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.
Cultural Elements Necessary for Business Intelligence Success
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:
•Propensity to scope and plan projects. I am constantly surprised at the number of people who find themselves suddenly managing projects without realizing they were projects. It is essential to think of every task, large and small, as a project and set the scope and timeline right away. This is especially true of business intelligence applications because they are more accurately described as grown rather than installed in an organization (even if technical tools are installed to support the applications). In the story provided, Jo and I interviewed the manager (our project manager), found out who the sponsor was (the CFO), and established the timeline for completion (two days) and the criteria for success (ability to determine the total financial impact of the regulatory change). We recognized it as a project, and we scoped and planned it accordingly.
•Propensity to group data and look for patterns. We immediately looked for ways to leverage the information we had in order to cut the task down to size. In this case, we found that by performing the tasks on one case, we were able to get an 8800% return on each action we took.
•Propensity to think and work as a team. Jo has skills, knowledge, talent and ways of thinking that are different than mine. We used those differences to find and squeeze out as much value as we could from the analytical challenge before us. This mix of approaches gave us an advantage that allowed us to provide better results faster and easier than one person alone could accomplish.
•Propensity toward intelligent laziness. Carl von Clausewitz, the great military strategist, sorted his officers into four groups based on their laziness and intelligence. Dull-lazy officers could be ignored because they don’t do much and, therefore, don’t cause much damage. He got rid of dull-energetic officers immediately because they could cause great damage in a hurry. Smart-energetic officers were given staff roles because they produced a lot of positive operational results. It was the smart-lazy group of officers who were promoted to senior positions because they found ways to leverage resources to the fullest. In the story provided, a more sequential worker would likely have just started at row one and worked through as many rows as time allowed. An analytically oriented person is always on the lookout for ways to cut the drudgery out of the task at hand.
•Propensity to combine data to get results. Very few business applications use just one source and type of data to get the job done. Even our limited application required data from four sources. Just consider how many sources are required in your organization to answer even one question such as patient profitability, outcomes analysis, efficiency analysis and so forth.
•Propensity to be wary of estimates. Early in my career when I was a junior financial analyst, one of my mentors told me the difference between a financial analyst and an accountant is that in finance, estimation is necessary, whereas in accounting, estimation is avoided. In an analytical culture, both are essential; but by using intelligent laziness, even an approximation can have stronger backing through data. In our illustration, Jo and I felt very uncomfortable with an estimate for a population of 3,000 made on a sample of 30 to 40 instances, especially when we could do (and did) so much better using an analytical approach.
•Propensity to reuse information. Jo not only answered the question at hand, but also reused the same information for a half dozen more applications, most of which could not have been imagined or accomplished until after the analysis was done.
•Propensity to reuse methods. Jo and I documented the method we employed so it could be used again on the same application if need be but, more importantly, could be replicated across other applications. We are not sure, but we suspected that this particular finance department was swamped because they did not approach their work with these propensities in mind. We hope not, but our combined experience told us to bet that this was the case.
•Propensity to show off a little. In high school and in the movies, the smart kids aren’t the cool ones. But this is not high school; and in the movies, the brainy geeks show up at the high school reunion in their own corporate helicopters. If highlighting the difference between the analytical approach and the sequential approach helps more people in your organization jump to the analytical side, then perhaps a little showing off is a good thing.
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!