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Tuesday, August 10, 2010

BI & Analytics: Making the Data Work for You


In a recent HBR survey, 85 percent of the executives said that information and leveraging data were essential to the growth of business. However, only 7% of the respondents thought they were doing a great job using data to retain customers and create new products and services.

So where's the disconnect - and how can companies close that gap?




At a "Competing on Business Analytics" event sponsored by SAS and Accenture in Chicago this month, Professor Tom Davenport, author of the new book "Analytics at Work", outlined some of the "best practices" for companies as they move to use their data from greater competitive advantage.
First he suggested that companies make sure they have right capabilities in five areas: data, enterprise, leadership, targets and analysts. (This "Delta Model "can be used as checklist for becoming analytical or moving forward as an analytical company.)
Data must first be clean, common, integrated and accessible in a central data warehouse. But more important, organizations can realize advantage by having data that competitors don't. This entails measurements that are new, distinctive/proprietary, and important. For example, Marriott has proprietary metrics on revenue optimization and Harrah's measures employee "smile frequency," which predicts customers' experiences.

To become more analytical, organizations also must go beyond managing data locally or in silos. Successful analytical competitors manage their data and analytics program at an integrated enterprise level. They create enterprise-wide analytical capabilities and invest in enterprise-scale analytical technologies.
Organizations that become more analytical have leaders who fully embrace analytics and lead the company's culture toward fact-based decision making. This is the most critical trait of analytical companies, and remains extremely rare, Davenport said.
With limited analytical resources and limited budgets, Davenport said that analytic teams must pick a primary strategic target for their initial analytical efforts (such as marketing or supply chain) as well as a secondary target. Over time, the use of analytics and analytical decision making will expand in an organization. But long-term success starts with a targeted project that can displays real results on the bottom line.
And organizations can't become more analytical without analysts located throughout the company; Davenport said. The types of analytical talent required include: 1) Champions, who lead analytical initiatives (perhaps 1% of the organization); 2) Professionals, who can create new algorithms (5-10%); 3) Semiprofessionals, who can use visual and basic statistical tool (15-20%); and Amateurs, who use spreadsheets (70-80%). Organizations need each of these types of analysts to move into the top ranks of analytical competitors.
The Chicago program also included a question and answer session with Mike Haaf, former SVP of Sales, Marketing, and Business Strategy. When Haaf arrived at the North Carolina-based company in 2003, its performance was suffering and cash for investments was scarce. So he began to target small projects that could show immediate results on the bottom line. This allowed him to "bootstrap" analytic operations throughout the company.
The first effort was to determine what was really working in marketing programs through the use of randomized testing approaches. When the testing revealed that a program wasn't working, Haaf redeployed the dollars into more research to redesign Food Lion's market strategy.

Since then, Haaf has been able to put in place an automated test and control environment. He helped Food Lion identify customer segments and store clusters and then create different products, services, and brands based on geo-targeted customer needs. The company has also been able to maximize loyalty card and direct mail programs, using analytics. Too often, data from such program is wasted, Haaf said.
Haaf described the cultural value of admitting that some of his own ideas didn't work (including an idea he had for a bus route to take customers to stores) when the data and analysis proved them unsuccessful. He also overcame a lot of initial resistance from employees who were wary of analytics, by offering them educational programs, so they could better understand how to make analytics work for them.

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