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TDWI Upside - Where Data Means Business

How To Prepare Your Organization for AI’s Impact on Data Processes and Products

Here’s what your enterprise will need to benefit from generative AI.

In 2023, AI broke through, thanks in no small part to the commercialization of generative AI. In 2024, generative AI will continue to be a significant trend as companies begin to use the technology for more tasks associated with data management and analytics.

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However, to benefit from generative AI, you will need a data literate team that understands this emerging landscape and terminologies, and an airtight strategy to comply with the inevitable introduction of new regulatory frameworks.

Adapting to the AI Boom

Generative AI and other AI technologies will become more intertwined with data processes such as predictive analytics. However, there will be a more significant push for developing native AI products that will require the input of both the technical and business teams. This development will be spurred by business outcomes and as such, the following best practices will be critical to ensuring this development is secure and efficient, and produces the best results.

  • Only use high-quality data. When building AI products, the classic phrase "garbage in, garbage out" still applies. The people creating AI products need access to a vast amount of data and this data must be high quality, understandable, and easily accessible. When data is compromised, it can't be used to develop products such as generative AI bots. Ultimately, effective data management is about making your data AI-ready.

  • Be patient. Organizations must refrain from launching too many AI products too quickly. Instead, AI products must be tested numerous times to be sure they are achieving the correct result. The key here is agility -- starting small and working up to more integrated generative and other AI products.

  • Educate your teams. AI technologies are advancing quickly, and as a result, anyone developing AI products or installing out-of-the-box applications needs to understand what can be done with them. Ensure you've provided your business and technical teams with the knowledge they need to make the right decisions about which technologies to adopt, which products to develop, and how these technologies will address specific business use cases.

  • Pick the right partners. Consider technology partners with a platform that supports the data libraries you are using. Your data partners will play an essential role in helping you operationalize your AI efforts from a platform perspective and, crucially, will enable you to scale.

Implementing Advanced Data Literacy

In many ways, the advanced data literacy trend is connected to the growth of AI technologies. However, beyond the need for users to understand the breadth and power of AI, advanced data literacy requires users know the related terminologies, KPIs, and metrics connected with AI.

Data literacy is not just about how data is consumed. It's about how it is classified within your data ecosystem. You should focus on the literacy needs of each user and train them to understand the parts of your data ecosystem they will need to access to contribute to AI product development.

You must implement a solid, comprehensive framework that informs when and how you roll out data literacy programs in the various departments and teams in your organization. Based on the guidelines laid out in the framework, advanced data literacy training must be rolled out gradually, starting with smaller user groups, then transitioning to larger teams before attempting company-wide education programs.

Remember that data literacy must be a constant, ongoing process. To that end, you need stakeholders in place that will ensure everyone in your organization is updated as the program progresses.

A Widening Regulatory Landscape

Data privacy compliance and more-specific regulatory frameworks are a significant part of the data industry. However, as we enter 2024, we can expect to see more countries, jurisdictions, and industries introducing new regulations or updating existing ones. With discussions over the impact of AI a hot topic for governments worldwide, AI-specific regulations are likely to follow.

Once again, education is a vital part of the compliance process. Ensure that everyone in your organization understands the regulatory environment they are working in. Having a dedicated compliance officer to navigate this complex environment and a team below them to transmit this information throughout the company is a must.

You will also need a compliance strategy that incorporates all the latest requirements and includes processes for implementation. The best way to ensure this is through a dedicated data governance tool. The tool should be configured to ensure that only verified users can access specific data assets and that data is only made available for usage according to compliance regulations. Beyond this, a dedicated tool will automatically produce reports that can be made available to auditors upon request.

About the Author

Sharad Varshney is the co-founder and chief executive officer at OvalEdge, creators of a data catalog and data governance tool. He founded OvalEdge to blend his unique experience in big data technology and process management into creating a much-needed data management product. He has a nuclear engineering degree from IIT, the premier institute of technology in India. You can reach the author via email or LinkedIn.

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