There’s a lot of water under the bridge in the BI space. It has been a very challenging space to work in and solve problems. It’s a complicated beast. The problems are not simple, and there are many problems to solve. The volume of data is growing, the complexity of data is increasing, the speed of data is increasing… so I think all of those things come together to form the perfect storm of complexity, which is very difficult to solve.
But therein lies massive opportunity.
I would certainly do things differently. One of the struggles we had in the analytic BI industry was that it was always very technology / IT focused, very centred around software developers, but we struggled with the business users. So the vision of Indicee, and a lot of these cloud analytic tools that exist today are really about empowering the business user. We wanted to take the entire BI analytics and roll it all into one application – put a user experience on it, dumb it down, make it easy, and charge $20 per month.
I think there’s a lot of cultural factors inside of organizations that need to be put in place in order to execute on these analytic BI strategies. I think organizations struggle a little bit with this. In the start-up ecosystem when we pick start-ups as investors, if you take a VC or an individual investor, the cream rises to the top because there’s a diligence process – the best companies win, the best founders, the best ideas, and so on. This is what gets funded.
The question we must ask is: are the goals and objectives lined up with the technologies that you actually need and want rather than just thinking you need all this predictive stuff? This is what a lot of enterprises end up doing. I think the tenants of the analytics world (descriptive, predictive, and prescriptive) are valid, there are opportunities in all of those. But we need to first ask what do we want our data to do? Only then we can determine if we really need all those things.
I used to be resistant to the term, but I’ve since changed my stance. These titles used to be called business analysts, then it evolved into data science. The technologies were evolving so the complexity was evolving. There was more sophisticated analysis work being done with more sophisticated technology. So this new name update of ‘science’ came up because it felt like they were doing a lot more than a business analyst was doing.
I think the friction that has been in the BI space, in the analytics space in general, a lot of it has been mostly related to where the data is residing, the complexity in the number of silos, and the fact that we have most of the data on prem.