Phase Three: Valuable
Organizations that have reached this stage of data maturity are truly data-centric and take a data-as-a-product approach. In other words, they are thinking of and treating datasets as organizational assets. Organizations functioning at this level of data maturity may also consider data monetization. Here, we generally see secure, democratized data access across the organization. The data itself is reliable and trustworthy. Users can access self-service analytics in real time, while DataOps is enabled through end-to-end automation, as is efficient machine learning with MLOps.
To progress at this stage of data maturity, organizations should focus on continuing to expand the data platform to include all valuable company data assets and, beyond that, supplement company data with third-party sources that meaningfully enhance data value. From a data governance perspective, enable self-service data subscriptions governed by enterprise role-based access and enforce data SLAs so that data subscribers can trust their data dependencies.
Technical team members should continue to rapidly experiment with new GenAI models to reduce cost and/or increase performance, while also work to instrument all GenAI functions to ensure continued alignment, achievement of business metrics, and positive unit outcomes.
Lastly, on an organizational level, begin to involve business users. Upskill them on cloud data and AI concepts and introduce them to AI tools via no-code solutions.
Phase Four: Innovative
Organizations at this stage are not only deriving value from their data, but they are leading their fields using emerging technology and environments that foster unconstrained innovation.
They are creating competitive disruption through business model evolution—and revolution. Organizations at this level of data maturity breed the most meaningful and safe use of GenAI.
However, they must still continue to reinforce the culture and leadership that’s gotten them to the apex of data maturity. Organizations can do this by fostering an expectation that all roles in the business can be positively impacted by AI and data, as well as gathering feature requests and ideas from all functional areas. If organizations are looking to push to that next level, they can consider training a small or large GenAI language model if there’s a potential for competitive advantage.
Or, perhaps, look to identify unique data assets that could be monetized. Most importantly, they should never stop looking over the horizon for emerging technologies in order to experiment early and avoid being disrupted.
No matter where an organization falls on this spectrum of data maturity, the important thing is to have awareness before launching any GenAI initiative. This can ensure that goals and expectations are aligned with what the technology can accomplish within an organization’s distinct data environment. Though it can be tempting to join the frenzy and be able to say your organization is offering GenAI now, it will yield much more value in the long term to make sure your data is in a place that enables GenAI to deliver real, tangible results.