Getting the mandate to integrate automation, or machine learning and AI at your firm can be quite daunting. How are you meant to implement a strategy to effectively drive change within your organization when there seems to be limitless potential but no starting line for where to begin.
Often executives given the task of managing big data with no tangible end goal. Harnessing big data can often present several challenges, beginning with the software and hardware that is required to handle and store all of it. Another hurdle that many encounter is not just the analysis of the data set, but the ability to adapt to the size of the data set.
Big data is not just a long series of data, but also a wide one. An example would be the size of a client database on a spreadsheet. Each client would have their own row, and if there are lots of clients this data set would be very long. However, in addition to each client receiving a row they would also have a column for each variable attributed to that client, ensuring that we can collect as much data on every client as possible, and this typically creates a very wide data set as well. It is in the presence of wide data sets that machine learning tools make the best use of data.
The most important thing to remember when utilizing machine learning is that a prediction model does not get confused with that of a causal model. In prediction problems, causality is not the priority, rather it is the ability to identify patterns and predict outcomes in a specific environment. One data model that works for a particular data set will not always work for another.
This post isn’t meant to warn the reader about the over reliance on the abilities of machines, however, to identify where human engagement and intuition is still needed to process and synthesize the information. The alternative is to rely purely on human judgement which can also bring bias and errors. The best managers are learning that by utilizing a blend of both approaches that they are able to make better decisions, while learning that there is always the possibility for a better way.