FREE DM Review Site Registration!
Sign-up today and access DM Review on the Web!

Your FREE registration entitles you to:
FREE email newsletters

FREE access to all DM Review content

FREE access to web seminars, resource portals, our white paper library and more!

   

Business Intelligence: No More Living in the Past

French 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 traditional business intelligence (BI), such as end-user query and reporting, basic OLAP, etc., 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 vendors such as SPSS and SAS is helping business managers spot commonalities, trends and associations among customers that they never would have 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. All it takes is the desire to learn more than you ever thought possible about your customers and the right tools to extract the information.

The Telescope of the Past

Traditional 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.

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 conclusion.

It also smacks of a military truism: "Generals always fight the last war." Traditional BI allows business users to gain great insight into what worked (and didn't work) in the past in order to make decisions. Unfortunately, it doesn't allow for a change in market conditions, customer base or other factors that might influence the future. Like the French digging trenches at the beginning of World War II, you may be setting a strategy for conditions that no longer exist.

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

Venturing into the Unknown

The advantage predictive analytics provides is the ability to go beyond your assumptions and discover things you wouldn't otherwise know. The key is the ability to recognize patterns or associations between seemingly disparate things.

One famous example (that may or may not be apocryphal) is the association between diapers and beer. According to the story, a convenience chain looking at sales data noticed that when men purchased diapers, often they would also purchase beer. The supposition was that, if men were sent to the store by their wives for diapers, they felt they should pick up something for themselves as well. This led to the conclusion that placing diapers closer to beer in the store will increase beer sales.

Whether the story really happened isn't important. What's important is that diapers and beer are not an association most people would make based on pure business knowledge alone. Baby food, diaper wipes, formula, yes. But beer? In this case it was probably luck. Predictive analytics will find that association, and hundreds of others like it, because it doesn't use human assumptions. It simply looks for statistical patterns and tells you what it finds.

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. While it can identify the big trends that affect your entire customer base, it can also highlight small but highly profitable sub-segments of your customers.

For example, let's say you're 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. In my case, I know their predictions are pretty accurate.

All of this happens automatically. There isn't a person at the other end making the decision. 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 easily quantifiable. 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. 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 you're able to see there's a problem and do something about it before those customers abandon you and turn to a competitor.

Here's another example. If the word "blue" shows up often in regard to a product line that doesn't have any blue offerings, it may be an indicator that you need to offer the product in blue. At the very least, it's worth considering, and it's something you wouldn't have known before.

Of course, at the end of the day a human being is still required to take action and make decisions on how to improve on a situation, good or bad. But at least the information is there and available, not hidden away in a file cabinet.

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 people who are willing to go 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 skill sets 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, 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.

Going back to the blue product example, perhaps the total manufacturing cost of adding a blue SKU to the line is greater than the expected sales revenue. In that case, the user's business knowledge dictates that adding a blue SKU, while desirable, is not feasible - especially if the comments also indicate that not having a blue product isn't a barrier to continued sales. The decision can then be based on facts, rather than gut feel or a knee jerk reaction to customer comments, and a potential catastrophe can be avoided.

BI or Predictive Analytics?

With all that being said, organizations that are doing neither are faced with a choice: do they start with traditional BI and then ease their way into predictive analytics or do they make the jump to predictive analytics straight away?

More than anything, the answer lies in the company's culture. Predictive analytics requires a leap of faith that BI does not. The methodology is the reason. Remember that BI starts with your assumptions and then tells you if the statistical patterns are lining up with them. Predictive analytics simply looks for patterns anywhere and everywhere and, as a result, may take you far afield of your normal business thought patterns. That's a jump some organizations are willing to make, and others are not.

Here are a few questions to consider when deciding between the two:

  1. How willing is my organization to break away from established ways of thinking? If the corporate culture is one of following the status quo, this is the way it's always been done, predictive analytics will make your coworkers' heads spin on their necks. Better to ease them in with BI first.
  2. Do you have an analytic culture that uses a structured approach to business planning? The more analytical the organization, the better suited it is to predictive analytics because the people within it will know how to turn the data into useful information. When presented with the trends, they'll know how to process them.
  3. What is the established methodology for forecasting and budgeting? Predictive analytics could produce results that are significantly at odds with current forecasting techniques. If there will be great resistance to changing the way things have always been done it may not be the right option.
  4. Do outliers (a few big sales, a few underperforming offices) have the potential to significantly skew the big picture? Predictive analytics works best over a large group. If a few players can quickly change the landscape it becomes more difficult to accurately predict future trends versus historical expectations.
  5. What about external factors such as business cycles, currency fluctuations and natural disasters? If that's the case, you may need to have the resources to bring in weather data, government economic forecasts, etc. to provide the base of data necessary for the calculations.
  6. Do you rely on early warning signals/indicators in your business to spot trends or identify potential trouble spots? If you do, moving to predictive analytics will be an easy transition since it quantifies these types of factors and helps you spot others you may not be considering.
  7. Does your company have and follow a 12 to 24 month business plan? Surprisingly, many don't. If it does and it requires future facts and data to develop, predictive analytics can help with that process.
  8. Is the staff familiar with advanced statistical tools? Generally actuaries and economists are familiar with these types of tools, while operational and line managers are not. It's definitely an easier sell if there's already some confidence in advanced statistical tools.
  9. Can those people become "super users" who mentor others into making the jump? If predictive analytics is restricted to a very small group of users it will be difficult for the organization to see the kinds of results that are promised. It's when predictive analytics permeates the organization and taps into its full brainpower that it really has an impact.
  10. Can you identify three cost savings or revenue opportunities from your core business operations where predictive analytics will help your business? While predictive analytics can help you discover the unknown, it definitely helps to know there are opportunities already available. Being able to point to areas where having that kind of information can make an immediate impact - "if only we knew then what we know now" - makes it easier to justify the effort and expense of adding a predictive analytics solution.

Live for Today ... and Tomorrow

It's time to put the telescope of history away and start putting your customers' habits 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.


Tom Burzinski is a 20-year veteran in the fields of business intelligence and data warehousing. He has led successful engagements for both public and private organizations including retail, health care, life and property/casualty insurance, financial services, manufacturing, telecommunication and government.

For more information on related topics, visit the following channels:



Industry Vendors