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A leading Energy Sector client approached Optima to address challenges within their data estate. The major pain-points were key stakeholders not receiving timely and quality data insights to drive customer outcomes in key business capabilities and customer experience.  Pressure on the data team had built to such an extent that trust with the business had been eroded and a major Databricks transformation was stalling. 

Our client engaged Optima to partner them in lifting the quality and speed of delivery in building key data products; to playback key insights on improvements required within the data estate and personnel gaps; and finally injecting industry standard quality assurance and data management principles into ways of working.  


The overarching goal of migrating to the Databricks platform was to unlock the latent value in the Energy provider’s data estate and to empower the business to self-serve data needs, through the ML products Databricks unlocks. This would in turn reduce the tactical overheads for the data team and enabled them to focus on their own strategic roadmap. In the short term this meant rapidly accelerating the Databricks transformation and taking a data product led approach to the migration to unlock critical business use cases.  

We focused on each investment effort deriving a tangible return on investment; providing regular retrospective feedback on leadership insights; and improved ways of working for long term improvement. The client has now delivered significant data capability “wins” across the business lines they support, having built the foundational capability, and is now progressing their strategic priorities. 


To achieve this, Optima’s team of Data engineers, data scientists, BI specialists and data product owners worked shoulder to shoulder with client teams to implement a 5-stage transformation process: 

  1. Client Focus and Definition of Success 
  • In our collaboration with the client, we worked closely to achieve small but impactful wins that improved overall delivery. Additionally, we provided extra leadership support to the Head of Data and addressed any gaps in the way things were done and the skills needed. 
  1. Program Discovery 
  • We took two weeks to really understand the energy company’s situation. During this time, we checked if the solutions we proposed were practical and fixed any basic problems in their data systems before making any big changes. 
  1. First Wave of Impact 
  • Our efforts were concentrated on the three most important areas for the energy company. We set up teams with people from different areas to work together smoothly. Over eight weeks, we closely tracked our progress and successfully showed the company a preview of tools like an Energy forecasting tool and a Debt dashboard
  1. Expansion and Ways of Working Change 
  • We organised our teams more formally and made sure we had all the key roles filled. We also introduced better ways of managing projects to make sure data could be moved around efficiently. Our involvement with the company grew, reaching other teams like Commercial and Finance. We also planned out important steps for future work. 
  1. Pivot to Steady State 
  • We improved our relationships with the business side of the energy company and achieved important goals to ease the pressure on data teams. We also planned out the next steps for more successes. To keep everything on track, we put in place clear leadership roles and a structured way of keeping an eye on things. 


  • We delivered the migration of critical data to Databricks, enabling the development of new data product capability. 
  • We enhanced and repaired relationships with the business, building trust in both the data quality and processes that surrounded critical products (such as customer propensity models and critical MI). 
  • We reduced the burden of effort being placed on key SMEs within the business, introducing new ways of working to share the load of Quality Assurance within the engineering team. 
  • We established the “first foundations” of the data estate that create scalable table stakes to build cutting-edge data products and ML capability. 
  • We identified key person dependencies and resourcing shortfalls, temporarily plugging urgent gaps and upskilling key personnel to own and manage their respective estates. 
  • The business now has significant “self-serve” capability to manage their data needs, enabling the data team to focus on actioning their strategic roadmap and key capability investments. 



  • An underperforming and demoralised data team affecting business efficiency. 
  • A faltering data migration impacting the BAU service capabilities 
  • Key skills gaps in capabilities and effective roles and responsibilities 


  • A high-performance data team working on a cutting-edge data estate 
  • A tranche of critical first wins to ease the pressure of “urgent and important” wins 
  • A clear capability investment roadmap that has sufficient team capacity baked in to deliver it 
  • Teams reinforced by the knowledge of industry best practices and critical role gaps addressed