Creating short fast Proof of Value models



Published: 25/05/2018

Bringing Data Science capabilities to the fore in a way that that provides real and measurable value to the organisations we engage with is one of our founding principles, and this piece of work highlighted the benefits of a unified approach.  

Our client in this case was a distribution network who presented with a need to improve their processes around predictive maintenance, vegetation management, inventory management and street light analytics.

The client had identified a backlog of scenarios and were seeking to prove the value across these instances. This would ultimately drive future investment in tools and the capability to gain new insights.  Initially they had had ‘pockets’ of analytics occurring in the business, but no central capability to prove value across a number of these scenarios.

Our main goal was to bring our utilities domain-based data science and architecture experience to the client to enable them to move quickly through the backlog of scenarios over a short eight-week period.  To do this we used an agile fail-fast methodology to quickly identify gaps and issues and move forward to another scenario. Our previous experience also enabled us to position solutions and recommendations into their existing platform/capability from any proven scenarios, enabling them to understand how they could implement these learnings into their existing SAP HANA stack.

We aimed to: 

 
We approached this engagement by working closely with the innovation team and business SME’s to source data and understand the challenges they wanted to address. Our onsite team consisted of a Lead Data Scientist, who was supported by our Solution Architect to provide implementation and future state recommendations. Alongside this, the client provided access to an analyst and business SME resources.

The final deliverables on the project were:

 
During this engagement we were able to bring our extensive utilities domain knowledge in combination with our data science and architecture experience, to quickly and effectively identify gaps, issues and measurable value held in each POV without wasting time and resources