The path to AI-ready data: Chief data officer considerations and a rubric for readiness

The benefits of AI include quicker insights through ML techniques, automation of manual processes, and improved accuracy. It’s no secret that AI flourishes with clean data; because of this CDOs must find a way to acquire and make data accessible to facilitate their adoption of AI.

CDOs can follow a data preparation rubric to “level up” toward data readiness and AI adoption. An example of a data preparation rubric would be:

• Tier 1: AI ready – Organization can immediately provide answers with data.
• Tier 2: Maturity readiness – Organization must scramble to answer basic questions with data.
• Tier 3: Readiness deficit – Organization cannot provide answers to basic questions with data.

Organizations can use the following three steps to help them “level up” toward being AI ready:

Step 1: Build a key stakeholder network: Organizations and their CDOs must find a way to connect key stakeholders to the data products they are trying to deploy.

Step 2: Develop a robust data transformation strategy: To adopt AI, an organization and its CDO will need to develop a strategy for data transformation and figure out if their data is interoperable.

Step 3: Enable data security, privacy, and trust: In preparing for AI, security, privacy, and trust must be kept in mind while determining approaches to making the organization’s data reusable; CDOs can promote this by restricting user access as well as implementing ML algorithms to proactively search for weaknesses in security.

As we know, AI solutions are only as useful as the good, copious data by which they are trained and built. The CDO is in a unique position to be able to provide this data—helping organizations take honest assessments of their current data readiness and AI adoption and assessing future AI and data possibilities. The CDO’s role will evolve even more in the coming years alongside advancements in technologies, new regulation, and changes to data use patterns.