A scientific approach to business analytics
Harnessing your data requires an analytical sandbox
For many companies, business analytics works when a handful of very sharp people with complementary skills tackle a project. The results can be rewarding, but they often aren't translated into an ongoing, repeatable process. Effective companies turn business analytics from a craft, practiced by a few analysts and decision makers, into an established scientific approach that dramatically contributes, day to day, to the company's bottom line.
In this article, we'll share some successes in taking your enterprise data warehouse and harnessing it to user-driven advanced business analytics through the use of an analytical sandbox. A sandbox uses tools such as Teradata and SAS to optimize the performance of your analytics.
Getting off the treadmill – for good
What happens if the YMCA opens a new indoor pool in your area? Or the recreational soccer leagues move up fall and spring play by a week? A spike in off-season sales will occur, but you won't know to take advantage of the trend unless you've got an incredibly alert manager. Meanwhile, customers who can't find what they're looking for search for it online – and possibly never return to your business.
An advanced analytical approach does two things: It lets business users discover these trends by providing them with up-to-the-minute data in a user-friendly way, and it helps to automate – via alerts and dashboards – certain processes. Business users, for instance, might see a dashboard showing which products had the biggest out-of-season sales jumps and, by digging into the data, can see if an automated suggestion to stock more goggles at Store A makes sense.
An advanced analytics strategy relying on accurate data flowing quickly to business users can dramatically affect many areas. Here are a few examples:
What does it take to do this?
Let's take the real-life example of a successful sporting goods retailer. The company is using a sandbox approach alongside SAS to assess the value of the company's advertising efforts. Analysts can measure the success of each communication vehicle in a campaign conducted simultaneously with email, catalogs and retail fliers. They can also investigate how each medium interrelates with other media. Being able to perform most of the data preparation in Teradata without the help of IT – which is simultaneously working on other important data warehouse initiatives – saves time for IT and helps the marketing team gain the benefits of the data faster.
In addition, the technology is utilized to make the company more agile. With the data warehouse, the company receives each day's sales data by the following morning and can assess conditions and proposed steps – such as launching an email campaign – to mitigate problems and boost sales. With the old system, producing the same information would have taken days.
Keeping your system tuned
It is not unusual for large insurers to work with data sets of tens of millions of rows or more. Solutions that promise to use algorithms to detect outliers and alert investigators to suspect claims often create too many false positives, or it takes too long for IT to format the data to run a fraud detection solution against it. Many contracts require insurers to pay within a certain period of time, so investigations are initiated after the claims are paid. This model typically yields no more than 5 percent of the dollars lost to fraud.
What happens when your data is coordinated and your analytics are highly advanced? One state prevented $14 million in fraudulent Medicaid claims from being paid out and detected an additional $27 million in fraudulent claims that led to indictments. A private insurer detected and saved $11 million in one year of using a finely tuned solution. Investigator productivity climbed 30 percent as activities that once took a day now take minutes to execute.
A solution can be tuned to kick out cases to just the right business user. A spike in gym shoe sales? That will go to the buyer who orders shoes. A jump in questionable high-cost medical tests? The investigator with the skills in that area will receive the alert.
Those sharp people we mentioned in the first paragraph of this article? They're still doing what they do best. But they are able to do more of what they do – and in a more timely fashion – through the use of an analytical sandbox embedded within their EDW. The sandbox enables not just a faster, more current analysis, but also easier distribution of the results to the right people. Most importantly, using an in-database sandbox enables analytics that just aren't possible without one. Better information getting into the right hands faster – who would argue with the value of that concept?
* A version of this article appeared in InfoManagement Direct, Sept. 23, 2010.
This story appears in the Third Quarter 2011 issue of