Why scaling your AI without data governance is a mistake

Data governance and AI: Both here to stay!

Table of contents

∘ The rapid acceleration of AI initiatives
∘ AI in financial services
∘ AI in healthcare
∘ AI in retail
∘ The critical role of data in AI projects
∘ Risks of scaling AI without data governance
∘ How DataGalaxy supports AI integration
∘ Conclusion

The pace of change in modern business is relentless. From customer demands for better experiences and increased globalization to a dynamic regulatory environment and the proliferation of data, the pressure is on, prompting businesses large and small to explore new ways to remain competitive. And with new, innovative companies disrupting traditional markets, remaining a leader in their respective industries is a challenge, requiring organizations to embrace new strategies and new tools to support their goals.

Increasingly, one technology is emerging as a transformative force: artificial intelligence (AI). Once the domain of science fiction, today, AI is rewriting the rules for modern business. According to PWC, 73% of businesses currently use AI in some aspect of their business, proof that AI is quickly becoming a bedrock of modern business business strategy.

But as AI becomes increasingly important, so, too does the data that supports it. After all, AI models are only as good as the data that feeds them. The insights AI delivers are a direct reflection of the data that trains them. When that data is high-quality, the insights follow suit, giving users confidence in the integrity and validity of the results. When the data is inaccurate, outdated, or incomplete, the outputs AI delivers are flawed, causing faulty decisions that can lead to poor customer experiences, missed opportunities, and tarnished reputations.

The rapid acceleration of AI initiatives

The general availability of tools such as ChatGPT, Bard, and GitHub Copilot sparked companies to increase their investments in AI-driven technologies. According to Gartner, in October 2023, 55% of organizations reported that they are piloting or in production with generative AI, up 35% from a Gartner poll conducted earlier in 2023 which reported that less than 20% of respondents were either piloting or in production with generative AI solutions.

As AI takes a firmer hold on modern business, organizations across industries are benefiting from increased efficiency, improved customer service, and better, more informed decision-making.

AI in financial services

In financial services, AI plays a vital role in risk mitigation. Using AI, firms can analyze large volumes of transactional data to detect anomalies and flag suspicious behavior that could lead to fraud. Further, AI helps financial services streamline regulatory processes to ensure they remain compliant, even as regulations evolve. Further, more than 30% of financial institutions currently use AI in their product development.

AI in healthcare

Healthcare organizations employ AI to transform patient care through the use of automated imaging, providing critical insights to healthcare providers. AI also improves efficiency by automating routine, administrative tasks such as labeling images and generating reports.

AI in retail

AI enables retailers to improve customer experiences by surfacing personalized recommendations via targeted campaigns, websites, and mobile apps. Retailers also use AI to manage and optimize inventory and provide dynamic pricing to customers based on stock levels and seasonal demand. And that’s only the beginning. Retail use of AI is expected to grow at a compound annual growth rate of 30% from 2023 to 2030.

These examples showcase just a few ways AI is revolutionizing the business landscape today — and it’s only the start. As companies across industries continue to realize the transformative power that AI can have on customer service, marketing, operations, and decision-making, their adoption of AI will only accelerate.

The critical role of data in AI projects

While AI may be taking center stage, data is the star of the show. Data fuels AI algorithms, enabling them to learn, adapt, and make decisions. Here’s how it works.

AI tools start by collecting data — the more, the better. Ensuring that the data is clean and accurate is paramount, as the quality of the data ultimately drives the quality of the insights and predictions. If the data has questionable quality, it’s a good idea to fix the errors, remove duplicates, or label the data before the model consumes it. Once the model you select consumes the data, the model will process the data, look for patterns, and make predictions. Some will be right and some will not. That’s all part of the learning process.

Next, it’s time for you to test the model by providing new data and evaluating the accuracy of its predictions. If the model does well, the training was successful. If it returns flawed results, then you’ll need to go back and adjust the model parameters or add more training data until the accuracy improves.

By now, it should be clear that the quality of the data — its precision, reliability, and relevance — directly impacts the performance and outcomes of AI applications. Training models with high-quality data generally yield good, trustworthy results.

But when garbage data goes into AI models, garbage results come out. That’s when problems begin to arise. Bad insights lead to bad decisions which, in turn, cause problems for the business. Customer experiences are disjointed, revenue declines, operations become inefficient, and perceptions of the company suffer.

Risks of scaling AI without data governance

The importance of data quality cannot be understated. Poor data quality leads to inaccurate AI predictions which, in turn, causes business leaders to make poor decisions that lead to skewed outcomes, biased predictions, and error-filled conclusions. And when stakeholders lose faith in the integrity of the data, they become reluctant to use data to make decisions.

Even worse, when organizations scale their AI using poor-quality data, they can face financial and reputational harm, legal issues, or hefty fines stemming from data breaches, unauthorized access, and non-compliance with regulations such as GDPR and CCPA. New regulations focused on the ethical use of AI are emerging as well, increasing the need for greater oversight and governance when using AI-driven insights.

Overcoming poor data quality requires organizations to embrace data governance. By implementing robust frameworks for managing data throughout its lifecycle, organizations can improve the accuracy and reliability of their data, ensuring it is fit for purpose.

Data governance also establishes organization-wide policies that govern the collection, storage, processing, and sharing of data within the organization, as well as the processes needed to ensure regulatory compliance. Furthermore, data governance promotes transparency, accountability, and traceability of data, enabling stakeholders to more easily spot errors and mitigate risks associated with flawed or misrepresented AI insights before they negatively impact business operations.

How DataGalaxy supports AI integration

An established data governance strategy helps organizations minimize errors, inconsistencies, and biases in their data. This, in turn, fosters greater trust in the data and boosts users’ confidence as they apply AI-driven insights in their decision-making processes.

DataGalaxy supports AI by empowering organizations to embrace the data governance tools and frameworks needed to ensure their data remains secure, compliant, and of the highest quality. Using their Data Knowledge Catalog, DataGalaxy provides in-depth clarity about data definitions, data lineage, and other essential business attributes so all users can understand the data and use it as a strategic asset. User-friendly features such as an all-in-one Business Glossary, intuitive data visualization tools, detailed data lineage tracking, and natural language search enable users from all departments to find — and trust — the data they need to power AI.

As AI continues to gain momentum, businesses will, inevitably, want to scale their AI initiatives. Effective data governance holds the key to unlocking AI’s potential and expanding its use effectively and responsibly. Using DataGalaxy’s Data Knowledge Catalog, organizations can make it easier for everyone to find the data they need to run the business — and they can do so with the proper security and controls in place to protect sensitive data. By eliminating data silos, DataGalaxy also democratizes data collaboration, providing stakeholders with access to reliable, interconnected sources so they can make better decisions.

Conclusion

Clearly, AI is here to stay. Its use is poised to grow at an unprecedented rate, prompting businesses worldwide to rethink their strategies as they relate to the use and adoption of AI applications. With data fueling the learning and predictive capabilities of AI models, it is imperative that organizations adopt data governance as a way to ensure the integrity of their data. With DataGalaxy as a partner, organizations can harness the power and potential of AI to drive innovation, increase efficiency, and remain a competitive force in their industry.

To learn how DataGalaxy can help your business scale its use of AI, visit www.datagalaxy.com.

Sébastien (Seb) Thomas has over 16 years of experience in data-centric projects. He has served as a team leader, modeling expert, database manager, and now as a data entrepreneur by co-founding DataGalaxy, the industry’s first Data Knowledge Catalog delivering data culture and literacy across organizations globally.

Today, more than 170 international companies of all sizes trust DataGalaxy to enable data literacy, connect business & IT teams, and champion data governance for their entire organization.

Learn more about how DataGalaxy can help your teams bring data to the people: https://www.datagalaxy.com/en/