Imagine this scenario – you have just “clicked” on an item that you are ordering online. What kind of “data trail” have you generated? Well, you are sure to have generated a transaction – the kind of “business event” that goes into a seller’s accounting system, and then on to their data warehouse for subsequent sales analysis. This was pretty much your entire data trail until just a few years ago.
In recent times, the whole notion of data trails has exploded. The first wave of new data entering your data trail consisted of web and mobile interactions – those dozens or hundreds (or even thousands) of “human events” – research clicks and social media postings that you execute leading up to and after an online order. It turns out that these human interactions, when blended with business transactions, are critical to yielding more insight about behavior.
And now we are entering the next wave of new data – the observations made by the ever-increasing number of intelligent sensors that record every “machine event.” In our example above, for each human interaction supporting your online order, there may be hundreds or thousands of software, network, location and device metrics being gathered and added to your data trail. Further integrating and correlating these machine observations into your particular flow of business transactions and human interactions would enable game-changing advanced analytic capabilities – promising a “closed-loop” of ever more timely and accurate decisions.
The bottom line is that we find ourselves in a hybrid data landscape of such stunning heterogeneity that it forever changes both the challenges and the opportunities around the capture and analysis of relevant operational data – the business, human and machine events that make up your data trail. The ability to manage, integrate and analyze all these hybrid data events at price/performant scale – to build the necessary data-to-decision pipelines – becomes the key to modern data infrastructure and succeeding with modern analytics.