Of late, there is a tremendous hype about how Generative AI can do this, Generative AI can do that, etc. Most organizations who are anxious to get on the bandwagon have begun to realize that their data estate is not quite ready. Hitherto, CIOs have always put on the back burner any enterprise attempt at data management. It has always been a “surgical fix” designed to mitigate issues occurring at a point in time. Data management strategies abound but there are not many examples of successful data management implementations of these well researched (mostly) strategies. Why? There was no sense of urgency because the organization could always make do with workarounds and short-term fixes. They could never convince their CFO that a reliable and trustworthy data estate can indeed reap returns for the company.
Data readiness is clearly the foundation for unlocking the true business value of generative AI. The underlying models rely on large amounts of data to learn patterns and make appropriate deductions relevant to the business. If the data used to train the model is inaccurate, incomplete, or biased, the outputs will be flawed and potentially unusable. It is one thing for a public LLM like ChatGPT or Gemini to give you erroneous information, but it is another thing for businesses to rely on the model outputs to run their business. It is not just “Garbage In, Garbage Out” anymore but it is more like F.O.U.L. data (data that results in Fractured Outcomes and Unforeseen Losses).
So what then are the benefits of data readiness? There are at least four that come to mind:
- Clean, unambiguous and high-quality data allows generative AI models to learn more effectively, leading to more accurate, relevant, and creative outputs. This results in improved model performance which translates to better business results.
- Carefully curated data ensures that generative AI models don’t perpetuate stereotypes or generate discriminatory outputs. Reducing bias is crucial for ethical considerations and maintaining trust with your users.
- Data in the right format is easier for generative AI models to process, leading to faster training times and improved overall efficiency. This allows one to test the results of the models and refine them more quickly.
- Investing in data readiness upfront can reduce costs in the long run. Cleaning and correcting messy data after the fact is more expensive and time-consuming than preparing it properly before training models.
Data Readiness for Generative AI requires a multi-faceted approach:
- Data Collection: Gather relevant data from reliable sources and ensure it aligns with the intended use case.
- Data Cleaning: Clean data to remove errors, inconsistencies, and missing values.
- Data Labeling: If the model requires labeled data, ensure accurate and consistent labeling to avoid confusing the model.
- Data Governance: Establish data governance practices to ensure data quality, security, and compliance with regulations.
In conclusion, it is not just enough to sit on troves of data and except to do the Gen AI magic on top of them. IT will need to prioritize data readiness through effective data management strategies if they want to help businesses unlock the full potential of generative AI and achieve significant improvements in efficiency, innovation, and overall business value.
