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6 Big Data Mistakes Businesses Make

April 9, 2015 by Updated August 8th, 2017


Big Data is reaping big benefits for businesses. But the key to success with big data lies in doing it right. All too often businesses jump on the big data bandwagon with unclear strategies and unreasonable expectations for what big data can do. As a result, the full potential and value of big data are never fully realized. If your organization is in the process of choosing a big data solution, here are 6 big data mistakes businesses often make that you’ll want to steer clear of.

1.Going too big too fast

With all of the buzz surrounding big data it’s easy for companies to get caught up in the hoopla. Instead of testing the waters, they jump in with both feet investing in expensive technology without first considering how all of this extra storage and compute power will best serve the needs of the business. After all, collecting and analyzing massive amounts of data has no real benefit without a business context. A better approach is to start small, making sure that IT professionals build a big data infrastructure that is designed and aligned to support business outcomes. Instead of spending massive amounts of capital on big data hardware, a cloud-based Hadoop platform is a very affordable way for companies to start small and scale up to better deliver what the business actually needs.

2. Becoming too dependent on data scientists

A natural extension of spending big and fast on technology is the mistake of relying too much on data scientists to solve a business problem. Yes, the technical side of big data does require programming skills and expertise in math and statistics—the kind of expertise that business execs often lack. But in order to succeed, a big data initiative must also depend heavily on those who have a deep understanding and working knowledge of what the business is all about. When IT departments and business execs work together, the right questions will get asked of the data and real business value will be found.

3. Lacking management’s full support

While going all out with a big data deployment is never a good first step for any business, starting even a small big data initiative without the full support of management is a recipe for early failure. In order to gain actionable insights from a big data project, all stakeholders in the company need to buy in. This is easier said than done. In fact, recent research conducted by the Fortune Knowledge Group found that a majority of business leaders feel that, when it comes to making business decisions, gut instincts and real-world insight should trump big data analytics. Without the full support of management, big data initiatives become weak peripheral projects instead of powerful core strategies.

4. Failing to break down data silos

In order for a big data initiative to function optimally, all data collected needs to flow smoothly across all channels of the organization. Unfortunately, many businesses make the mistake of compartmentalizing data, breaking it up and keeping it sequestered in data silos – the manufacturing data silo, the marketing data silo, etc. As a result, the business can’t gain the kinds of actionable insights that only come when all data is shared in a single unified hub where it can be freely accessed by all departments.

5. Underestimating data relevance

The big advantage of today’s big data analytics platforms is that they can handle all kinds of data, be it unstructured, semi-structured or structured. With data streaming in at massive volumes, velocities and varieties from multiple sources, a big mistake businesses can make is to underestimate the relevance of a particular data set to business needs. Without an understanding of which data sets are the most relevant to the analytics, and which are not, uninformed business strategies will drift aimlessly in a sea of worthless data.

6. Failure to act on insights

When all is said and done, the real purpose of big data analytics for businesses is to unearth hidden insights—the kinds of insights that inform better decisions and result in actions and changes that boost the bottom line and create competitive advantage. Failing to act on these insights because decision makers either don’t trust the data or are averse to making the changes that the data dictates is a mistake that will doom even the best big data initiative.

Fortunately, executives can take steps to avoid these mistakes and see great dividends from their investment in big data. By starting with a business objective in mind, executives will be better able to identify which data to collect and which systems to put in place or change to ensure the project yields actionable insights. For more tips on how to ensure your big data project is successful, see our big data tips from the experts series.

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