Many data teams pursue custom projects that make their environments increasingly complex over time. They can regain balance by consolidating processes into a managed semantic layer that handles data management while also presenting the business with consistent metrics for analytics. The managed semantic layer strikes a balance between standardization and customization, running current projects efficiently while supporting new projects flexibly. When designed and implemented well, a managed semantic layer enables companies to boost productivity and IT efficiency while reducing operational risk and assisting regulatory compliance. 

Data leaders and their teams should take the following actions to realize the benefits of a managed semantic layer:

> Find the pain. Consult business owners to find and understand the greatest sources of pain that a managed semantic layer can alleviate.

> Evaluate tools. Evaluate offerings using the criteria of automation, generative AI assistance, modularity, open architecture, and hybrid/multi-cloud support.

> Take modular steps. Implement projects in a modular fashion to complement rather than disrupt the rest of the business.

> Anticipate future needs. Define your business and technical requirements according to the desired future state rather than just current operations.

> Learn and teach. IT and business unit stakeholders must collaborate to document their successes and mistakes so the full cross-functional team can improve.