If you are considering a Business Intelligence Project, there are two questions you should consider. These questions will help you pinpoint what exactly you may need to do with your BI project, as opposed to simply getting “analytics” or “implementing a BI tool”. Anyone who has experience in the BI market knows that the nature of these projects can be deep and involved, and it helps to have a clear idea of what you are trying to accomplish.
From that article, “Two Questions That Will Dictate the nature of your Business Intelligence Project” on DashboardInsight.com, the first question is, can we discover significant relationships in our data that aren’t apparent without elaborate data mining techniques? The example the article presents is of Target’s uncanny ability to recognize a women who is in her second trimester of pregnancy–an ideal time to become the object of a woman’s buying habits, and and ideal time to ship out some coupons! This would be a significant relationship that is hidden in the data — a change in the type of lotion or the purchase of a large bag. Targets ability to, ahem, target this change in a woman’s life wins them a customer for life and big bucks in the long term. [Read more about Target and pregnancy marketing here]
The second question is about accuracy– can you improve on a common, repeated action to improve customer retention, marketing efforts, or predict customer behavior? This type of project usually involves looking at large amounts of historical data to see what is to be learned in order to avoid making the same mistakes again. As markets become over-saturated, the retention of customers is very important, if not crucial, and this type of BI project improves customer service and marketing efforts to large effect.
Read the full article from Dashboard Insight: “Two Questions that will Dictate the Nature of Your Business Intelligence Project”
“No matter what, you still need business intelligence to know what really happened in the past, but you also need predictive analytics to optimize your resources as you look to make decisions and take actions for the future.”
The blog clearly explains the complementary nature of Business Intelligence (a.k.a. descriptive analytics) and Predictive Analytics despite the various differences between the two. See excerpt below.
Business intelligence looks for trends at the macro or aggregated levels of the business, and then drills up, down, or across the data to identify areas of under- and over-performance. Areas may include: geography, time, products, customers, stores, partners, campaigns, or other business dimensions.
Business Intelligence is about descriptive analytics (or looking at what happened), slicing-and-dicing across dimensional models with massive dissemination to all business users.
Predictive analytics, on the other hand, builds analytic models at the lowest levels of the business—at the individual customer, product, campaign, store, and device levels—and looks for predictable behaviors, propensities, and business rules (as can be expressed by an analytic or mathematical formula) that can be used to predict the likelihood of certain behaviors and actions.
Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.
View Full Post - Business Analytics: Moving From Descriptive To Predictive Analytics
Source – EMC
Image Courtesy of Stuart Miles/FreeDigitalPhotos,net
An urban development initiative in Austin, Texas is using enormous amounts of data pulled from various agencies to create astonishing 3D simulations of what communities would look like based on certain assumptions and inputs contributed not only by engineers and city planners but pretty soon by citizens themselves.
For years businesses have been using analytics software to predict the outcome and measure the performance of campaigns and projects but the City of Austin is using new analytics data visualization tool create interactive and collaborative images of the future.
Read Full Post: How analytics can help communities visualize the future
Source: Canadian IT News