The phenomena of Big Data continues to grow as companies of all sizes start to realize the potential ROI that comes from the correct use of massive amounts of data. MIT found that firms who can leverage Big Data can achieve 5-6 percent greater productivity and profitability than their competitors. Knowing the most common mistakes made when handling Big Data will help your organization achieve its goals.

1. Lacking a business cause: At the end of the day, every organization has bills to pay and salaries to fulfill. IT leaders are not immune to making the mistake of trying to apply Big Data analytics solutions without knowing how or if the proposed solution even helps organizations achieve their business goals. Focusing on business outcomes first will lead to CIOs identifying the proper Big Data steps to take resulting in a valuable ROI. Companies designing solutions with business goals in mind will allow for the potential of reduced spending on new investments.

2. Looking for “quick wins”: Turning to quick fixes may not be the best move for enterprises in the long-term. Vendor cases seem to provide an opportunity for a “quick win” but are not always tailored to focus on future goals for the organization in search of Big Data solutions. Vendor use cases are great in helping organizations realize the benefits in their Big Data implementations but nothing long-term.

Part of avoiding “quick wins” is remembering to not jump to conclusions. As CIOs look through reports and try to interpret data, it’s easy to see information that cause alarm. At times, the alarm is raised simply because the data presented has not been examined properly or have read a so-called best practice causing them to worry. Patience is key to finding the best Big Data platform available.

3. Ignoring data preparation: Lazy data collection can prove to be costly. Information Week notes that if your data collection skills are bad, the data your organization collects won’t benefit your organization or help you achieve your Big Data goals. If an enterprise is on top of its game and humble enough, it’ll be able to recognize its weaknesses. It’ll be able to recognize which aspects of data preparation and collection it struggles with and will make the necessary changes to succeed.

If an enterprise is on top its game, it’ll also be able to avoid problems that come with data negligence. Analytical errors can be easy to make as data makes its way through any organization. Recognize that not everyone on staff are data experts trained to gather and interpret data for the benefit of achieving business objectives.

4. Information gluttony: For some companies, meetings beget more and more meetings. In other companies, more reports beget even more reports. Too many visualization tools and a horde of information can lead to an obsession of vanity metrics like the number of registered users to the company websites.

Since Big Data is the handling of massive amounts of information, it’s easy to waste a great deal of time and resources trying to find insights that don’t solve major concerns like improving customer service and workplace efficiency. If there is more information being gathered than necessary, it makes it easy to overlook important information. Don’t make the easy mistake of allowing the avalanche of data to cloud your judgment and ability to make enterprise critical decisions.

5. Ignoring bias: Big Data is meant to resemble pure science to eliminate bias. While the analytics can be difficult, human nature can be a pitfall for some organizations. Being biased as to how information is processed and handled can lead to a close-minded culture ignoring other Big Data solutions which may be more efficient, cost-effective and in line with business objectives.

6. Forgetting about instinct and creativity: IT insiders expect leaders in each sector to start having Big Data capabilities and access to the same data in the future. It’s expected that the leaders who will find the most success will be those who are the most creative and innovative. While everything Big Data related may come across as hard science, don’t limit staff to only credentialed data scientists. While Big Data is in its infancy, be open to different options and working with competent people. Ignorance and bias cannot be allowed to hold organizations from moving forward.

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