AI and LLMs impact on data governance
- Posted on January 30, 2024
- Estimated reading time 5 minutes
Data governance is a crucial aspect of any organisation's data strategy and the first steps in anyone's AI journey. According to the Avanade AI Readiness report, a survey of 3,000 business and IT executives across the globe, nearly all leaders (92%) agreed that organizations need to shift to an AI-first operating model in the next 12 months to remain competitive. Almost everyone (95%) shares Avanade’s optimism in an AI-first future. But most executives (94%) said they need to increase investments on data platforms to make those aspirations a reality, and scale them across their business. Data Governance is an essential part of this journey and AI itself can help accelerate this journey across an enterprise.
Data Governance involves the management of data throughout its lifecycle, from creation to deletion, ensuring that it is accurate, complete, secure, and compliant with applicable laws and regulations. The financial impact of poor data governance and data management is vast, as Gartner found in their 2021 study that organizations with poor quality data suffer an annual cost of $12.9 million. Completing data governance tasks can be a complex and time-consuming process, and organizations may struggle to keep up with the rapidly changing data landscape or have issues getting started. Populating a data glossary, data dictionary, identifying critical data elements with relevant metadata, applying data quality rules and assembling data lineage for an enterprise or even one department can be a daunting task. On top of this, even with a robust data governance policy, there can still be unanticipated threats with 82% of data breaches occurring due to human error. This is where AI-powered solutions come in, providing businesses with the tools they need to speed up their data governance processes and reap the benefits of automated and AI-enabled data governance, accelerating their progress towards becoming a data-driven organisation.
One of the most significant benefits of AI-powered data governance solutions is the ability to synthesize insights from many diverse sources and knowledge sets into one data store. This allows businesses to access the information quickly and easily to ensure they make informed decisions about their data. For example, AI systems can review and summarize legal documents, providing lawyers with quick access to key information. Similarly, NLP-powered solutions like Microsoft Copilot can be used to run queries on data compliance and breaches, creating summaries and reports of data activities using natural language.
Another advantage of AI-powered data governance is the speed with which it can generate intuitive processes and policies from multiple points of reference. This can be particularly valuable in rapidly changing business environments where companies need to update data governance policies in line with business and legislative demands. For instance, Generative AI coding assistants can not only generate code from natural language prompts by users, but also generate queries based on a company's pre-existing data structures and business rules. This ability to generate content and resources as required allows companies to promptly develop new data governance policies, frameworks and standards among rapidly changing circumstances.
AI-powered solutions also offer improved data stewardship by updating metadata in a data catalogue automatically to comply with company needs, industry standards, and regulations. This is particularly important in industries such as healthcare and finance, where data privacy and security are critical with new regulations coming in on a regular basis. For example, automation of security tasks and processes across multiple systems, as well as comprehensive reporting on all data access and security policies. This allows companies to stay on top of their data governance requirements, even as the regulatory landscape continues to evolve.
Business enablement is another key benefit to integrating AI within data governance. Data governance tasks such as defining business definitions or defining data quality rules can be tiring, boring and time consuming for Data Owners and Data Stewards. If we think about the creation and monitoring of data quality rules in AI-Powered governance automation – all data outliers can be found and flagged before analysis, consistency checks with source systems can be done effortlessly, and cleansing and standardisation of all new data can become seamless. In 2018, Accenture found that for every hour spent analysing data, analysts may spend up to seven hours cleaning and preparing it. Thats a lot of time and money that could be potentially saved across an organisation by utilising LLMs and AI. An example of this can be the use of Generative AI in data catalogues which can automatically enrich metadata and suggest descriptions, synonyms or alternatives data owners/stewards based on previous data use.
AI-powered governance solutions can be used to carry out low level data transformation work, allowing data governance teams to quickly adopt new AI technologies along their value chain, and architects to future proof data strategies. It is important to note that all Data Governance policies and procedures (new and updated) should be devised with the aim of being automated to ensure computational Data Governance thus allowing Data Owners, Stewards and Governance teams more time to focus on more value-add activities.
In conclusion, AI powered data governance solutions have the power to completely transform your business' data operations from improved data quality and security to faster and more effective policy development. It is important to note that Ai is not here to replace your data governance teams, but used correctly will compliment them by becoming your organisation’s number one data governance advocate. Without having humans in the loop your organisation could run the risk of incorrect labelling of assets or AI hallucinations being adopted. As the data landscape continues to grow exponentially, it is essential that companies embrace these technologies to stay ahead of the curve and make the most of their data.