Forbes has just published an article on the Top Four Big Data Trends for 2014 and we here at eSpatial have given it some consideration. Below are the predictions in a nutshell and our thoughts on them.
1. Big Data Will Transition From Hype to Actionable Insights
“companies will re-focus strategies and processes and hit their stride due to greater maturity of technology tools and skills”
We love this. It’s basically saying that, after all the talk of Big Data, and what it’s going to do to transform business (and the countless dissatisfying articles such hot air produced), it now appears that there are tools out there that allow you to use Big Data. We know – we produce one of those tools!
2. Traditional Companies Will Derive Revenue From Data
“even retailers such as grocery stores have begun selling their data–and the trend is only in its infancy”
Yes, we agree. The massive companies and organizations that appear to have the potential to benefit most from using Big Data are not going to have it all to themselves. Data manipulation tools are widely available, often have a free version, and are quick to learn (we know one like that, ahem) – or at least they should be.
3. Visualization Tools Will Become Essential Enterprise IT Investments
“In 2014, visualization tools for big data will no longer be a nice-to-have in the enterprise; they will become a must-have IT expenditure”
Well, this makes complete sense to us. Presenting data in a dull format does not encourage interaction, retention or engagement. It’s a hard slog (and very time-consuming), looking for that vital piece of data in a spreadsheet, and it ignores one of the vital requirements of human communication – to be engaged and stimulated. (eSpatial promotes engagement by putting color-coded data on interactive maps.) But here’s the real benefit: the quicker you analyze something, the faster you can begin to take advantage of what it is telling you!
4. More Companies Will Implement Machine Learning and Predictive Analytics
“executives will forge ahead with greater awareness that algorithms have their limitations and need to be balanced with human rationale”
Frankly, we prefer human learning to machine learning, which is why we homed in on this section’s reference to the merits of “human rationale”. After all, if data is to have any value, then it is in the eye of the beholder – who knows his or her business better than anyone, and can therefore derive more from what that data presents. And that’s where the Predictive Analysis comes into the equation. If you can interpret the data quickly and perceive trends and patterns immediately, then you can start planning for the future, can’t you?