Improving Data Science Strategy at Neste

Winder.AI helped Neste develop their data science strategy to nudge their data scientists to produce more secure, more robust, production ready products. The results of this work were:

• A unified company-wide data science strategy
• Simplified product development - “just follow the process”
• More robust, more secure products
• Decreased to-market time

Our Client

Neste is an energy company that focuses on renewables. The efficiency and optimization savings that machine learning, artificial intelligence and data science can provide play a key role in their strategy.

Data Science Problems

Data science is a nascent industry, yet the discipline is growing at an explosive rate. When most data-based projects start there is a focus on delivering a product and value. But over time multiple projects evolve in different directions and lead to divergences in tooling, best practices and quality.

Typically the breaking point is an incident like an operational failure or a security breach. These events trigger a holistic review of a companies data applications and it becomes clear there is no coherent strategy: no unification, no best practices, no process, etc. The goal of this project was to:

• Provide a new strategy for data science ways-of-working
• Incorporate modern industry data science best practices
• Deliver a range of materials to help guide this process
• Further support the dissemination of these best practices

Winder.AI’s Industrial Data Science Best Practices

We developed, alongside the client, a new set of high level strategically important best practices that focussed on operational readiness, security, robustness and architectural guidelines. We delivered new documentation to help guide fledgling projects and advised upon where current projects could improve and migrate to the new ways of working. In full, we provided:

• A suite of strategic best practices to help guide Neste in the following areas: data science, deployment, engineering, operations, security, architecture, project management and governance.
• New “living” documentation that define these concepts, provide information and link to further reading.
• Processes to help deliver new projects
• Guidance and consultancy to current projects

Results

The processes we helped put in place now provide a clear yet simple methodology of developing new data science products. Stakeholders can be confident that the resulting products are safe and reliable. Developers are happy because they are leveraging new techniques and technology that help them build more robust products in a simpler way. They know exactly how well their project is doing because they have a clear list of what is expected from their products. In summary:

• New projects have a robust framework and set of best practices to follow
• Guarantees on model robustness, operability, monitoring and security
• Unification of tooling, whilst retaining flexibility
• Higher job satisfaction and less stress; developers know what is expected and can plan accordingly

You Can Do This Too

You already understand the benefit of optimising industrial process; ensuring you have an optimal data science strategy is just as important. If you want to improve the efficiency of your data scientists, need a new scalable platform or want to leverage state of the art models, get in touch.

You might also benefit from our free data science maturity assessment, which helps you prioritise improvements to your business’ data science workflows.