We all know that data is the new oil, but which organization has really extracted it, refined it, fueled an engine with it, and leveraged it to reach a new level of operational excellence?
The implementation of a frictionless process doesn’t end when it has been augmented or fully automated. Organizations need the ability to control, understand, and analyze the execution of each process in real time in order to conduct an ongoing program of continuous improvement. To do this, data must be collected and analyzed to detect malfunctions, inefficiencies, errors, improvements, unplanned ways of consuming the process, and fraud, or to link process execution to business and financial data. This is simply about applying continuous improvement.
Leveraging AI is essential to detecting a problem as soon as it arises, especially to avoid multiplying the errors and their consequences when using microservices on a huge scale. Analyzing data requires smart analytics to correlate and identify the hot spots that need to be corrected, stopped, improved, redesigned, or raised to the management for a strategic decision.
Of course, access to data has to be easy, efficient, and well documented, especially in terms of data quality aspects. The ability to effectively exploit data lakes and data warehouses is as important as the quality of the employees that analyze the data. And the use of touchless processes, microservices, and robots doesn’t prevent feeding these data sources with fresh and quality data.
To deliver an optimal user experience, organizations need to consider the psychology of interaction and monitor digital activity using data and analytics to detect gaps in the solution. Let’s not forget that the user experience is no longer limited to windows and a mouse – touch screens, haptics, and conversational interfaces provide distinct means of interaction that must be designed to optimally support the user. Another complementary approach to detecting non-optimal interactions is leveraging digital surveys and interviews with users to provide rich insights that can be correlated with generated data, monitored KPIs, and business activity in order to identify improvements.
Ultimately, enterprises should not only look at data knowing from the beginning what they’re looking for. The huge increase in data vendors (both AI, data providers, vertical solutions) creates new opportunities. Why not ask data scientists to look at data for what it is, to cross it with external sources, leverage algorithms and AI to find what couldn’t be found by starting from the conclusion? Data should enable organizations to find an exit to the maze that was not supposed to be there, and that could really deliver the change they need.