Putting big data and advanced analytics to work
I always describe both a short-term problem and a medium-term problem. The short-term problem is that if you’ve developed a new model that predicts or optimizes, how do you get your frontline managers to use it? That’s always a combination of simple tools and training and things like that. Then there’s a medium-term challenge, which is “How do I upscale my company to be able to do this on a broader scale?” The question then is how to build what I’m going to call the “bimodal athlete.” And what I mean by this is, imagine that we go to a retailer and meet its buyers, or to a technology company or consumer company and meet the people that make the pricing decisions, or to somebody doing scheduling. Here you need people that have a sense of the business, and they need to be comfortable with using the data analytics. If you’re good at data analytics but you don’t have this feel for the business, you’ll make naïve decisions. If you’re comfortable with the feel of the business but you never use analytics, you’re just leaving a lot of money on the table that your competitors are going to be able to exploit. So the challenge is how to build that bimodal athlete and how to get the technical talent. Executing big data There are several things you just have to do. The first is you need to focus. And what I mean by focus is, let’s take a pricing manager in a consumer services company or a buyer in a retailer. They have 22 things they do. Don’t try and change 22 things; try and change 2 or 3 things. Focus on part of the decision and focus, therefore, where the greatest economic leverage is in the business. The second is that you’ve got to make a decision support tool the frontline user understands and has confidence in. The moment you make it simple, understandable, then people start using it and you get better decisions. For a company, if you have 100,000 employees and you’ve got only 14 that actually know this stuff and how to use it, you’re not going to get sustainable change. You don’t have to have 100,000. But you might have to have 10,000, five years from now, that are comfortable with analytics. So, again, link it to the processes, get the metrics right, and make sure you build the capabilities across the company.
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