Networked Intelligence: Connecting Businesses to the Retail Analytics “Smart Grid”

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Eric Green
By Eric Green
Sep 08 2014

As long as meters have been attached to homes and businesses, utility companies have been sending workers out to read them. These technicians have been a fixture of public life for many years, ringing on doorbells and trundling around to check the numbers. But as quickly became evident to energy providers, gathering information on foot clearly doesn’t make much sense in the digital era.

Thus, the “smart grid” was invented: a network of devices enabled with sensors to track crucial data streams (power meters, voltage sensors, fault detectors) in real time and exchange that information with a central computer.

Of course, the smart grid does a lot more than relieve foot traffic for beleaguered meter readers. Because energy isn’t easily stored, it must be generated basically at the same rate that it is consumed.

To achieve this difficult feat with any degree of efficiency, utility companies need to master an array of constantly changing variables: fluctuations in the flow of energy, especially from alternative sources like wind power; inputs from additional generators, such as household solar panels; increased demands from new electronic devices, and the unpredictable consumption rates that arise from proliferating new technologies; and so on.

Building a Retail Intelligence Smart Grid

It’s an extremely complex and sensitive supply chain scenario, something that retail product suppliers will relate to. As the multi-channel universe has empowered consumers to share reviews, compare prices, and use different purchasing platforms, it has also placed greater demands on vendors and retailers to respond to customer demands with lightning speed and flexibility.

But just as the challenges have increased, the dividends that arise from improved efficiency are also greater. The energy smart grid allows utility companies not only to distribute electricity more responsively and therefore save resources; it also lays the groundwork for integrating alternative energy sources in the future. The smart grid saves money, but it also just might save the planet.

Likewise, product suppliers that use retail analytics to integrate their many data streams into a type of smart grid for retail intelligence are going to enjoy the benefits of improved efficiency in the short term. But in the long term, they will already have a platform to handle the many new data streams that will inevitably arise as technology improves and sensors become more ubiquitous.

How to Be Smart: Use Your Data, Not Your Energy

In one sense, the term “smart” just means it has a computer in it. But it also means something more than that, namely, using the full range of available information to achieve the greatest possible efficiency. Or to put it more simply: use what you’ve got, and don’t waste your energy.

Utility companies already have a lot going for them as they continue establishing these grids. Even though they face some architecture challenges bringing these networks online, the existing electricity infrastructure is already in place, and teaching it to be “smart” is basically just a matter of installing the right sensors.

Product suppliers often have more data than they know what to do with. There’s no lack of information, just the means of using it. Sifting through reams of spreadsheets is a bit like walking around door-to-door and asking to read the meter. It’s needlessly time-consuming and patently wasteful, when the tools already exist to integrate that information into one place.

The Possibilities of Comprehensive Data Intelligence

There are countless case studies of companies that have used Business Intelligence software to create a retail analytics smart grid out of all their different data streams. In every case, bringing these integrated data networks online has yielded surprising and unexpected results.

The current reigning Big Data star began exploring the possibilities of online retail analytics in 2001, and has since found stunning new applications for data mining in sports.

Jeff Luhnow, a former McKinsey consultant who was hired in 2003 to use advanced analytics to manage draft picks for the Houston Astros baseball team, originally cut his teeth selling custom apparel online. Using information gleaned from a variety of inputs, he was able to identify purchasing patterns among his customers and alter the ordering process to diminish product returns.

Having established the power of comprehensive data intelligence to prevent negative outcomes in retail, he was able to apply those same principles to sports statistics to get the best results from the players.

Luhnow’s story, which is described in a recent issue of Bloomberg Businessweek, illustrates an important point about the new era of retail analytics. Business Intelligence that’s powered by Big Data is going to take your company to places that you can’t predict.

To find out more about how you can create a retail analytics smart grid for your company, try a demo of the Askuity Retail Intelligence platform.

 

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