Driving Value Delivery Model - Business Value | People | Process | Data | Technology

To be a data strategy company focused on enterprise transformation means that we have to embrace a broader role with the client organization. Many a times, this transformation may be within a certain business unit of the enterprise, and at other times, this could mean a broader mandate, even the entire organization. This kind of an enterprise transformation consulting assignment represents a different kind of challenge. S4 methodology gives us the framework and thinking tools that can be used to drive business outcomes.





While data strategy can seemingly be only about the data assets, in practice, an effective data strategy is one that incorporates an understanding of all the value drivers for the enterprise. Data needs to be treated as an asset at the business level and be used and invested in like an asset. Not every company will have that much of data to start with. Nevertheless, they should look to develop and deploy data, triggering a virtuous cycle of value creation. Just like capital, if data is not used, it results in wastage and is most likely detrimental to the business. This is where STUDY comes in. Study is the process of arriving at an exhaustive understanding of the value drivers for an enterprise. Most enterprises already “know” their value drivers but from a traditional sense. We end up complimenting that knowledge by focusing on the role of data in influencing each value driver.

The traditional value drivers are usually the business units it has and their business model including operating conditions, socio economic conditions, competition etc., the people and processes contributing to and running the business, and the technologies enabling the business. The traditional value drivers and the data assets need to be considered together to begin to define the data strategy. At this stage it is crucial to enable a method to SCORE data value creation. Score is a process of benchmarking the enterprise against competition in terms of maturity of usage of data in each of their value drivers. Often times we find that enterprises happen to organically grow and focus on different value drivers at different points in time based on its then leadership and operating climate. On the other hand, a mature data-driven organisation shows up quite clearly in their use of data for all value drivers, which have all thus been matured to a similar extent. This scoring process becomes a pretty accurate representation of where the enterprise is in its bigger journey, and what it needs to do next to get there faster.

The beauty about becoming data-driven is that enterprises can approach this in as agile a manner as they can muster. The faster their iterations are, the faster is the course correction, the faster is the value creation and the further along in the journey of being data-driven. This agility and constant innovation is required to continue building the business. Ironically, in this digital era of disruption, such agility is required to even sustain revenues. To paraphrase the Red Queen, “the faster you run, the more you stay in the same place”. In the SUGGEST stage we work to advance an enterprise data journey. The Suggest step provides concrete recommendations for undertaking transformation of the value drivers so as to achieve the desired state of being data driven. We dig into the value drivers that the Scoring process finds out as being immature and then incorporate the knowledge from our investigations into the overall transformation roadmap. This roadmap forms the deliverable from this Suggest step.

SOLVE is not a stage where the data value creation cycle ends. The cycle ends when the enterprise is satisfied with the outcomes. This is best judged by the enterprise in the context of the current state at that point in the future. The challenges when an enterprise has become data-driven are of a different nature, and the enterprise would see that quite plainly when they come to that point. Everything that is suggested is solved for by implementing the recommendations using technologies, often newly chosen for the tasks proscribed with processes that set the stage for an active data culture with the help of leadership that is sympathetic to the cause of being data-driven. Typically, we do the first iterations of Solve ourselves to prove that the transformation can be feasibly undertaken with immediate ROI. Once enough conviction builds up, the enterprise starts to take on the iterations of transformation themselves. At that point, we start to take a more strategic view and continue to play an oversight role.


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