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Originally published enero 22, 2008
Not all organizations have an analytical culture, but such a culture is essential to achieving success with business intelligence. Because business intelligence capabilities are not so much installed as they are grown, they need a fertile, analytically rich environment. This is true in healthcare organizations as it is with businesses of any type. Consider this story.
A while back, Jo, one of my fellow analysts, and I were in neutral for a few days awaiting a decision by our project sponsor. A finance manager at the company asked us to help him with a task that he and his staff were unable to get to. The task involved a regulatory change that affected roughly 3,000 employees. The manager e-mailed us a list and thought that between the two of us, we should be able to analyze maybe 30 to 40 records. From this, he would then estimate the impact for all 3,000 in time for him to report to the chief financial officer in two days.
At first, we braced ourselves for a potentially complex, laborious project. We started by “interviewing” the manager by phone for thirty minutes, looking for the sticking point. The study turned out to be pretty straightforward, but involved a tedious sequence of looking at one government website to get rates, converting each into a code used on another website, and then translating that code into a dollar amount on still another website. This dollar amount was to be compared to a company-generated dollar amount on the spreadsheet he sent. The difference was the amount of risk to the company.
After we hung up, we took another half hour to decide on our approach. We sliced, diced, sorted and filtered the spreadsheet looking for patterns. Our first discovery was that there were not really 3,000 cases to be solved, but thirty-four cases, each repeated an average of eighty-eight times. Next, Jo found and downloaded a crosswalk on one of the sites between the codes and the rates. She then did screen-scrapes of the other website into her spreadsheet and built out the other cross-reference we needed.
By noon, we had the entire analysis done. No estimation was needed, and we had time to double-check the numbers. In addition, Jo came up with six recommendations based on the patterns we found. Two of the recommendations provided additional risk reduction for the company, three offered ways to actually save money as a result of the regulatory change, and one involved a design to automate this analysis for future changes.
We shipped the whole package and asked the manager to call us, which he did that afternoon. He thanked us for our quick work, but sadly never asked how we were able to accomplish it so quickly. Plus, he never inquired about the recommendations Jo put together even though we walked through them carefully. We wondered how many other analyses were bogged down in his work group, and how many were never even started because the group just did not have the time.
This was not a big project by any measure, except that in three hours we had helped the company save a little over $100,000. I would like to say that Jo was the hero of this story. Better yet, I would like to say that I was the hero. But neither is the case.
The real hero of the story is the analytical process we used. And it is that analytical process that is essential to successfully employing business intelligence on any scale at any organization in any industry. Furthermore, most of the analytical process is actually a mind-set embedded in the culture and methods of an analytically oriented organization, division, department or work group.
In order to be successful, business intelligence applications must be used. In order to be used, the people using them need to see the value of an analytical approach to the enterprise as well as to themselves. Whether or not your business intelligence initiatives pay off, therefore, depends on the culture into which you grow your business intelligence applications.
Many people do not view the world and their work analytically. Rather, they prefer to view the world in concrete, operational terms and their work as simply a sequence of tasks. Over the past two decades, I have kept a list of the people I interviewed for business intelligence projects, decision support systems, strategic planning efforts and financial modeling and analysis projects. This list has 3,746 names on it, ranging from C-level executives, directors, managers, doctors, nurses, researchers, scientists, buyers, plant managers, entrepreneurs and administrative assistants. I am not saying this is necessarily representative of the population of your organization, but it does represent a handy cross-section of roles and views toward work. I have found that about 80% prefer to work sequentially (concrete, operational) and 20% prefer working analytically (abstract, conceptual). One approach is not right and the other wrong. After all, many of those in the eighty-percent category are out there saving lives and creating real economic and social value. But I believe that they are expending too much effort and not exploiting their talents to the fullest. An analytical approach typically leads to greater success with far less effort, as illustrated in the provided anecdote.
Let’s break down this anecdote into some of the elements that lead to analytical success. I like to refer to these elements as analytical propensities. An analytical culture is one where most of the people have a:
This list of propensities is not exhaustive, but there are some elements of several of the most successful methodologies represented in this list, such as Six Sigma, lean, TQM (total quality management) and, of course, business intelligence best practices.
In addition, behind every successful business performance management initiative, information management architecture and embedded intelligence capability is a culture where analytical thinking fits in and works well.
Take a look at your business intelligence applications and how they are faring out in the real world of the culture in which they were placed. If you are hearing numerous success stories about people doing amazing things with the information they are accessing, then this is an indication that you have an analytical culture and you are tapping into that analytical propensity. If not, then you may have to go out and look for those stories or rethink how your application is reaching your market. Whatever you find, you and your organization will be better off with business intelligence capabilities that fit your culture.
Thanks for reading!
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