Thinking About ERP in an Ever-changing World of Data

While this sort of data will be much easier to integrate into ERP environments due to inherent structure and the likelihood that it already will be product-or process-related, the sheer volume cannot be ignored.
  Changing Environment: Risk and Opportunity

So far, the changes described relate to the basic “Vs” of Big Data (volume, variety, velocity of change, etc.), but there are also very important changes taking place in the environment. Many of these changes relate to regulation around the discovery and curation of data. For example, privacy laws (which typically focus on personal information), data sovereignty regulations (which focus on where data may be created and stored), and data governance regulation (which focuses on how information must be maintained) continue to emerge globally. Many of these laws require organizations to rethink much of their infrastructure and process. In ERP-enabled organizations, especially those with cross-border process through either single-or multiple-instance strategies, all of these regulatory changes must be carefully considered.

The only certainty about the changing face of data is that it will continue to change. How decision makers in organizations react will dictate whether they fall into a posture of defending the environment against all of the change surrounding it or a posture of taking full advantage of the new opportunities.

Leading Change: Where to Focus

A very powerful frame for considering change in an organization considers four factors at the outset: people, process, technology, and mindset.

From a people perspective, we must consider that the skills that have made us successful so far are necessary, but not sufficient. Ongoing focus on new skills, such as working with unstructured data, using non-regressive methods (e.g. AI or heuristics) to derive value from never-before-seen data is crucial. Equally important is the ability to recognize new types of information that are becoming available to the enterprise, thereby avoiding silos of information that otherwise might be used only for a single purpose.

ERP is all about process, but how must that process be re-envisioned in the context of the continuous and unprecedented data-related change? Clearly, processes must become more learning-based, and to some extent more self-correcting. For example, while closed-loop manufacturing processes have been around for decades, we now have the ability to augment these processes with signal analysis, intrusion, and anomaly detection based on far more advanced capabilities that can learn from the ongoing operation. Such approaches are being integrated into new technologies, such as autonomous self-driving vehicles and drones. Integrating the same sort of intelligence into more traditional processes, especially in light of the far richer troves of available data, could provide dramatic benefits to the ERP-enabled organization.

From a technology standpoint, while the IoT is an obvious focus for ERP-enabled organizations, it is not the only area where the pace of technology change gives rise to both risk and opportunity. Of particular focus lately are the many forms of AI, including deep learning and cognitive approaches to problem formulation. As organizations put focus on these emerging fields, we will certainly see both use and misuse of technology.

Finally, we should consider mindset. How are we reacting as leaders in an increasingly data-driven enterprise? Einstein is often quoted as saying that we cannot solve a problem with the same mindset we had when we created that problem. The shift from MRP to ERP was about connecting everything. It seems we are still on that journey, but now there is so much more to connect and amazing potential if we get it right.