A funny thing happens when you come up with a label for a particular concept. Suddenly something that had perhaps a fuzzy meaning or couldn’t easily be thought or talked about before becomes a “thing” which allows us think about it, discuss it, even build businesses around it. But the very act of labeling can also obscure and drive thinking along a particular path that may lead us into dead ends and prevent us from truly grasping the significance of the concept or its wider context.
I would argue, this has happened to the terms “Big Data” and “Internet of Things (IoT)” and the consequence is that we’re looking for Big Data and Internet of Things use cases, and as a result flailing about, expending energy, flapping our wings, but in many cases getting nowhere. We may by now be convinced that we have to do something with Big Data and IoT and that it holds much promise. We may even be scared to miss the boat and that our competitors are further along, and that if we don’t start now we will fall further and further behind.
I was talking to a customer the other day, and the conversation started off almost typical: we know we need to do something around Big Data, we have access to large datasets, so what architecture do we need? My response was, that it depends, what is it that you are you planning to do? “Well”, was the reply, “we plan on putting together a data lake so that the business can ask any questions they would like”. That is, the approach to Big Data was entirely technical, and no real thought had yet been put into why or for what reason we might make the investment for a Big Data landscape in the first place. The almost inevitable result is failure, and articles like this and this.
And to be honest, this is an easy trap to fall into. I myself have been looking for Big Data and IoT use cases, and trying to think where best to apply its techniques, and found myself struggling. I started to think about “Smart Data” instead of Big Data, to at least reflect that Big Data requires advanced/predictive analytics to be truly useful, and that helped widening the scope a bit, but in the end didn’t really solve the problem: because I was still thinking in terms of the technology, rather than where the technology should be applied.
“Big Data” tells us we’re going to analyze large data sets. We might use “unstructured data” (even though the definition of unstructured data runs from CSV files all the way to images, video and audio). We find ourselves falling back on social media feeds as an obvious default, regardless whether it may or may not be appropriate to our use case or business. We may even argue endlessly whether there are 3, 4 or 5 “V”s, as if that is somehow important or relevant to the problems we’re trying to solve.
“IoT” tells us we’re going to use internet connected devices. We’ll use the measurement of sensors to understand and predict…. something. But what? The German term “Industrie 4.0” is a term I like a little better, as at least it has a bit more of a focus towards corporations, in particular manufacturing. But even there it seems the concept drives the discussion, rather than how it enables us to run better.
These concepts, therefore, are mental traps. They force us to start with the technology, rather than where we might apply the technology in the most optimal way. It’s the classical “solution in search of a problem”. Whether we start with the concept, or we start with the data set (“I have this fantastic large data set, what wonderful things is it able to tell me?”), we are simply starting from the wrong place. And because Big Data and IoT are still a bit fuzzy and undefined, and we lack focus, anything becomes possible, and we’re likely to overshoot and be overambitious if we do identify a “Big Data” or “IoT” use case, and fail to appreciate what we may already have.
I propose, therefore, that we think of it in a different way: What if we simply thought of it as an opportunity to optimize business processes? That is, we pick something that we already do inside the business, and use new techniques to improve them, optimize them. That leads us to Manufacturing Optimization, Supply Chain Optimization, Human Resource- or Human Capital Optimization, Customer Relationship Management Optimization, or more generically, Business Process Optimization.
This is not particularly profound. It is really just a mental switch you make to think about the problem in a different way. Rather than looking for use cases for IoT and Big Data, we’re looking for use cases to optimize an existing business process. That provides us with immediate focus, first by shrinking the domain space (I like parameters and constraints; they limit the field of investigation immediately to something much smaller and more concrete), and then by focusing our search for datasets and techniques that can assist us in our goals. If we’re trying to improve a manufacturing or supply chain business process, for instance, we probably don’t need a Twitter or Facebook feed, but might look for particular sensors or location-aware devices instead.
It also forces us to re-appreciate the data we already have, and whether we’re leveraging it appropriately. In Customer Relationship Management, for instance, corporate email is still often an underutilized resource, while it can just through metadata analysis already provide a list of everyone in the company who touched a particular customer. And of course, there is a wealth of information in our ERP systems and data warehouses (BW or otherwise) that could tell us a lot more than weblogs or mobile device location data ever could. Such data, for instance historical weather reports in Retail, can certainly be used to enrich existing transactional data, but such data by itself is largely going to be meaningless or at least not as effective as when combined with data we already have.
Once we know what we want to optimize, and have identified the data sources required for it, the definition of the solution is going to be much easier, much more concrete, and with a much clearer justification for investment and ROI calculation. And since S/4HANA allows us to customize and add to HTML5 front-end applications, we can bring the results of our solution directly into those applications where it is most clearly needed and provides the most business benefit.
In conclusion, then, forget about Big Data and IoT, not because these are not important techniques. They are, and their promise and potential is enormous. But let’s think of optimization, focus ourselves on the business problem we’re trying to solve, and only then figure out how to apply Big Data and IoT techniques to achieve that goal.
If you liked this, you may also like my 4-part series on The Future Analytics
