Business Intelligence: Today’s Decisions, Yesterday’s Data

rench writer Victor Hugo once said, “We see past time in a telescope and present time in a microscope. Hence the apparent enormities of the present.”

Yet, when it comes to business intelligence (BI), often the opposite is true. We receive mountains of data on what’s already happened?the things we can no longer affect?all the while missing out on information about the things we can still change. It’s an odd way to do business.

The landscape appears to be changing, though. Predictive analytics software from publishers such as SPSS and SAS is helping business managers spot commonalities, trends, and associations among customers that they never would’ve seen before, which in turn allows them to refine their selling efforts. And it’s doing it in real time, which means companies can react quickly and take advantage of those trends rather than finding out about them after the fact.

In other words, business intelligence is becoming less about storing and regurgitating data, and more about creating knowledge. Here’s a look at where it’s headed.

The Telescope of the Past
Business intelligence started out as a means to use historical data collected over a period of time to predict trends. Analysts would spin through a mountain of data, and use their business knowledge to determine future strategies. This methodology is still the most popular today.

BI often turns into just more BS.

While typically better than pure “seat of the pants” guesses, this method is still limited by the knowledge and/or interests of the users. In some cases, it follows the adage “research proves what the researcher set out to prove.” Business users start with an assumption, and then use data gleaned through BI to support the desired conclusion.

The thing to keep in mind when looking at historical data is the farther an object is from a telescope, the smaller it appears. In the telescope of history, the farther your data is from the present, the less significant it is to what’s happening today.

Data Under the Microscope
In science, a microscope is used to look for very small things that can have a large impact on our lives. Predictive analytics does the same thing, giving you the ability to go beyond your assumptions and discover things you wouldn’t know otherwise. The key is the ability to recognize patterns or associations between seemingly disparate pieces of data.

For example, let’s say your company is selling commemorative plates. How valuable would it be to know that people in Wisconsin whose family income is more than $60,000 per year buy three times as many plates as any other customer group? Or that customers in the Green Bay area almost always by a plate with a U.S.A. theme, but rarely purchase one with a movie theme? And that they are four times more likely to make a purchase in June than in September? Do you think that would have an effect on your direct marketing efforts?

With traditional BI, it would be difficult to dig out this kind of data unless you already knew to look for it and ran a query or built a model to pull it out. Predictive analytics finds it for you automatically.

A good way to think of predictive analytics is as “what-if?” scenarios on steroids. It spins through millions of pieces of information, finds the associations, considers the variables, and then predicts what is likely to happen if you take a particular course of action.

Amazon.com is probably the best known user of this type of thinking. Once you register and make a purchase at Amazon, its predictive analysis engine starts churning. It looks at what you’ve purchased, and looks at what else other people who’ve purchased what you’ve purchased have bought. The next time you return to the site, Amazon presents you with a list of merchandise you’d probably be interested in. They simply use statistical probabilities and all the data at their disposal to tempt you into disregarding your budget and spending more money at Amazon.com.

Your Thoughts Matter
Most of this discussion has focused on things that are relatively easy to quantify. Every purchase has an SKU number, and while deriving patterns from millions of bits of hard data is a number crunching challenge, it’s still fairly straightforward.

One of the values of the better predictive analytics tools, though, is their ability to identify patterns in soft data, such as comment cards, as well. If the simple text is input properly, predictive analytics can be used to identify patterns that can have a huge impact on the business. For example, if the words “customer service” and “sucks” show a pattern of association, you know you have a major problem that needs to be addressed, preferably sooner rather than later. With a little digging, you can even find out if that’s an association that goes across the board, or only pertains to a particular time period. The point is the company is able to see there’s a problem, and do something about it before those customers abandon you and turn to a competitor.

Making Sense of It All
Another advantage predictive analytics offers over traditional BI is the way it presents information. BI has always been dependent on rummaging through stacks of raw data in order to discover the information contained within. This can be very tedious work, and well beyond the interest level or skillset of business decision-makers. As a result, BI often turns into just more BS.

Predictive analytics tools are now presenting the information in a more accessible format, e.g. charts and graphs that make it easier for non-technical people to visually identify trends and statistics. Business users are able to see the predicted outcome of various decisions more easily, compare that with the costs to implement, and determine which courses of action will produce the best ROI. And they’re able to do it in far less time than ever before.

Live for Today and Tomorrow
It’s time to put the telescope of history away, and start putting your data under the microscope of predictive analytics. There’s a great deal to be learned?and now you don’t even have to know where to look.

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