Data strategy in the age of GenAI

The new age of Gen AI is likely to bring about several changes in the way we approach Data Strategy.

Data strategists will have to incorporate new Gen AI powered opportunities and reshape their views on what is possible with data. The most common question organizations have been asking of Data Strategists is “How can we incorporate Gen AI within our decision-making process”. To answer that we will have understand how Gen AI will change the approach to data strategy.

Demonstrate Value –Yes, we all have seen the regenerative capabilities of ChatGPT etc. but how it will enable an organization does to achieve tangible goals of efficiency and product/service development?

A data strategist needs to clearly articulate the benefits and costs of Gen AI. Organizations need to closely look at what kind of investments can they realistically make in Gen AI.

Increased focus on data quality: There is no more doing data quality in bits and pieces through a combination home grown and vendor tools. A comprehensive data quality approach must be in place for Gen AI to achieve benefits in the context of an organization’s ecosystem. A strategy must be in place to ensure data is accurate and complete.

Address data privacy and security: With the massive amount of data that Gen AI needs, it is critical to put measures in place that protects personal and sensitive information. This includes using anonymized data, secure data storage and transmission and access controls.

Data Engineering: Most of organizations today have processes in place to consume structured and semi-structured data. With Gen AI comes the requirement to process huge volumes of unstructured data. A Gen AI driven data strategy should have an approach and a roadmap to establishing a data engineering pipeline to consume text and video. This has both compute and storage implications.

Synthetic data: Synthetic data is a powerful capability of Gen AI but with it comes the challenges of bias and realism. A data strategy needs to include a robust mechanism to weed out bias and ensure the synthetic data reflects the real world.

AI Skills:  A framework to identify which Gen AI skills are required and a roadmap to develop them with an organization. A new training curriculum should be developed that will help ensure Gen AI is democratic and does not exist in a silo creating fancy and expensive models that no one can understand and use.

Gen AI has great promise, but organizations need to look before they jump on the bandwagon. They must ensure that they have all the foundational elements ready for Gen AI success including identifying tangible use cases. Don’t go for Gen AI just because everybody else is.