Determined to overcome these obstacles, a new data science team took matters into their own hands and built a data lake on the Google Cloud Platform. Using lean engineering principles, the group quickly set up the data lake and an analytics sandbox and started bringing AI-enabled experiences to market. However, the challenge then became managing the DevOps process, as the data science team pondered whether they should also support the DevOps process.

As the business saw rapid results from its efforts, the question shifted to how the rest of the organization could tap into this data lake and whether it should become the enterprise data lake. That's where Productive Edge came in, working with the business intelligence IT team to craft an enterprise roadmap for the adoption of the data lake. The team had to consider multiple areas, including redefining the mission of the BI team, creating an iterative roadmap aligned with lighthouse use cases, redefining data governance policies, and putting in place data integration and curation best practices.

With the help of Productive Edge, the specialty retailer was able to establish a data governance framework that focused on providing reliable data and opening up access, while still meeting regulatory and compliance requirements. A master data strategy and approach were identified, and an operating model was put in place to allow IT to take ownership of MLOps and DataOps, freeing up the data science team to focus on the analytics tasks at hand. The journey towards a modern data platform had begun, and its iterative nature was already having a rapid and calculated impact on the organization's culture and ability to harness data to drive differentiation.