Business Intelligence is More Than Just a Fancy Graph

"Business Intelligence" is a hot topic these days. There are companies that produce business intelligence products such as Microsoft, Business Objects, Dundas Charts, and so on that focus on visualization of data with the approach that by providing a simplified view of complex data it will enable key stakeholders to make decisions.

A good example is Dundas Charts - the company produces an amazing set of widgets and controls for building dashboards, charts, and visualization of data.



The price is right too - you can buy these fancy BI controls for a license of about $1000.

However, in my experience with Business Intelligence applications, I have come to the following basic conclusion:

Visualizing data is relatively easy.
Obtaining the data in an enterprise environment is hard.
Defining what the data means is even harder.

Let me give you an example. Wait Times are calculated in Ontario and presented to the public in order to show accountability for the funds being invested into improving access to surgeries.

Here is what the data looks like:



As you can see, the indicators are quite simple. The data could have been visualized in a number of different ways such as a red-yellow-green style KPI, a guage, charts, etc. Implementing this would simply involve taking a control library like Dundas Charts and feeding it the above data to get a graphical representation. However, visualization is actually not that valuable - the average person does not need a graph to understand a basic set of numbers.

Obtaining this particular set of data is hard. It requires the synthesis of raw data coming in through automated feeds from about 150 hospitals into a central database that is then scrubbed to match patient records and wait times records together. The cost of building this application to collect the data was millions of dollars - the cost of producing the PDF file containing the numbers is significantly less.

Defining the original business rules, definitions and targets for how wait times data was to be collected was even harder. What does a "Wait" actually mean? There are in Ontario two different wait periods (called Wait One and Wait Two). Wait One is defined as the time it takes for you to get the appointment with the doctor who provide the diagnosis. Wait Two is defined as the time it takes between the point where the doctor provides the diagnosis and the point where you get the procedure. This is what the current Wait Times application tracks - Wait Two data. Different provinces have different definitions and track different wait intervals. In addition, you will notice that only certain types of procedures are represented. These were chosen through government priority - another period of business analysis that took several years to define, prioritize and fund. Similarly, the "targets" were defined through another complex process using clinical experts who invested significant time to analyze and provide recommendations on what was the clinically appropriate targets.

If you are looking at a report, a set of numbers, etc. you are looking at the tip of the iceberg - the amount of energy, work and thought that goes into defining the data and then obtaining it is enormous in comparison to building a fancy graph. In the case of Wait Times, producing the report now is done by running automated processes. The cost of the underlying system to obtain the data cost millions of dollars and took years to implement.

So the next time you look at a report, appreciate the amount of work that has gone into it. And as an IT professional, the value to the customer is in helping them to define the data and then obtain it from a variety of data sources. Produce the actual report is simple in comparison.