Ah, BIG DATA. In the last few years there haven’t been bigger buzz words than those. If you haven’t heard of it, then I hate to break it to you, but everyone and their mother have been talking about it, look at the Google searches over last ten years.
Working at an artificial intelligence startup, we’ve seen our fair share of clients/prospects looking for an alternative to all of the dashboards out there (Tableau, Domo, etc.). With these dashboards (just a fancy word for all of the data and charts about your business on one screen), they don’t scream at the user what they should know, or what business decisions need to be made next based on all of their current data. It takes interpretation and people are afraid to get the answers wrong.
As we continue on this path of “sensor-ized everything”, people and businesses will be able to collect more data on themselves and about others, to possibly influence decisions. Regular consumers with their FitBits and businesses with rewards cards and cookies in their websites; all of that data is now at their fingertips, but the question is, how do we turn all of that into actionable items? But more importantly, the correct actionable items?
Today companies are working to figure out how to interpret all of this new found data and how to act correctly upon it. Sure, you can throw all of the correlations, relationships, and other fancy stats at these new data sets and find which one leads more directly to increased sales. But the funny thing is, there is rarely one answer to this question. What people need are instantaneous perspectives and explanations as to what all of this means to their business without having to interpret the correct answer, and to have those explanations change as the data changes, to better help businesses understand the current state of their business.
The way I see it, there are a couple of answers to the “big data” problem:
1.) Businesses need tools to not only aggregate all of their old and “new” data, but a way to communicate that data, and its every changing properties. The only way to do that is through hiring people to dig into and communicate all of this data. But that is hardly feasible, given the capital needed to hire the necessary talent. That’s where artificial intelligence comes in.
Now, let me rant a little bit. Artificial intelligence can mean numerous things, and it means something different to everyone. “Deep learning” is one practice of AI, “machine learning” is another. People associate “algorithms” with AI. Heck, you could even classify the first chess program that beat a person as AI.
What’s different about today’s AI, however you want to classify it, is it can begin to understand the outcomes of it’s analysis and communicate it. That’s where Narrative Science comes in. When the world starts collecting more data, businesses should have systems/applications in place that allows the data to speak to us, instead of employing more resources to look at dashboards to give us the same insight.
Oh, and the second answer to “big data”, in my opinion, is good ol’ common sense.