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The role of data quality in fueling Gen AI success

AI has touched virtually every facet of our everyday lives, with generative AI applications transforming the way modern businesses operate. A study of 63 use cases across 16 business functions has revealed that generative AI can add value of $2.6 trillion to $4.4 trillion annually. Diving deep into each of these use cases, data sits at the heart of every successful use case of AI data analysis and generative AI systems.

Although data quality remains one of the biggest hurdles to AI success, modern businesses are becoming more aware of data needs and focusing on data quality to fuel AI success. But how do machine learning and data quality affect each other?

Importance of data quality in fueling AI success

There is no denying the fact that data has become the most valuable currency in today’s digital economy. With the transformative potential of data analytics, AI, and generative AI, investments in AI projects are increasing. However, 33% to 35% of these projects either fail or experience delays because of poor data quality. Not only this, but the cost of poor data quality can run up to trillions of dollars every year, where retraining a 530-billion parameter model can cost as much as $100 million.

Aside from the cost factor, poor data quality can introduce unnecessary and harmful noise into the generative AI systems and models, leading to misleading answers, nonsensical output, or overall lower efficacy. And with already a sceptical outlook towards generative AI and an overall resistance to change, this can further erode trust in generative AI applications, hindering their progression from experimentation to scale.

Ensuring data quality for AI

There is no shortage of data for businesses to use in their AI projects. In fact, many industries collect vast amounts of data in their day-to-day operations. But as we have seen earlier, not every bit of data can be useful when it comes to training AI models. So, how can we ensure data quality for AI applications?

  • The first step is data profiling, giving you insights into the distribution of values, basic statistical information, formatting inconsistencies, and much more in the dataset of interest. This way, you can determine whether the dataset is useful or the steps to take to make it functional.
  • Prepare the data by tweaking the dataset to help make sure your data will work well within the parameters of your AI model.
  • The next step is to quickly validate and evaluate the quality of your dataset with pre-built quality rules or standardisation formats.
  • The last step is continuous data quality monitoring and evaluation to identify specific problems any attribute may have and decide whether they will be useful to your machine learning model or not.

Taking generative AI applications from experimentation to scale

The potential for generative AI to generate value for modern businesses is tremendous. However, with a rapidly shifting landscape and technological advances, taking generative AI applications from experimentation to scale can be challenging. Here are some guiding principles that can help data leaders scale the generative AI systems:

  • Let the value you desire guide your data needs.
  • Build specific and relevant capabilities into your data architecture to support a wide range of use cases and unstructured data.
  • Focus on significant points within the data lifecycle – and develop necessary interventions – to ensure data quality.
  • Stay on top of the fluid regulatory environment to appropriately secure any sensitive and enterprise proprietary data.
  • Focus on building data engineering talent to support your data programs and generative AI systems.
  • Leverage generative AI applications to make your data value chain – from data engineering and governance to data analysis – more efficient.
  • Focus on performance monitoring to stay agile and intervene quickly if you encounter any challenges or changes to improving data performance.

How can Infosys BPM help your business with generative AI data analysis?

The ever-evolving space of AI has the potential to overturn innovation and revolutionise the way the business world operates. Irrespective of where an industry or a company falls on the AI-readiness spectrum, focusing on data quality in AI and generative AI applications has tremendous potential. Infosys BPM Generative AI Business Operations Platform can help you leverage AI data analysis and harness the power of generative AI to transform business operations and accelerate value creation. So, lead the charge with AI-first operations while reinforcing AI ethics to reimagine your business with Infosys BPM.

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