Dec 29, 2014

Perhaps 2014 was the year Big Data hype peaked. In 2015 we may finally move to the next phase of Big Data analytics.

Like many IT buzzwords, Big Data has risen from obscurity to become a "household" - or at least boardroom - name. As the Google Trends chart below shows, interest in Big Data as a search term started picking up in 2011-12, grew sharply in 2013, and reached new heights in 2014. Google Trends predict that search volumes for Big Data will continue to increase in early 2015, but the curve appears to be flattening.

Despite, or perhaps because of, the peak in interest in Big Data, many pundits and experts have already denounced it as a fad - an industry-generated hype that will die down soon. Critics point out that few companies or organizations have managed to extract value from Big Data projects yet. Also, some claim Big Data-related products and services tend to promise more than they deliver. Big Data projects are usually costly, time-consuming, and attention-grabbing for organizations, and the payback is often far off in the future, or difficult to identify or quantify.

Some Big Data proponents are raising the stakes by adding the Internet-of-things (IoT) to the mix. Getting continuous data streams from millions or billions of internet-connected devices will surely give data scientists and analysts more to work on - but it will also require development of new analytical models and methods, storage solutions, software tools, and more. Return on investment is likely to be even harder to achieve in such projects, at least initially.

The backlash against Big Data was bound to happen, and some of the concerns surrounding Big Data projects are understandable. Nevertheless, underneath the hype and punditry of the past couple of years, significant progress has been made to realize the vision of Big Data analytics. Vendors have sharpened their product offerings; data scientists have improved their models and methods; data sources are more accessible through open databases, APIs and web services; cloud computing has enabled flexible storage and processing of data.

Therefore, in 2015, it will be easier than ever to successfully carry out useful and profitable Big Data projects. However:

  • The human analyst must be central in extracting meaning and relevance from data
  • Projects should have clear, realistic goals and priorities aligned with business strategy
  • Analytics software need to become more accessible and intuitive for end-users
  • Big Data products and services must deliver on their promises

As the hype recedes, Big Data will become more and more real. By using mature technologies, improved products, and proven methods, businesses and organizations with Big Data projects will be able to get good returns on their investments.

2015 might well be the year Big Data graduates from its buzzword status to becoming a reliable business tool.