Infographic: Why BI is Key for Competitive Advantage

A great infograhic from the Master of Science in Computer Information Systems program at Boston University.  The BU researchers focused on: growth of business intelligence, management of data, decision-making and budgeting.  Enjoy!

 

BU-BusinessIntelligence-Is-Key

{Originally posted here}

Big Data Collection: What’s in it for Me?

Girl with a bar code on her neck, the protection personal data

Many of us stand to benefit from certain types of big data collection. In the medical field, big data can be applied in numerous ways, for example: predictive modeling for R&D, enhancing clinical trial design, or conducting Comparative Effectiveness Research (CER).

The idea behind big data is that everybody’s data goes into a big pool, which is then analyzed to find patterns and trends.  We may learn that x and y are indicators of condition z later on in life, and we can then institute preventative care… and, that’s certainly using Big Data to benefit people.  [Click here to read more about big data in the Healthcare community]

Or, it is mostly used to give companies a competitive edge, like in the Financial Services industry.  Financial services firms are utilizing big data to transform their processes, their organizations and perhaps, their entire industry. This isn’t quite as beneficial on the individual level as in the healthcare industry scenario, but we still stand to benefit, from lower prices, or higher growth in our long-term accounts, or even just by having expectations based on a big data set.  [Click here for an IBM article on big data in the Financial Services world]

But, on the flip side,  big data could also be used to advertise to us in seemingly manipulative ways.  And what if sensitive data gets into the wrong hands? Many people are concerned about that.

In a research article conducted by BI and data software review firm Software Advice, Consumer Positions on Data Collection and Use, they take a look at how people feel about data collection as a concept, as it relates to Medical, Financial, and Employee-related data.

It turns out many of the people they interviewed do not have a strong opinion, but a few themes were clear. The report showed that people over the age of 45 were more uncomfortable or skeptical about sharing their personal data in general.  The older you are, the more likely you have been burned by sharing personal data. People under the age of 45 tend to be more optimistic.  They are still cautious, but are more likely to share their data if there is a clear benefit to them.

 Comfort With Use of Data (Age-in general)Comfort With Use of Data (Age-if benefit)

 

The full report can be found here, but as a general rule, if a company is going to collect data, people share more generously if the reason and benefit to sharing that information is transparent from the start.

 

2 Questions to Ask about Your BI Project

Two questions to ask about your BI project

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”

Business Intelligence Versus Predictive Analytics

“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 verses Predictive Analytics

 

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[2].

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

How analytics can help communities visualize the future

Analysis Image

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