The proof is in the delivery - a JC Chapman "Data Vision to Execution" case study

Our client was tasked with championing data in a financial institution of just over 1,000 people focused on a dozen lines of business. Data was scattered throughout the organisation and siloed for the needs of each unit. Staff tended to grab data reactively to provide quick fixes, but neither explicitly associated a value with it, nor perceived a need for change.

As our client began their significant data transformation project they met with hard-line resistance. Individuals refused to participate and vocally opposed the initiative, unwilling to let go of spreadsheets they were used to owning and controlling.

The client needed to sell the value proposition of data, bring the business onside in the shift towards a data-driven culture and quickly demonstrate measurable benefits. A vision for data was needed.
 

FROM OPPOSITION TO ACTIVE SUPPORT

Our 'Vision-to-Execution' methodology provided the structure needed. Users had expressed frustration at spending days scrubbing bad data, but opposition turned to active support for the data programme once they understood how the proposed new approach would save 60 percent of their time. With that time they could focus on work they enjoyed and quickly reap the benefits demonstrated.

With clearly articulated, measurable objectives, users could associate business value with the data programme as it progressed. Particularly early wins like time-savings and improved capital calculations. The benefits were so clear that many became impatient for the changes to be implemented.
 

'RIGHT DATA IN THE RIGHT PLACE AT THE RIGHT TIME'

The 'Vision-to-Execution' process built a sales message through a vision of why data is important to the company. It tied the value proposition of data to the future vision for the business, which was 'to promote the good of its customers by providing them with monetary and financial stability'.

This was complemented by a strong 'vision for data' which began by simply stating that the business should be 'a data-driven organisation'.  By setting a goal to 'keep customers fully informed', the data vision linked to the aforementioned aim to promote the good of its  customers.

Similarly, a need to provide stability through objective decision-making was expressed through a vision 'to strive for all staff to be empirically empowered to make the right decisions through data-led analysis'.

How these 'visions' would be achieved was summed-up by the concluding statement of needing the 'Right Data in the Right Place at the Right Time’, a succinct strap line used to disseminate the new vision throughout the organisation.
 

Drivers, Principles, Goals and Implications

Engaging stakeholders and bringing them onside was critical. This was done by asking them to identify business drivers affecting data, and involving them in crafting long-term data principles that would shape the organisation’s data culture.

The business’ objectives were one internal source of these drivers, which included 'providing stakeholders with safety and soundness' and 'helping SME customers remain competitive'. External drivers included 'the need to reduce regulatory risk', as may be expected for the banking industry.  

Though guiding principles may include ubiquitous statements like 'data is a valued asset', the specific vision to empirically inform staff drove principles to ensure data was 'well defined', 'fit for purpose', 'shared' and 'in an adaptable environment'.

How drivers and principles would be addressed was first articulated by setting goals for the drivers and understanding the implications of the principles. For example, the driver of 'reducing regulatory risk' drove the goals of 'ensuring capital allocations are correct and efficient' and 'meeting regulatory demands even in times of stress'. Similarly, the principle of data 'fit for purpose' led to an implication of ensuring data is captured correctly at source.

These goals and implications were in turn used to demonstrate to stakeholders how success would be achieved through measurable objectives and a clear strategy. Together, the goal of 'correct capital allocations' and implication of 'correct source data' generated the objective of improving RWA calculations, measured by a reduced capital allocation through the strategy of capturing correct customer data at source for better risk classifications.
 

Actions and Results!

Actions to be taken were explained via governance and operating models and a clearly defined roadmap, with priorities identified by use of a gap analysis and focused on early wins. The principles and implications were, and often are, key determinants of the governance and operating models.

The progress made by following the 'Vision-to Execution' methodology saw stakeholders buy into the need for change, switching from vocal opponents to impatient advocates wanting immediate implementation. This was furthered when early wins were demonstrated through significant improvements in the capture of customer data that sharpened risk classifications and drove more accurate capital calculations.