Big data gets a lot of attention from journalists these days. Look at any site publishing articles about data, analytics or business in general and I’m sure you’ll find a considerable number praising the potential of big data. It gives the impression that every company should be investing in big data analytics and might even have considered it for your own organization. However, it makes sense to think twice before investing in something that doesn’t actually fit your business model.
It’s true that many companies have successfully implemented business models around the opportunities offered by big data: analytics start-ups, global pharmaceutical companies and information industry giants like Google have all had success with resources like social media feeds, Internet of Things data and genetic codes. It’s also true that the list of sources of big data and the number of use cases for it have grown significantly over the past decade.
A few weeks ago I defined the term analytics use case and considered its implications. I established that the size of the data set plays no role in whether something is a viable use case and it certainly has no impact on its ROI.
Let’s develop this idea further, a primary use case is defined as a generic business issue that needs to be analyzed by a business function. This same issue might exist in multiple organizations or in different offices/countries of the same company, but it will only count as 1 primary use case. Based on our experience from working with numerous clients over the year, we can calculate that there is an estimated one billion implementations of primary analytics use cases globally.
However, only 5 to 6% percent of all these primary use cases really seem to require big data and the corresponding methodologies and technologies. This finding is completely contrary to the image of big data in the media and public perception. In my experience, for many companies, it is far more important and profitable to focus on managing a portfolio of small data analytics use cases rather than investing into unproven big data use cases. Remember that your goal should be to provide a solution to a business issue, not to follow the latest hype.
Using mind+machine you can set up simple and easy small data use cases that can provide great ROI. Big data has the potential to be a great resource. But like all resources, it doesn’t have unlimited application: you wouldn’t use an expensive sushi knife to butter your toast or a smartphone to hammer in a nail; and likewise, you shouldn’t use big data in a small data use case.
If you want to learn more about the common misconceptions that exists about data analytics, check out my new book on the power, potential and logistics of analytics: Mind+Machine.