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Generative AI

The Foundation of Generative AI: The Imperative of Clean Data

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The Setting

Imagine a solitary walk in a crowded park in the middle of downtown. Walking down the main path, one can hear joggers bounding past one’s left, a couple laughing to one’s right, a street musician playing the violin over one’s shoulder, and a group of children laughing at the playground somewhere off and to the left. As one walks deeper into the park, the number of conversations grows, and the amount of discernable words shrinks to zero. Next, imagine walking that same path with a small child just beginning to speak. As that child listens to the individual conversations in the bustling park, imagine quizzing that child on what the joggers are thinking, what music the musician is playing, or how many children are playing in the park half a block away.

 

This story reflects the reality of Enterprise Generative AI. These tools are infinitely curious children with photographic memories, capable of understanding nuance and many details. But even the most well-engineered AI platforms trained on noisy, crowded, or incorrect data will hallucinate, predict incorrectly, lie, or provide dangerous recommendations. Therefore, it is no surprise that data preparation and cleaning takes up eighty percent of data scientists’ time, and 78% of organizations say that poor or noisy data is the top barrier to enterprise AI Adoption¹.

Clean data is the bedrock upon which successful generative AI initiatives are built. It encompasses discoverable, available, and trustable data that is free from errors, inconsistencies, and biases. The importance of clean data cannot be overstated, especially as businesses embark on their journey to harness the power of generative AI.

One of the primary reasons why clean data is essential for generative AI is its direct impact on the quality of outputs generated by AI models. Generative AI systems can only learn patterns and structures based on the data they are trained on, so any noise or inaccuracies within the data can significantly compromise the integrity and reliability of the generated outputs. For businesses relying on GenAI to automate tasks, create content, or make critical decisions, the quality of these outputs is paramount.

Moreover, clean data fosters trust and confidence in AI-driven solutions. Inaccurate or biased outputs resulting from poor-quality data can erode trust among stakeholders and lead to skepticism regarding the capabilities and effectiveness of generative AI technologies. Building trust in AI requires a rigorous commitment to data quality and integrity throughout the entire data lifecycle.

Businesses should also recognize the profound implications of using bad data in generative AI initiatives. When organizations feed flawed or incomplete data into AI models, they risk propagating and amplifying existing biases, errors, and inaccuracies, thereby perpetuating systemic issues and reinforcing negative outcomes. Furthermore, using bad data can undermine the credibility of AI-driven insights and recommendations, hindering decision-making processes and impeding progress toward strategic objectives.

The Challenge

The consequences of using bad data extend beyond immediate operational challenges and can have far-reaching implications for business performance, customer satisfaction, and regulatory compliance. For example, poor data quality costs the US healthcare system $210 billion per year². In an era where data privacy and security are paramount concerns, the integrity and trustworthiness of data assets cannot be compromised.

To mitigate the risks associated with bad data and maximize the potential of generative AI, businesses must prioritize data quality as a strategic imperative. This entails implementing robust data governance frameworks, investing in data quality management tools and technologies, and fostering a culture of data stewardship and accountability across the organization.

Furthermore, businesses should adopt a holistic approach to data management that encompasses data acquisition, preparation, validation, and maintenance. By proactively addressing data quality issues at each stage of the data lifecycle, organizations can enhance the effectiveness, reliability, and scalability of their generative AI initiatives.

Looking Forward

At Perficient, we find that clients who reinvent their processes to teach their AI models use AI far more often and with more significant impact than organizations who force AI into the periphery. Therefore, to leverage AI, organizations must recommit not only to data quality initiatives but also to the operational initiatives most critical to leveraging AI in the future. Ultimately, this effort includes executives as much as Enterprise Architects and organizations that align around these goals will be well-positioned to win in today’s business environment.

Clean data is not just a prerequisite for successful generative AI—it is the foundation upon which the future of AI-driven innovation and transformation rests. By recognizing the importance of clean data and taking proactive measures to ensure data quality and integrity, businesses can unlock the full potential of generative AI and drive sustainable growth, innovation, and value creation in the digital age. Learn more about how Perficient can help your organization harness the power of this technology- contact us today.

¹https://www.experianplc.com/newsroom/press-releases/2022/quality-data-proves-critical-to-business-performance

²Source: IBM

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Jordan Kanter, Marketing Director

Jordan Kanter is a Marketing Director at Perficient. With over a decade of experience in digital, he has helped leading brands such as TD Ameritrade, Fidelity, The Hartford, United Airlines, Intercontinental Hotels Group, and Hyundai to drive ROI across channels.

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