The client is at the forefront of its industry and has a real grasp and hunger for embracing new technologies that enable them to solve traditional problems. From an analytical perspective, they foresee huge value from their ability to converge data from multiple different sources. Particularly in supply chain where the need to combine internal data with external data has become vital.
As the project progressed, the framework we developed began to increasingly appear like ripples expanding across the water when an object is dropped into it (in our case, it was the waves of information that were created under certain circumstances and conditions).
Examples of the ripple effect can be found in economics where an individual's reduction in spending reduces the incomes of others and their general ability to spend which goes on to impact many other areas of the economy.
In the case of analytics, we have found that analytics implemented correctly can have the same effect where the growth of information and analytics in one area of an enterprise can positively affect adjacent areas in the business, some of which may not have been directly related in the initial interaction. Mapping the impact of analytics in an enterprise is what we call 'chaining'.
Our framework helps clients think through each phase (1 to 4) and iteration that creates value beyond its initial perceived boundaries in what looks like a ripple effect.
Each blue circular line defines the “edge” of value for a given set of analytical characteristics and conditions. Establishing and or understanding the 'edges' for each phase will materially assist in a client’s analytical expectation and transformation, whilst putting in place structure to the journey.
The framework has six vectors that define each of the outer edges of each phase (or ripple).
Below is a basic framework for a procurement and supply chain transformation journey: Each phase is a ripple with an edge.
In summary, this framework is not a silver bullet but it is aimed at helping teams navigate through some of the non-trivial complexities that being more data driven and analytical presents.
We will continue to develop this framework but please feel free to use it and send in any suggested modifications we could all benefit from.