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Data is the critical investment for a profitable return in robotics

Conversations about digital transformation often start with lofty goals about wanting to "improve productivity" and "reshape the way we work." In more recent times, this conversation has gained urgency as companies look to the expected benefits of robotic process automation (RPA), machine learning and artificial intelligence (AI) for significant yield on their investments.

These are all very important objectives and successful companies are realizing significant benefits when these technologies are able to be implemented well. McKinsey & Company cite case studies which show a return on RPA implementation of between 30% and 200% in the first year. It's a hot topic with a growing market and some terrific, innovative vendors having "grabbed the tiger by the tail" and enjoying strong growth. Clearly these technologies must be on your technology agenda.

Realizing these ambitions is not necessarily as easy buying a robot and throwing data and transactions at it. Like most things in IT, it takes some proper planning to develop a clear set of expectations of what this technology is going to deliver; to work out what success will look like.

A big part of this planning is going to need to be a detailed and pragmatic conversation about the data in your business. While managing the status quo, it is easy to pay little attention to the state and nature of the data in your business. Much of the data is created, managed and used in the context of individual systems, with business processes built around them. Of course, we transfer data between these systems through APIs, or file transfers or services or whatever, but it is still a lot about the data, inside a system, comfortably cloistered and happily doing its thing.

Once you start working on digital transformation, and then RPA, machine learning and AI, data starts to take a different role. Data goes from being the input to / output from a process to being one of the core building blocks of your enterprise. You will need to evolve the way you think about the data in your business.

In a digital enterprise, data needs to have a few key attributes:

Wherever possible it should be stored once - a "Single Source of Truth." This SSoT needs to be available to every system or process that needs the data.

Your SSoT MUST be "golden." As you are only going to have this as the single source of that data, you must make sure that it is accurate and robust.

It must come from the best root process for that data, and it should be a natural byproduct of that process. For example, get your employee data from the HR system, it is maintained by processes which manage the real world 'stuff' about people in your company; who joins, who leaves, who changes roles, who gets a new phone number and so on. Don't rely on data which need manual update or comes from somewhere which isn't "natural."

It must be based on standards, which are clear and well understood. By making sure that the definition of what goes into your data sets is well defined, documented and managed properly, you will ensure that it is more easily able to be leveraged across your business. Where possible, align to industry or international standards to improve opportunities to integrate to key partners and customers.



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