Thomas Davenport, distinguished professor of Information Technology and Management at Babson College in Wellesley, Mass., has co-authored another business analytics title that includes a new model to help organizations improve their analytics capabilities.
Published early this year, Analytics at Work: Smarter Decisions, Better Results (which Davenport co-authored with Jeanne Harris and Robert Morison) follows up on Competing on Analytics: The New Science of Winning, which Davenport also co-authored with Harris in 2007.
The first book looked at how organizations can start to make more analytical decisions; the second helps those that want to become even more analytical than they already are, said Davenport to executives at a recent dinner series event hosted by SAS Institute Inc. in Toronto.
The DELTA model
DELTA is the Greek letter for change, and in terms of the model, an acronym for five success factors: D (accessible, high-quality data), E (enterprise orientation), L (analytical leadership), T (strategic targets), A (analysts).
“You can’t be analytical without data and the companies that are highly analytical tend to have good data … but more than that, I think the companies that are analytical leaders also have distinctive data,” he said.
Organizations need to take an enterprise-wide, as opposed to localized, approach in terms of data, people and technology capabilities, said Davenport.
“It’s not everybody making these decisions on their own. It’s as an enterprise saying, ‘What do we really want to do? What is our strategy? How does analytics fit into it? How do the different groups who have analytical capabilities relate to each other?” he said.
Analytical senior executives make it easier to develop a highly analytical organization, he said. Davenport highlighted Gary Loveman, CEO and president of Harrah’s Entertainment Inc., as an example.
Organizations must also establish a target for their analytical work. “You have to have some focus for your analytical activity, because at least initially, you can’t be analytical about everything,” he said.
And good analysts are key to success. “If you want to be good at analytics, you better have some very smart people,” said Davenport.
There are different classes of analysts: the high-level analytics professionals create new algorithms and communicate them effectively, the analytical semi-professionals can do “some” digital analytics and spreadsheet work, and the front-line analytical amateurs (like bank tellers) need to explain offers to clients in a way that is persuasive, he said.
The five stages of maturity
Davenport also presented an overview of the analytical maturity model, a five-stage scale originally introduced in Competing on Analytics and re-capped as follows in Analytics at Work:
- Stage one: Analytically impaired. The organization lacks one or several of the prerequisites for serious analytical work, such as data, analytical skills or senior management interest.
- Stage two: Localized analytics. There are pockets of analytical activity within the organization, but they are not coordinated or focused on strategic targets.
- Stage three: Analytical aspirations. The organization envisions a more analytical future, ahs established analytical capabilities, and has a few significant initiatives under way, but progress is slow – often because some critical DELTA factor ahs been too difficult to implement.
- Stage four: Analytical companies. The organization has the needed human and technological resources, applies analytics regularly, and realizes benefits across the business. But its strategic focus is not grounded in analytics and it hasn’t turned analytics to competitive advantage.
- Stage five: Analytical competitors. The organization routinely uses analytics as a distinctive business capability. It takes an enterprise-wide approach, has committed and involved leadership, and has achieved large-scale results. It portrays itself both internally and externally as an analytical competitor.
There are increasingly fewer large enterprises at the stage one level, he said, but stage two remains the most common, even among larger organizations. The transition from stage two to stage three is one of the easiest ones for organizations because all they need is “desire” from the senior management, he said.
But analytics isn’t enough
Analytics is a very powerful and common tool, but other factors also need to change, according to Davenport. There are too many circumstances of organizations having a lot of data and analytics, but not using them to make decisions, he said.
“We sometimes take for granted that when we have better information and better analytics, it leads to better decisions, but I have concluded in my research we should not take that for granted,” he said.
With support from SAS, Davenport conducted a study involving 55 companies. Asked whether they have ever improved a particular decision, about 90 per cent of companies said yes; when asked what they used to improve that decision, about 85 per cent said analytics, he said.
But the second most common factor was changes in culture and leadership, followed by better data and then education. Business rules came in fifth and over-rides of business rules as sixth, he said.
There is this interplay between “left-brain, highly analytical, computer-oriented stuff and culture, leadership, education, empowerment,” he said. “That suggests that if you want to make decisions better, you better bring more than analytics to the table,” he said.
Other studies suggest “analysts prefer intuitive and experience-based decisions rather than analytical decisions,” he said. And these analysts hand over their analysis to decision and policy makers who may or may not be data-oriented, he said.
“We have a whole breakdown between the data and the analysis and the decisions made on the bases of it,” said Davenport.
On a Gartner Inc. blog, Mark McDonald, group vice-president and head of research in Gartner Executive Programs, recommends Analytics at Work “as perhaps the book for people looking to establish and sustain an ability to use information in decision-making and process execution.”
“Davenport, Harris and Morison have taken ideas originally expressed in Competing on Analytics and taken them to the next level – reality. If competing on analytics describes the characteristics of an ‘analytic competitor’ and their principles, then this book moves from principle to practice,” writes McDonald.
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