How biz analytics can transform data to knowledge

In 1997 Reed Hastings returned a video of ‘Apollo 13’ to Blockbuster-and was charged $40 in late fees. The software entrepreneur wondered why video stores didn’t charge like gyms-with a flat monthly fee per month, and unlimited use. But would customers really go online, order their movies, wait for delivery from the Postal Service, and then return the videos? And could his start-up company, Netflix, compete with Blockbuster’s network of thousands of US stores and $3B pa revenue? 

Today, Netflix has 9.4M customers and revenue of $1.36 B (2008 data). It offers free shipping and return of DVDs anywhere in the USA. It has been successful in part by analytical capabilities it has cultivated, such as a movie recommendation engine for customers, and quantitative models it uses to price movie distribution rights from film studios.

 

Professional data management and analytics are becoming a basis for competition for an increasing number of companies.

The gaming firm Harrah’s, for example, has chosen to compete on analytics for customer loyalty and service, rather than on building the expensive mega-casinos in which its competitors have invested. Amazon.com uses extensive analytics to predict what products will be successful, at what price to sell them and how to wring every bit of efficiency out of its supply chain. At Dreyfus, a mutual fund, analysis of customer information defined segmentation helped reduce investor attrition from 22 to 7 percent a year.

Researchers have begun to document the returns that companies can achieve from investments in analytics initiatives. A study by IDC found that analytical projects focused on manufacturing had a median ROI of 277 percent; those involving financial management had a median ROI of 139 percent, and those focused on customer relationship management had a median ROI of 55 percent.

Virtually every major company uses some form of statistical or mathematical analysis, but some take analytics much further than others. Thomas Davenport, in research for Babson Executive Education, has identified four key attributes of firms that compete on analytics:

• Widespread use of not just descriptive statistics, but predictive modelling and complex optimization techniques;
• One or more senior executives who are strong advocates for analytics and fact-based decision making;
• Substantial use of analytics approaches across multiple business functions or processes;
• Movement toward an enterprise-level approach to managing analytical tools, data, and organizational skills and capabilities. So where should small businesses who wish to apply analytics begin? There are a number of required components, including software applications, IT architecture, business processes and governance. But the first place to start is data: aspiring analytics competitors need to improve their transaction data in order to provide a solid foundation for fact based decision making.

A first step would focus on cleansing the data by applying rules that remove all possibilities for false duplication. There are new software packages becoming available that provide logical frameworks for enterprise information, such as IBM’s WebSphere Product Center and Customer Center. These should be used to ensure that customers and employees create a single, correct version of the truth so that data can be used in a variety of applications without inconsistencies. A data architecture, eg IBM’s Zachman Framework, can provide an overarching model for the various aspects and perspectives of enterprise data.

The path to becoming an analytic enterprise is not simple, but the rewards are great, as the examples above illustrate. These companies will find ways to outperform their peers, by making consistently better decisions.