Big Data Mishaps That Companies Can Avoid

Higher business returns and greater customer relationships have driven numerous organizations, big and small, to employ big data programs. While high profile successes have quickened investments in big data, there are a large number of projects that have failed to bring the organizations their promised treasure chest – basically due to the mistake made in the selection, implementation and maintenance of big data.

Here are some basic drawbacks you can avoid to optimize analytical insights and the processes of decision making.

  • Stretching too wide: Big data is huge, and often strewn across several different repositories. With a single product, there are often compliance issues that organizations find themselves dealing with, which ultimately brings the realization that they have extended the technology farther than its capacity.What can be done instead is employing a portfolio of modular solutions which can build on the bases of each other, in a seamless form. For instance, dark data can be understood and classified with a file analysis tool; the data can then be moved into a records management system or archive.
  • Having a narrow focus: With big data, one has to be comprehensive. For example, an organization cannot solve its regulation problems and manage risks by just looking into one source of data – say, you look up the emails but not other communication sources like text or voice, which are as important – problems regarding regulatory penalties are bound to happen.
  • Lacking context savviness: How you treat a data is more of an ‘aboutness’, rather than just a specific context. For example, if a call center only looks at the time and duration of calls and not their nature, it is missing out on a crucial component. Though aboutness’ is exceedingly complex, it is extremely important to involve technology that can understand the context for governing unstructured data.
  • Failure to preserve information: There is often a risk of losing information that may be relevant to an investigation. This risk is considered more in industries where legal action and investigations are commonbig data. Other industries are sometimes unawareof the concept. Organizations in such industries, when need arises, end up taking the simplest solution, which might be to ask end users to not delete particular information or move the relevant information to a central repository. The manual tasks involved in such activities are not only expensive but also error-prone. Solutions that can automate and establish consistency are the best option to ensure appropriate management of risk.
  • Not making the best of a situation: While regulations and risks are something no one can avoid, the value that compliance-friendly data holds should not be undermined. After you have implemented the best information governance software solutions to manage such risks, the information becomes more palatable for undergoing analysis, which can deliver valuable insights. Look for new avenues to work with compliant data.

Although there is no one-size-fits-all approach regarding information governance and regulatory compliance, proactive planning and a modular and flexible technology are important ways to avoid pitfalls, mitigate risks, and derive value from data over a long time.

Have you come across experiences that taught you what not to do while handling big data projects? Share in the comments section.

James Mathews
 

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