We define a business process as:

A sequence of interdependent procedures which, at every stage, consume one or more resources to convert inputs (data), into outputs (information).

No matter how complex, a process (or sub-process within a larger process) can be summarised by a simple 3 step methodology:

  • 1. Data Input

    Data is received

  • 2. Data Manipulations

    Logic rules are applied against the input data.

  • 3. Data Output

    The output is packaged in the format required

Even the execution of the most ambitious, strategic, global level goals delivered from a firms CEO, manifest themselves in the execution of a collection of individual processes (hundreds of thousands, even millions) which share the 3 step methodology described above.

Years ago these processes were people centric, as people’s roles were based around creating, understanding, and processing huge volumes of paper work. In 2018 the service firms offer their customers is broadly the same, but the nature of the work, and by extension, the processes that define them, are unrecognisable thanks to advances in technology, and the extensive use of data.

When you add the ‘3 V’s of big data’ (volume, velocity, and variety) into the mix, it is easy to see how even simple processes can become complex, very quickly.


Every industry, company within an industry, and team within a company, has a different view and expectation of what automation is. Along with this wider context, we must then consider that not all processes are created equal. Simple processes can be made complex, and complex processes can be made simple.

It is also because automation itself is subjective, and so the scale of automation for any given process falls along a spectrum, from full end-to-end automation solutions, to partial solutions that automate some (for example, the most time intensive, or intricate) sections, of a larger, overall process.

With that in mind, we define automation as:

The elimination of user interaction when collecting, manipulating, and formatting data to produce meaningful information.

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