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5 Essential Truths about Big Data Analytics


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With big data creating so much frenzy in the tech world, business leaders worldwide are scrambling to see what big data can do for them. While big data analytics can answer many legitimate business questions, it can also get overrun with too much hype—causing organizations to hit pitfalls and fail to get real value from the data. For those organizations looking to start their big data projects off on the right foot, here are five essential truths about big data analytics:

1. It’s a Priority for the Majority

Given that big data analytics is relatively new, it’s interesting to note that a majority of organizations are already planning on adopting it. According to a 2012 Cisco survey of IT professionals across a variety of industries, about 60% of IT managers feel that big data will increase their organization’s global competitiveness. 68% also identified big data as a strategic priority for their company over the next five years, with 60% expecting that their IT budget will also increase.

2. It Presents IT Challenges

Big data platforms likeHadoop were built to solve big data challenges. While Hadoop can handle large amounts of both structured and unstructured data, it also presents new challenges for IT.

  • Security is a huge concern for many departments considering a big data system, especially since Hadoop is still weak when it comes to preventing data loss. Some enterprise-grade Hadoop distributors have addressed this issue on their own.
  • Finding the right people who know how to work the tools to analyze the data can also be a challenge. In addition, it can be cost-prohibitive to install and manage a big data storage system in-house, leading many companies to turn to a cloud storage service. In fact, the Cisco report found that 81% of those surveyed felt that cloud computing would be necessary for at least part of their big data projects.

3. Analysis Should Be Based on Clear Goals

At the end of the day, big data isn’t about the amount of data or even storage capacity—it’s about gaining valuable business insights from the data. However, many businesses struggle to glean actionable business intelligence from their data, partly because they are plagued by unclear business objectives. Every big data project that is developed should have a clear goal in mind that will answer a critical business decision. It’s also important that these goals are realistic in nature; analyzing big data will not solve all of an organization’s problems.

 4. It’s More Human than You Think

Relying on big data to help make informed business decisions seems to take the human element out of business even more. However, big data analytics is actually more human than you would think. Many businesses fail to see an ROI with their big data analytics because they aren’t getting involved in the strategic planning process. Achieving value from big data doesn’t come from investing in the biggest or most powerful system, but from people coming up with a plan on how to use the data to their advantage.

5. Data Quality Has a Huge Impact on ROI

In a traditional database system, businesses have heavy control over the data that is stored. However, even this structured data has been found to have flaws due to human error or failing to account for data variations during analysis. Imagine the impact on data quality when multi-structured data from thousands of outside sources is thrown into the mix. Bad data or poor data quality has been found to cost businesses $600 billion annually. On the other hand, using quality data assurance can boost a company’s revenue by 66%.

As businesses adopt big data solutions, addressing security concerns along with ensuring ROI through clear goals and data assurance should be top priorities. With the right approach, organizations can turn to big data analytics to help them make informed business decisions.


About the author

Michele Nemschoff, who is vice president of corporate marketing at MapR Technologies, received an MBA from Stanford University and a BS in Economics from The Wharton School.


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