success, it’s possible to limit the chance of a data investment going off the rails.
What Was The Question, Again?
The big lure of big data? That companies can ask any question and get unique answers that provide never-before-seen insight.
The mistake many businesses make? Asking the wrong questions. A recent Huffington Post piece points out that the real purpose of big data solutions is to answer questions of why not what. Companies already know how to find what, and using big data to give the same answers is a waste of time. Instead, organizations need to focus on incorporating new data sets and deriving new relationships to answer questions of why why do employees, customers or business processes take certain actions or yield particular results?
Forest For The Trees
As noted by Tech Target, many companies assume a more is better approach to collecting data and grab every bit of information they can find. Then, they use the answers generated by these new and fast-changing data sets to drive all business decisions. Here is the problem: Not all data is relevant, and the expertise of human experts such as IT admins, C-suite executives and the emerging class of data analysts should also be a factor in line-of-business (LOB) decision making. Sidestepping the problem means recognizing big data for what it is: Part of a larger whole. Alone, it’s a finite resource that can misinform or even mislead companies. In conjunction with existing knowledge and expertise, meanwhile, big data is a formidable resource.
Want to avoid the top four big data mistakes? Start small, plan well, ask the right questions and diversify your data decision making.