Most of us at Platon have been working with BI for many years, some even for decades. And we have seen it before – new disruptive technologies changing everything, or at least claiming it did. But this time I actually believe something is more disruptive as ever. The BI space has been its own little well defined niche for years, with its own specialized players (including the mega vendors) claiming market shares for platforms and toolsets. For years the BI toolsets has pretty much been defined and grouped into nice little boxes and people like us (the BI Pros) “knowing” exactly what’s best to use in each situation. Then today, it is no longer possible to browse the net for BI stuff without getting in touch with “Big Data” within seconds of browsing. All BI vendors go crazy about Big Data about their latest acquisition, initiative or partnership in this space. Actually, if you want to follow the hype in more close-up, my good friend Andrew Brust has just started a great Big Data blog on ZDnet covering in much more detail.
Now, I’ve already mentioned it four times, but what is Big Data? For IBM, Gartner and Forrester it is identified around a number of (three, four even five) characteristics beginning with a V – like Volume, Velocity, Variety, Variability. And I agree very much to that definition, but there is definitely more to it than v’s. To me Big Data and anybody embarking on a Big Data journey, it is basically three things beyond the Data aspect of the v’s.
- Big Data Infrastructure. This is certainly about technology, but still very important to understand, the ability to handle datasets larger than our conventional BI platforms is capable of, and at a much greater speed. Years ago Platon realized that BI infrastructure is becoming extremely important and we build our Platon Infrastructure (PI) business division around it. Big Data infrastructure requires yet even more skilled professionals and certainly formalized BI best practices around it, than ever before. Luckily Platon are very prepared and we can also add the velocity dimension to the equation in terms of real-time streams as well as handling and storing any new types of relevant data when variety also comes into play. For those of you not “infrastructure ready” or without access to skilled professionals, Cloud Computing (like Microsoft Azure etc.) could also be considered a solution in near future.
- Advanced Analytics is also technology to some degree, but to put it short, it is about making sense of data using business knowledge, technology and various types of logic/algorithms. Smaller data(sets) can be reported on using tables and cross tabs. Big Data must have the essence extracted and noise removed before use; otherwise we are overloaded or just unable to comprehend it. There is a book with the title “Liars and outliers” that spells it out quite well – I love that title. Generally advanced analytics is revisiting traditional BI niche areas like Data Mining, Text Mining and Sentiment Analysis, various Algorithms, Languages etc. But also Data Visualization when using logic/mathematics to visualize advanced information like relations or new types of visualizations beyond the histogram. And maybe also stretching Advanced Analytics a bit further it could include areas like Rules Engines or Decision Management.
- Information Use Cases is the last and the odd one of the three, since it is not about technology – it is about harvesting the value of Big Data technology through business value. Without doubt this is the most important area to address to be successful with Big Data. People from Business and IT/BI must come together and consider how to leverage (Big) information as a business advantage in the business. Each Information Use Case then has to be supported by a (BI) Solution created around an Information Management strategy. At Platon we suggest our customers to host each and every of these information use cases in what we call an Information Centric Strategy.
Think if you could get immediate response on new products from your customers. No need for focus groups or interviews and slow successive reporting about the findings. No need for tedious and specialized customer satisfaction surveys. What if we could get access to this information in real-time? Would we engage differently with our customer? Should we change our sales channel or even our business model? What if I had access to the same information about my competitors’ products and their market initiatives?
These questions are rhetoric and should be important to must business people. At Platon we work primarily with businesses that treat information as a strategic asset, so this is important questions for our customers. As an example, there is a huge business potential in use cases around becoming better in understanding behavior in many scenarios, which is why Big Data is becoming increasingly interesting for to our customers.
Most people in BI today are just doing reporting and simple analytics. Big Data will be driven by business and BI together, since none of them will be able to succeed alone. BI needs to get the infrastructure in place to handle the four v’s and dust off the good old predictive books on the forecasting shelf and focus on advanced analytics. There is no doubt that Big Data is becoming the next version of BI (after DSS, MIS, DW, BI) and that is exactly why Big Data is redefining BI but also disruptively many industries as we know them today.