Digital transformation projects aim to fully overhaul entire business models so that every element of the business is truly data driven. To know where to start, we need to take a look at the current processes and find the most efficient way of digitising them.
Digital transformation is best thought of like building a factory, rather than building the end products the factory produces. We need to figure out which existing processes and infrastructure improve productivity and whether we also need new ones.
Then we need to prioritise how we develop these in a way that keeps the overall cost of the project from spiralling, that demonstrates the project to your business, and that will continue to deliver once the project is complete.
In this blog, we’ve laid out how we prioritise these features in a way that creates economies of scale.
At the start of the project, we’ll ask people from across the business to think about how they’ll use data, analytics and machine learning to improve their users’ experience, lower their costs, and accelerate their work.
We’ll then work with each area of the business to define and refine these use-cases into a set of actionable data-driven projects. At 6point6 we’ve created a capability architecture to use as a framework for digital transformation projects. This means we don’t just digitise your individual processes; we will build you a data platform.
An immediate benefit of this approach is that we can quickly estimate the value of each use-case, and the costs of building and running the solutions for each one. Once we know these estimates, we can calculate the total value of the project:
Enterprise-wide benefits = (total use-case benefits) – (total costs to build and run)
Creating a single investment portfolio is useful because the whole organisation can understand the size of the prize. It helps to show how the investment will result in improved revenue, reduced operating costs and reduced risk associated with manual processes.
We prioritise use-cases by plotting them onto a chart of value versus effort to focus the project on early wins. By doing this we identify the work that will be initially viable and showcase the value of the project best.
If there are no existing core data capabilities when we start the project, the estimated effort needed to deliver most of the use-cases is exaggerated.
Only a small number of use-cases fall in the viable top-right quadrant. They are typically ‘quick wins’ where most of the people, data and tools needed to deliver them are already in place.
It might seem cheaper to simply pick the most economically viable and visible use-cases and develop those in isolation. But the impact on the business won’t last and won’t result in true digital transformation. In fact, you’re likely to end up building duplicate capabilities that do the same thing in several areas of the business.
To avoid this, we need to identify those repeated processes and build capabilities that multiple use-cases can share.
We define a capability as a reusable skill, technique, process, standard, or code. Key capabilities tend to be repeated across different areas of the business and often come under the umbrella of “data services.” They are often composed of:
We’re experts at identifying missing capabilities and building them to reduce the overall cost of the project.
This approach does usually inflate the cost of developing the early use-cases. But, as the project goes on, the cost of building each use-case decreases.
Almost all the use-cases become viable as we develop data capabilities, which improve over time and can be reused as we work through the transformation.
To develop these capabilities in a way that creates economies of scale, we create a framework around which we can build the data platform. We do this by sectioning the work into three phases of development.
First, we develop visualisation capabilities to showcase the benefits of the digital transformation early on in the process. Then we develop observation capabilities. At this stage, the cost of developing individual use-cases begins to come down. Finally, we develop automation capabilities, which transforms your businesses into being truly data driven.
We will explore this process in more detail in part two of this blog.