Data Analytics And Artificial Intelligence Driving Disruption

One of the most critical capabilities to responding to change and disruption in the marketplace is the ability to sense the change in a manner and, most importantly, timeframe that allows a response to be identified and executed. The frequency and discontinuous nature of change rocking the market requires a robust process that takes into account as many factors as possible to identify the change. This process then must describe factors and relationships to allow them to be analyzed to develop the response. The difficulty lies in the volume of data, both new and old, that must be taken into account to first identify the change and second to guide in determining a response. This is where data analytics practices utilizing artificial intelligence comes into the equation to support the business.

The two challenges; data for analytics and the actual analytics require a thoughtful strategy and approach that will allow you to sense the demands from the market in both a manner and a timeframe that will meet the velocity of change requirements. These challenges go hand-in-hand as two sides of the same coin; you cannot sense the change without a great deal of data to analyze and you cannot analyze the amount of data without the artificial intelligence to process large amounts of data.

From the data perspective it is very fortunate that the big data tools and storage technology has advanced to the stage were the collection and maintenance of the data is no longer an issue. This allows the collection of vast amounts of data available from all points of the supply chain and especially from the eCommerce channels. This data along the supply chain can now easily be captured for detailed analysis and because of the volume of data available the results of the analytics can be more accurate and more informative of trends. The important point here is the collection of data from the viewpoint that the more data the better because you never know where the analysis will take you and in order to come to a conclusion you must have the data to analyze and also prove the concept.

Ten years ago, in the early growth stages of big data, the challenge was the ability to collect and store the amounts of data for analysis and the analytic query tools to quickly perform the analysis. This required careful review of the data available to select the appropriate elements that you believed were necessary to produce the analytic results. Then the data collection required over night collection and the analytics were run to produce large reports in a daily schedule. Everything took time and you had to be careful to analyze the expected outcome. Now though because of the dramatic improvements in the technology the process and results are much more robust and immediate. Now there is no concern about the amount of data and the queries themselves are also much more interactive.

The analytics of the data presents the challenge in this equation and this is where the focus should be placed now. Artificial intelligence tools really come into play from this perspective to provide a value add to the equation. Artificial intelligence and machine learning will be a baseline requirement to allow the market to sense and then determine how to respond to the changing demands. These tools will are necessary to first sense and understand the demands of the market and then these same tools will provide the means to analyze the potential solutions and even forecast the impact to the market of change.

The key benefit of these tools is the speed of analytical results and then combine that with the delivery speed of a solution. Speed of sensing the demand and then speed of response are the objectives that must be front and center for the market and the participants in the market. All indicators based on technology lead to increased speed of demands and resulting disruption in the market and market participants will not have the time required for any manual analysis of these trends. In addition, only failure will come from participants that wait for the market to deliver a solution that they can adopt.

The participants that embrace artificial intelligence combined with machine learning will be the players that succeed and prosper in the market. The rate of failure that we have seen in the market will only increase as the velocity of changing demands increase. The good news is the building block tools to quickly and efficiently sense and respond to the increased velocity of changing demands are already available. The bad news is that the market participants must embrace the tools to develop their own practices to use the tools.