Your maturity result: BeginnerIntermediateAdvanced

Beginner

Your score means your organization’s data strategy maturity is only at the beginning stage.

• At this stage, you may find that your organization’s strategy thus far has produced a proliferation of tech tool adoption in order to meet the immediate needs of your organization or business.
• Your data science team is likely centralized under one management function without direct access and influence on your different lines of business.
• The steps of your data lifecycle require a large number of tools to complete, and there is functionality overlap across tools.
• Your data science team is either struggling to create machine learning models or those models are not generating the results you would like to see.

How should you change your approach?

• In order to move to a more intermediate level of maturity, rethink your approach to your data science team structure. A more decentralized or hybrid approach to team structure will increase the influence of your data on your business. Increase employee-related metrics, monitoring to identify gaps in your structure or technology infrastructure.
• Cease the adoption of point solutions to meet immediate business needs. Instead, consider consolidating your data lifecycle steps by reducing the number of tools needed to complete these steps. Focus on finding a tool that allows for things like ingesting structured and unstructured data, managing, and storing data all in a single platform. This could mean rather than relying on your data lake or warehouse, you could move toward a data lakehouse model.
• Invest in using native connectors for integration. APIs are great, but if your platform schema changes, that can increase manual API updates for your data practitioners.
• Move toward optimizing your machine learning practices, rather than addressing each issue on a use case by use case basis. Update your infrastructure with machine learning optimization in mind.

Intermediate

Your score means your organization’s data strategy maturity is at the intermediate stage.

• At this stage, you may find that, while your organization’s strategy has begun to pivot toward the reduction of tools, your decision-makers are still focused on meeting the immediate needs of your organization or business when it comes to data tech adoption.
• Your data science team is likely centralized or decentralized under one management function, though your company is beginning to prioritize the move toward a hybrid structure. A decentralized structure or distributed management across multiple teams without a centralized management structure creates duplication of work and data mismanagement, as well as a lack of awareness of data infrastructure maturity across data teams.
• The steps of your data lifecycle still require a plethora of tools to complete, and there is functionality overlap across tools.
• Your data science team can now create machine learning models, but either have not socialized them across the business to increase collaboration or don’t have the monitoring and observability protocols in place to understand their full potential for effectiveness.

How should you change your approach?

• In order to move to a more advanced level of maturity, continue to move your data science team towards a hybridized structure, including creation of a data center of excellence. Having a hybrid approach to team structure will increase influence of your data on your various lines of business, while providing a standard of excellence for all data teams. Also, continue to increase employee-related metrics, monitoring to identify gaps in your structure or technology infrastructure.
• You have already ceased continuous adoption of point solutions to address immediate needs. Now, consolidate your data lifecycle steps by reducing the number of tools needed to complete these steps. Find a tool that allows for things like ingesting structured and unstructured data and managing and storing data all in a single platform. This could mean rather than relying on your organization’s data lake or warehouse, you could move toward an end-to-end data lakehouse model.
• Now that you have a larger strategy to optimize machine learning, begin to adopt MLOps and mature AI governance practices.

Advanced

Congratulations, your score means that your organization’s data strategy is advanced!

• At this stage, you may find that you have progressed beyond many of the pitfalls of advancing your organization’s data management environment.
• Your data science team is likely hybridized, functioning across lines of business while reporting under one management function. This approach increases awareness of data maturity across teams, as well as productivity and collaboration.
• The steps of your data lifecycle require fewer tools to complete all the of the steps in your organization’s data lifecycle. Most of them may even be consolidated into one end-to-end data lakehouse.
• Your data science team can not only create machine learning models, but also has begun to implement them across the lines of business, increasing cross-team collaboration.

How should you change your approach?

• If you haven’t already, now is the time to create a data center of excellence to manage the maturity of the data environment throughout your company. Multiple teams operating in one data lakehouse and reporting into one central structure will not only continue to increase data availability, but also observability.
• You have already ceased continuous adoption of point solutions to address immediate needs and consolidated the majority of your data functions into one end-to-end lakehouse platform. You should now sunset any remaining redundant legacy technology or systems and move toward a benchmark of 70% to 80% tool consolidation.
• Now that your leadership and teams have adopted MLOps and mature AI governance practices, you can now use these insights to continue to level up your data practice. As the market evolves to focus more on ESG standards and sustainability requirements, consider how a further reduction of your organization’s cloud footprint and careful monitoring of MLOps and AI insights can reduce your company’s footprint to keep you in good standing in your industry.