Maybe because it's summer, writers, editors, researchers, and educators are feeling lazy, and it's easier to latch onto a buzzword than to specify something such as predictive analytics or visualization. Lump anything involving data under big data, and you think you have it covered.
Big data is helping Olympic athletes win in Rio. Big data is helping insurance companies make money. Big data is curing rare diseases. No, in most cases it's analytics, and in many cases -- maybe more than ever -- the data can be fairly small.
Blogger Charlotte Erdmann had a nice article yesterday, Big Data: An Art Form in Itself?, about using lots of data from all over the world to build out visualizations that aren't just pretty but tell a story. For example, one artist drew on many data sources to find relationships among victims killed on September 11. That's big data.
Tracking an athlete's performance under certain weather conditions or against certain opponents isn't big data. It's analytics. Remember Doug Laney's three Vs: volume, variety, and velocity.
Plus, big is relative. If you have never had data available to you beyond what you can lump into Excel's auto-sum function, then a few thousand data points melded together from two sources seems big. What it is likely to really be is darn useful, but not big data.
If a healthcare provider pools biometric data with behavioral data to identify predictors for depression, that's big data; lots of data from multiple sources that changes over time.
However, there are plenty of ways that healthcare provider can utilize analytics without getting into big data's data quality issues and basic logistical challenges. In most cases, those applications focus on identified goals.
For example, it's analytics if the provider looks at the average length of hospital stay and common treatments for certain medical conditions. That provides useful data and can be accompanied by an action item that involves closer looks at the reasons behind outliers, such as a patient whose stay was twice as long as average and why another patient was discharged much sooner than average.
The above scenario becomes a case for big data when -- as some providers are doing now -- you add in non-medical data sources such as home, family, and economic factors for each patient. In that case, you are looking for causes that can be addressed.
For all types of business, not just healthcare, an inordinate focus on the "big" in big data leads them to lose sight of the business problems they need to solve. They acquire, and have to secure for years, data that will never be used. They dream of a common enterprise data lake that will support hundreds of diverse business unit applications. They build data bureaucracies. They spend so much time thinking big that they forget the reasons they built those applications in the first place -- to run the business better, to serve the customer, and to sell more goods and services.
It's nice to read case studies about successful companies that have invested decades of work into complex enterprise-wide projects that, in total, represent big data. However, you aren't likely to duplicate that in a mere six months, a year, or three years. While you are sitting in meetings spinning up some grand scheme for bigger than big, some simple business problems will be begging for answers now.
So stay focused on solving problems with analytics and then, only when you are ready, tack "big" onto "data."
Is your company focused on solving business problems? How do you keep that focus?