BI by any other name

I have a bit of an identity crisis with regard to what I do professionally. 

I have spent most of my career working within what we now call “Business Intelligence” or “BI” for short. Before that, this general discipline went by some other names, including Decision Support Systems (DSS) and Executive Information Systems (EIS). Since then the software marketing machinery, with the aid of the industry analyst community, has effectively retired those terms and coined a few new ones. These include Data Warehousing and Data Management for solutions that describe back-end platforms that support the user oriented front-end solutions like Analytics and Visualization. In general, though we tend to use BI to describe the process of acquiring, managing, processing and presenting information in direct support of decision making across business processes and organizational levels.

In recent years, I have focused on the Digital Analytics discipline and tools. This term is used to describe the art and science of capturing user interactions across platforms and touchpoints. We then integrate, aggregate, model and present this data to support decisions around things like marketing spend, personalization features, content management, and product experiments.

For me, the transition is relatively seamless as I think of Digital Analytics as simply a specialized form of BI. The basic processes are the same. We acquire the data, manage it, and present it in such a way as to discover patterns, model outcomes, and track results of previous decisions. The skills required are similar as well: You need Business Analysts who can bridge between the technical and business side personnel, developers who make the tools work, QA folks to verify the results and experienced managers who can keep everyone rowing in the same direction.

Since Digital Analytics is still a fairly young discipline, there is a perception out there that it is really something distinct and different from BI. There is some truth to this in the sense that the software tools universe is still pretty bleeding edge and fragmented - new big data/data science startups seem to appear on the scene almost daily - and few true data integration standards have emerged. This works to the advantage of relatively experienced practitioners and consultants who can command premium rates by promoting themselves as Digital Analytics experts.

We have seen this all before in the early days of recognizing BI as a discipline and a wave of OLAP and SQL-based reporting tools hit the market in the 1990’s. In those days, purchase and hiring decisions were often made outside of IT as Technology executives were slow to accept the importance of BI applications and the notion of end-user computing in general. Eventually, the tools market consolidated into a small number of dominant players and BI specialists became easier to find and identify. IT moved in to regain control of the spend and inject some needed discipline into application development and maintenance.

Currently, we see the investments in Digital Analytics driven mostly by business side organizations and facilitated by vendors who offer their solutions as a service that can be stood up almost entirely without the participation of IT. I think we will see history repeat itself. Business users are realizing that Digital Analytics data has limited value until it can be integrated with data from other business process like CRM, supply chain and ERP/Financials that IT manages. The best tools will continue to be absorbed into the enterprise software firms, and issues like data governance, privacy and security will need to be addressed by IT professionals who have the necessary experience. Beyond that, the market will force a balance between supply and demand for the specialized big data and predictive modeling skills that are scarce right now. Beyond that, those who are already skilled at application development, data presentation and interpretation in other domains will adapt their skills to the Digital Analytics space.

As the majority of businesses face their customers and partners using digital touchpoints, the information these interactions produce will become part of mainstream BI and digital analytics will become just another BI data domain. IT will embrace its role in managing that data, the technology will standardize, and the valued expertise will center on the data itself and the processes that add value to that data.