Over the last 12 to 18 months, more and more D&A leaders have been pivoting their focus to delivering value from data and AI. We are seeing traditional organisations adopting global revenue targets for value from data and, as a consequence, new organisational structures, roles and processes emerging. In a recent Data Leaders peer discussion, Data, Analytics and AI leaders shared their learnings as they evolve their operating models and frameworks to deliver measurable value from data.
Cost reduction use cases are easier to justify and engage on
For D&A leaders focused on gaining initial buy-in for data and AI use cases, look to corporate cost-reduction programmes and process-optimisation opportunities, especially on critical processes, easy-to-scale and/or easy-to-quantify (for example, in terms of FTEs) solutions.
“Cost reduction use cases are usually easier to justify and engage on early on. We started off being really focused on cost reduction in the early days. And it’s now pivoted, so about half is on cost reduction and half is on growth. But in the next year it will be more growth because a lot of the cost savings have now been realized.”
Deliver Proofs-of-Concept at pace
Use agile methodologies to showcase the possibilities of data and AI applications quickly:
“We use a framework called RPM: Rationalise, Prototype, Make. Each phase is timeboxed: a two-hour discovery and rationalisation workshop with the business to select the use case, five days to build the prototype, and two weeks to make a close-to-productised solution. Each phase ends with a go/no-go decision. At the end of the process, we request the funding to fully productise and scale the final solution. To date, we have a 100% successful funding rate.”
Embed KPI definition in the value assessment phase
A common point of failure is the non-commitment of business SMEs to dedicate the necessary time and resource during development, even when a value outcome has been identified. A proposed solution is to embed the definition of KPIs during the initial value assessment:
“To reduce the discrepancy between the value calculation and the value realisation we work with the business upfront on how to measure the agreed outcome. We define together the KPIs that will prove that the value has been created and we make sure that we have the commitment from the business side that they are going to measure it. And that’s something that we do at the very early stage because, without it, it will be a no-go for me.”
Make the ownership, status and results of use cases transparent to top management
An additional way to inspire accountability is through transparency to senior management and business leaders.
“We published our portfolio of use cases with the ownership and value attached so all entities were able to compare how many use cases they had, how many use cases in production, how many inputs, how many in industrialisation and the value associated with it. And when they started to compare, that was very interesting because they don’t like to be exposed to the company partner as the worst of the class.”
Define the adoption plan upfront
Adoption is the most critical component to fully realising a use case’s value. Adoption commonly fails due to unanticipated resistance to change or process-change requirements. Use specialists to define with the business the technical and process steps to fully scale and implement the use case.
“You have to think about the data, how you are going to collect it, how you are going to store it and so on. That’s what we have done by using data translators who work on daily basis with the business guys to help them think “Team Data” first. This is really helping to accelerate adoption.”