Making data analytics operational
After 6 months of silence, I finally take the time to get back behind my keyboard. I would like to say that I used these 6 months to reflect upon my writing, the current data market and came out of this hiatus a better, more informed and well versed person, but that would be a lie. And, despite the current pace at which the social fabric of our society is moving towards considering lies as acceptable and moral, I prefer not to. I don’t really know why I stopped writing for a bit, but most likely because I had nothing to say. So today, brace yourselves for a semi-informed opinion piece on data analytics, because I actually changed my opinion a bit on it through real-life experience.
My opinion then: analytics are a fringe use case of data management
In my article “Why data driven companies should stop investing in data analytics” I argued for the death of dashboards. I still stand by that point of view, as too often the Business Intelligence (BI) platforms are an end point of the data life cycle. Countless data replication processes, ETL, busses and other goldengate push data into data warehouses or data lakes where data scientists pat themselves on the back by showing dashboards that could potentially contain information to be integrated in the current business processes. Quick aside and nugget of knowledge from my PhD friends: if your title contains “science” in it, you’re not a real scientist. Shots fired. Moving on, while I still stand knee deep in stale data lakes despite being on my soapbox, there is one thing I did not consider enough: Machine Learning algorithms. There are two main reasons why the existence of machine learning algorithms as they are implemented now changes my opinion. First and foremost, the problem I describe of BI being the end of the data chain and its outcome only being driven by humans trying to improve business process can be alleviated with analytics automation via these algorithms (to some extent at the moment, but will be more and more true as the technology progresses). Secondly, ML needs access to data lakes, not operational big data. The algorithms need to be able to train using any data sets, looking at data from any angle in order to make usable predictions.
My opinion now: analytics need to be better integrated in the data life cycle
Consequently, here my proposal to the data world. We need to envision an architecture where data warehouses are not the raiders of the lost ark type but more the amazon type: they need to be an inherent part of the data life cycle. Drilling a bit further in the architecture I contemplate, your data as a service layer would feed current data sets to your data warehouse, where ML would run asynchronously, but the outcome of these analytics would then feed back the rules of data manipulation embedded in your DaaS layer. If you manage a constant feedback loop of the kind, your end user application served by your DaaS will constantly get fed more accurate and relevant data, which in turn can enable the next generation of platforms: Information as a Service. But that’s for another day.