RESEARCH & RESOURCES

Ten Mistakes to Avoid in Big Data Analytics Projects

Ten Mistakes to Avoid in Big Data Analytics Projects

May 12, 2014

By Fern Halper, Ph.D.

Big data analytics requires the ability to collect, manage, and analyze potentially huge volumes of disparate data at the right speed, within the right time frame, while providing the right-time analysis and activity to the end consumer. Big data analytics has the potential to provide great value for companies by increasing productivity and performance. It is an exciting and challenging time for organizations as they consider big data opportunities.

Recently, TDWI launched its Big Data Maturity Model Guide and Assessment, which provides a benchmarking tool for organizations to assess their big data maturity. As part of the research for the model, we spoke to many organizations at various stages of their big data journey to understand best practices for big data and big data analytics. Although many enterprises are still in the early stages of their big data efforts, a number of interrelated themes emerged about what works and doesn’t work when it comes to big data analytics projects.


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