Big data analytics continues to be a prime area of enterprise investment with 37 percent of IT leaders saying it will eat up the largest portion of their technology budget this year. But as Bob Violino put it in a recent CIO article, “There are no guarantees that analytics investments will pay off. In fact, the discipline can be fraught with problems that can temporarily derail these projects or doom them to failure.”

In order to avoid these negative outcomes, consider the following recommendations:

  • Implement a solid data management strategy. Just as cybersecurity is recognized as a business-critical component of IT, data management should be prioritized given the critical role of information in business operations today. A recent CompTIA survey found that only 44 percent of organizations have employees dedicated solely to data management or data analysis. If companies want to make the best of their data analytics investments, it’s important that more effort is directed to filling these positions and ensuring the comprehensive collection, processing, and analysis of data.
  • Make data integration a priority. Violino writes, “For several years, CompTIA research has found that business units working independently on technology initiatives eventually leads to a challenge with integration. As a result, organizations are trying to avoid shadow IT in favor of collaborative approaches that still give business units some freedom while maintaining an inclusive view of all business systems.” This is a critical step in overcoming the data silos that still plague most organizations, and it’s also important that companies establish data-sharing processes between business units and IT.
  • Practice effective DataOps. As the CIO piece puts it, the practice is “an automated, process-oriented methodology that can be used by data analytics teams to improve the quality and reduce the cycle time of analytics.” Like DevOps, DataOps draws on agile techniques to shorten development time and aims to improve data analytics through continuous delivery. Violino spoke with numerous IT leaders who stressed the importance of implementing DataOps, particularly as it helps automate processes that ultimately reduce errors and avoid data integrity issues.
  • Ask the right analytics questions. Even the most advanced big data analytics tools will not deliver value if they’re not tied to impactful, business-critical questions. It’s important that IT leaders are always mindful of this and ensure that the insights extracted from analytics investments are focused on strategic outcomes.
  • Analyze only clean, accurate data. It’s a basic step but arguably one of the most critical. As Violino puts it, “If the data being analyzed is not accurate, the results and insights will be tainted.” As part of this, companies must ensure they have a solid underlying architecture that can address data integration and cleansing and pave the way for the analysis of accurate information.
  • Create a cohesive, collaborative analytics team. Eliminating department silos can be a critical step in obtaining more insight from big data analytics investments. When teams are structured in multi-functional ways and include representatives from various data disciplines, companies have a much greater chance for better insights and, ultimately, success.

For more on what you can do to strengthen your big data analytics initiatives, check out this previous APEX of Innovation post.