Thomas Davenport,distinguished professor of Information Technology and Management at Babson Collegein Wellesley, Mass., has co-authored another businessanalytics title that includes a new model to help organizations improve theiranalytics capabilities.
Published early this year, Analytics at Work: Smarter Decisions, Better Results (which Davenport co-authored with Jeanne Harris and RobertMorison) follows up on Competing onAnalytics: The New Science of Winning, which Davenport also co-authored with Harris in2007.
The first book lookedat how organizations can start to make more analytical decisions; the secondhelps 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 Greekletter for change, and in terms of the model, an acronym for five successfactors: D (accessible, high-quality data), E (enterprise orientation), L(analytical leadership), T (strategic targets), A (analysts).
“You can’t beanalytical without data and the companies that are highly analytical tend tohave good data … but more than that, I think the companies that are analyticalleaders also have distinctive data,” he said.
Organizations need to take an enterprise-wide, as opposed to localized, approach in terms ofdata, people and technology capabilities, said Davenport.
“It’s not everybodymaking these decisions on their own. It’s as an enterprise saying, ‘What do wereally want to do? What is our strategy? How does analytics fit into it? How dothe different groups who have analytical capabilities relate to each other?” hesaid.
Analytical seniorexecutives make it easier to develop a highly analytical organization, hesaid. Davenporthighlighted Gary Loveman, CEO and president of Harrah’s Entertainment Inc., asan example.
Organizations mustalso establish a target for their analytical work. “You have to have some focusfor your analytical activity, because at least initially, you can’t beanalytical about everything,” he said.
And good analysts are key to success. “If you want to be good at analytics, you better have somevery smart people,” said Davenport.
There are differentclasses of analysts: the high-level analytics professionals create newalgorithms and communicate them effectively, the analytical semi-professionalscan do “some” digital analytics and spreadsheet work, and the front-lineanalytical amateurs (like bank tellers) need to explain offers to clients in away that is persuasive, he said.
The five stages of maturity
Davenport also presented an overview of the analytical maturity model, afive-stage scale originally introduced in Competingon 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 enterprisesat the stage one level, he said, but stage two remains the most common, evenamong larger organizations. The transition from stage two to stage three is oneof the easiest ones for organizations because all they need is “desire” fromthe senior management, he said.
But analytics isn’t enough
Analytics is a verypowerful and common tool, but other factors also need to change, according to Davenport. There are toomany circumstances of organizations having a lot of data and analytics, but notusing them to make decisions, he said.
“Wesometimes take for granted that when we have better information and betteranalytics, it leads to better decisions, but I have concluded in my research weshould not take that for granted,” he said.
With support from SAS,Davenportconducted a study involving 55 companies. Asked whether they have ever improveda particular decision, about 90 per cent of companies said yes; when asked whatthey used to improve that decision, about 85 per cent said analytics, he said.
But the second mostcommon factor was changes in culture and leadership, followed by better data and then education. Business rules came in fifth and over-rides ofbusiness rules as sixth, he said.
There is thisinterplay between “left-brain, highly analytical, computer-oriented stuff andculture, leadership, education, empowerment,” he said. “That suggests that if youwant to make decisions better, you better bring more than analytics to thetable,” he said.
Other studies suggest “analysts prefer intuitive and experience-based decisions rather thananalytical decisions,” he said. And these analysts hand over theiranalysis to decision and policy makers who may or may not be data-oriented, hesaid.
“We have a wholebreakdown between the data and the analysis and the decisions made on the basesof it,” said Davenport.
On a Gartner Inc. blog, Mark McDonald, group vice-presidentand head of research in Gartner Executive Programs, recommends Analytics at Work “as perhaps the bookfor people looking to establish and sustain an ability to use information indecision-making and process execution.”
“Davenport, Harris andMorison have taken ideas originally expressed in Competing on Analytics and taken them to the next level – reality. Ifcompeting on analytics describes the characteristics of an ‘analyticcompetitor’ and their principles, then this book moves from principle topractice,” writes McDonald.
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