Dears,
The recession is possibly the best thing that has happened to business intelligence, allowing the power of BI analytics to finally come into focus.
BI analytics is about finding patterns in the avalanche of data generated by, and relevant to the business that allow it to anticipate future events and help drive business decisions.
The BI enthusiasts predict “Predictive BI analytics” the next best thing to a time machine or crystal ball. A data-driven crystal ball, that is.
Predictive Analytics like many an IT term, refers to an amorphous discipline that involves using statistical and other complex mathematical analyses of data to predict the future.It is not exactly data mining, the industry experts like to point out, and it's certainly not data mining for data mining's sake. Predictive BI analytics identifies patterns in the volumes of data generated by a business and by relevant external sources. Those patterns are used to predict the future so the business can adjust its strategies accordingly. Using analytics to shape business strategy is not easy to do, however, and not just because it can be expensive or because it's a relatively new undertaking for many but the biggest corporations. Putting predictive analytics to work in the corporation requires change management skills, finding the right people and the right mind-set. Organizations or departments with an "analytical pain," are good places to begin a new BI analytics project
Here are some insights for what CIOs / IT Heads should keep in mind as they get started on a Predictive BI analytics program -- and some reality checks from CIO attendees.
1. Find great analysts.
People good at this flavor of predictive analytics not only are inquisitive and critical thinkers but also like to experiment and are "doggedly persistent." They have to know both the data and the business processes that produce the data inside out; they also need to know how to use the tools -- a rare combination. The best analysts are not philosophers living in ivory towers; they know which business questions to ask for actionable results -- increased profits, Social scientists, statisticians and people trained in Six Sigma have an affinity for this work, as do ambitious data analysts ready to take their career to the next step. The best people are pricey. Competitors will try to woo them away.
2. Put analysts into a centralized group.
These analysts will work better and more creatively if they are together, So, rather than "bury" them in business departments, companies centralize business analysts and locate them near the data warehousing team, the people with whom they will be working closely.
3. Cultivate a fact-based culture.
A fact-based decision-making culture is willing to test assumptions, embraces transparency and often uses dashboards up and down the organization. Such organizations also recruit other analytical leaders. The very top leadership helps decide which analytics projects to take on and when, and funds them. Executive support is critical because the best analytics projects cut across departments.
4. Start by testing one assumption to gain support:
Predictive BI analytics is about testing assumptions, which by definition are hard to dislodge. By starting small, with a pilot project, it is sometimes possible to effect a culture shift. An example: To gain support for analytics, a BI professional at a major retailer of online office supplies asked to test a long-held assumption of the business: the belief that online customers stopped ordering or decreased their orders if a big office supply store, like a Staples, was located within a certain distance of them. In fact, the data showed that the critical factor was not geography, but frequency of purchases. Online customers who placed four orders every 60 business days showed a retention rate of 95%, regardless of the number of stores on the ground. Frequency of purchases is a key metric for measuring customer retention, and the sales exec immediately "got it”.
5. Don't get uppity.
Never come off smarter than the executive you're supporting, or suggest the data model is smarter than the executive. "It is the kiss of death," If the results from the data modeling and statistical analysis make "intuitive sense" to the business exec, as in the example above, so much the better. Too often the team keeps the business intelligence strategy and performance measures to themselves. But what many successful BI initiatives have in common is a large degree of transparency,
The IT head should spend 25% to 30% of his time marketing and selling predictive analytics because once you are scalable as far as number of users using the applications and numbers of processes that take advantage of those applications -- without those economies of use, you don't get ROI and results of Predictive Analytics may go without appreciation and Its important the Tool gets the due credit for the makeover.
If you think about it, Predictive analytics is really the next frontier for competitive advantage.
Good Luck Amigos
Loving P&C
DC*
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