Last week, TechTarget hosted a virtual seminar titled Leveraging Enterprise Data. The panel consisted of myself, Wayne Eckerson
, Jim Gallo
, and William McKnight
and we discussed several issues surrounding organizations' use of BI and how they can become more effective. For the full recording, click here
General questions included issues surrounding BI maturity, required training, where BI projects should be championed within the organization, and so on. Overall, many interesting points were discussed, highlighting the current diversity in the market place - both of available solutions and of actual maturity and adoption within organizations. Depending on the organization size and structure, the way in which BI is deployed and managed will differ. And these differences can range from having a business intelligence competency center (BICC) to being driven by C-level executives with little internal IT support.
One of the interesting points brought up by Wayne is that BI should be intuitive; that the best environments are those that have intuitive interfaces whereby end users can access and interact with them without additional training or input from outside sources. And even though this was definitely an interesting point, it led me to wonder how many organizations actually have BI applications that are easy to use. One of the issues surrounding pervasive BI is the fact that traditional BI users tend to have advanced analytics skills and analyst type roles within the business. Consequently, what is intuitive to these users will not be to others. This means that when companies consider broadening the use of business intelligence within their organizations, the expertise of a broader audience needs to be taken into account - in many cases by developing something that plays to those with the least amount of technical savvy.
Although many companies have mature BI environments and are able to move towards this type of model of BI deployment, many companies starting on their BI journey are struggling with data issues - identifying the right data sources, ensuring data quality, and preparing the data so that valuable output is possible. And in many cases, the ability to develop widely deployable solutions that are easy to use and maintain are still out of reach.