It's time to start treating big data like a call center. Show us the money.
There is no more heavily analyzed business function than the call center. Every call is measured and analyzed, every cost is known, every result put into a spreadsheet for managers to look over and use in decision making. If you believe in a quantifiable business model, then the modern call center is your ideal environment.
To this point big data has been a bit more difficult to quantify. Some of that is the fault of the incoming data: few things in the business world are more "squishy" than social media. But new ways of thinking about data and analytics can make the processes around big data more quantifiable -- and make them a much bigger part of your overall business success.
According to Tom Davenport, a professor at Babson College who also happens to be an expert on data and analytics, it's time for you and your business to enter the world of Analytics 3.0. If you weren't sure that you were thoroughly ensconced in Analytics 1.0, then you've got some catching up to do.
What are the various versions of analytical maturity? I'm so glad you asked. Analytics 1.0, according to Davenport, is what we all grew up thinking of when "decision support" was the topic. If your idea of analytics involves thick paper reports based on internal data, created by white-shirted analysts working in some gloomy dungeon, and delivered weeks or months after the query was made, then you're living in the Analytics 1.0 world of the 1960s. It's hard to figure out the precise value of the analytics because they're so far divorced from action: The value tends to be attributed most often to the decision makers acting on the information rather than the information itself.
If you've made it to Analytics 2.0 then you're solidly in the "big data" camp. Unstructured data manipulated by data scientists using Hadoop means that you're using more data as the foundation for your decisions, but there's still a significant characteristic that 2.0 shares with 1.0: You're still using all this data and all of these tools to tell you what happened in the past. Humans use that information to make decisions about what you're going to do in the future, but there are non-trivial gaps between data gathering and action, though the gaps are probably now measured in days or weeks rather than weeks or months. If you really need to see measurable value from your data, though, you need to step up to Analytics 3.0.
In some ways, Analytics 3.0 is big data with an afterburner, using tons of real data to provide analysis and information almost instantly and (here's the critical piece) at the point where the decision is made. If your salespeople have the ability to make decisions on pricing and service offerings, then information on the customer's accounts, practices, preferences, and history should be put into those employees hands.
Analytics 3.0 requires massive compute power, solid networking infrastructure, and user devices that can access and display information in the most easily understandable form -- but it's also the only form of analytics where you can compare sales before and after, compare results with and without information, and truly put a value on the analysis of data.
If you're interested in learning more about Davenport's take on all this, he took part in a Harvard Business Review webinar that has details. What do you think -- is your business already stepping into the world of Analytics 3.0? If not, what's keeping you from making the move? Let us know -- we can all work together to analyze the reasons in the old-fashioned community way.
— Curtis Franklin, Jr., Executive Editor, Enterprise Efficiency