{"id":7409,"date":"2026-03-17T09:30:40","date_gmt":"2026-03-17T13:30:40","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=7409"},"modified":"2026-03-17T09:41:25","modified_gmt":"2026-03-17T13:41:25","slug":"datagovops-the-unsung-hero-in-achieving-ai-ready-data","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/datagovops-the-unsung-hero-in-achieving-ai-ready-data\/","title":{"rendered":"DataGovOps \u2013 The Unsung Hero in Achieving AI-Ready Data"},"content":{"rendered":"

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DataOps.live’s Guy Adams offers commentary on how DataGovOps is the unsung hero is achieving AI-ready data. <\/strong><\/em>This article originally appeared in Insight Jam<\/a>, an enterprise IT community that enables human conversation on AI.<\/span><\/strong><\/em><\/p>\n

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Last week, we introduced the concept of\u00a0AI-ready data<\/a>\u00a0and defined the various ways that organizations can measure it. Here we\u2019re going to look at strategies for improving the metrics and actually achieving AI-ready data, which will lean heavily on an emerging practice called DataGovOps.<\/p>\n

To quickly recap from my earlier piece, the quality of AI is based in large part on the quality of your data. Bad data equals bad AI. Having unsuitable data is not the only way that AI projects can go off the rail, but it\u2019s certainly a big one. Research has identified about 200 metrics that can be used to measure the AI-readiness of data. These metrics span six categories, including data quality, data for AI training, data governance, data semantics, data management and operationalization, and data interoperability.<\/p>\n

Clearly, data must be monitored if it\u2019s going to be improved. But measuring the data is not enough. To succeed with AI, one must take concrete steps to improve the AI-readiness of data. That\u2019s where the real work begins.<\/p>\n

How Automating Data Remediation Can Speed Up AI-Readiness<\/strong><\/h4>\n

The majority of organizations are likely to have a low AI-readiness score, which is a form of technical debt. After all, data engineers only have so many cycles to spend in a day, with a never-ending list of demands on their time. They may have established methods of improving structured data for traditional BI and analytics projects, but that doesn\u2019t really cut it in enterprise AI, which operates on a much larger and diverse pool of less structured data.<\/p>\n

The good news is that it is possible to improve the AI-readiness of data if an organization is willing to take the steps to get there. The field of data management has improved significantly over the years. Through trial and tribulation, a series of best practices have emerged that can help an organization improve the quality of their data.<\/p>\n

Thanks to the maturation of AI itself, data professionals can not only receive targeted recommendations on how to improve data management for their specific environment, but they can lean on AI to automate much of the rote work of remediating the specific data management challenges. Think of it as using AI to improve AI.<\/p>\n

This doesn\u2019t absolve the data leader of responsibility, of course. It\u2019s still up to data leaders to define the overall data governance strategy, to set specific AI-readiness goals, and to oversee the execution of the strategy. In some ways, setting the governance goals is a more difficult challenge. Enterprise data is extremely diverse and it\u2019s constantly changing. The specific goals and metrics that are important for one organization will not necessarily apply to another organization.<\/p>\n

The odds of successfully achieving AI-ready data improve when a data professional takes a proactive approach. Those who try to deal with these issues in a reactive way are likely to fall even further behind.<\/p>\n

Operationalization of Data Governance<\/strong><\/h3>\n

The world of software development has been fundamentally transformed through DevOps, which is a cultural and technical methodology combining software development (Dev) and IT operations (Ops) to shorten the development lifecycle and deliver high-quality software continuously. Organizations have adopted this approach to take much of the risk out of the development, testing, and deployment lifecycle.<\/p>\n

Data engineers have adopted a similar approach to managing the data management lifecycle, dubbed DataOps which\u00a0Gartner defines<\/a>\u00a0as\u00a0a collaborative data management practice focused on improving the communication,\u00a0integration\u00a0and automation of data flows between data managers and data consumers across\u00a0an\u00a0<\/u>\u00a0organization<\/u>. As the AI revolution has emerged and the importance of good data governance has rose to the fore, we\u2019re seeing another variation of that theme, which some call DataGovOps.<\/p>\n

DataGovOps involves automating data governance policies, security, and compliance across the data lifecycle, applying DevOps-like, continuous, and automated approaches to ensure data quality and integrity. For organizations looking to apply DataGovOps techniques to achieve AI-ready data, it is important to consider the following recommendations before getting started:<\/p>\n