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Trust in the Age of AI: The Key Role of Data Governance

A business professional looks at charts and considers a data governance solution.
A strong data governance solution can help establish a solid foundation of trust and meet evolving AI challenges. [STORYTELLER / Adobe]

With a data governance solution, you can take control of your data, build trust, and achieve your AI business goals.

In today’s world of artificial intelligence (AI), data is the key to success. But simply collecting data isn’t enough — data is only valuable if it’s accurate, secure, and private. In fact, a recent survey found that 92% of analytics and IT decision makers say trusted data is needed more than ever before. That’s where your data governance solution comes in. 

With data governance, your company can confidently build and use AI solutions your customers will trust.

Expert Coaching Unlocked: 5 Data Governance Essentials for AI Trust

Learn how to build a robust data governance framework, manage risks, and maintain transparency, fostering trust in your
AI-driven ecosystem.

What is data governance?

Let’s start with the basics. In simple terms, data governance is a set of processes, policies, and guidelines that ensure the proper management of an organization’s data. It involves defining who has access to data, how it should be used, and how it should be protected. Data governance provides a framework for managing data, ensuring its accuracy, consistency, and security, and ultimately driving better decision-making.

The key components of data governance include data quality, data security, data privacy, data architecture, and data management. Let’s break each of these down: 

  • Data quality means that your data is as accurate, complete, and reliable as it can be. Without data quality, businesses will run into daily customer challenges. For example, if a retailer does not have your complete and accurate shipping information, customers likely won’t receive orders on time.
  • Data security keeps information safe from being stolen or changed without permission. Financial institutions use multi-factor authentication to protect customer information, like checking account and credit card numbers, from unauthorized access.
  • Data privacy ensures that personal information is collected, stored, and used in a responsible and ethical manner. A healthcare provider needs to collect patient information to give medical care. This means they likely have rules in place to make sure they only collect and use the information they need for that purpose.
  • Data architecture describes how data is organized, integrated, and managed across an organization. Data lakes store raw data, data warehouses store structured and processed data, and databases manage transactional operations. In this architecture, supplier data, like orders placed, payment information, and shipping dates, would be stored in a data lake.
  • Data management comprises the processes and tools used to organize, store, and access data. A data platform, like Data Cloud, provides companies the ability to see a 360-degree view of the customer. With real-time data, retail customer service representatives can see a single view of a customer during a call — including in-store and online purchases, loyalty status, and mobile app data.

By incorporating these essential components of data governance, you’re establishing a solid foundation for AI success. But why is that? 

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How are data governance and AI connected?

AI uses data to learn, make decisions, and improve its capabilities. Without data, AI can’t understand patterns, make predictions, or find solutions. In theory, the more data AI uses, the better it gets. However, the quality of the data is more important than the quantity. And with the rapid growth and adoption of AI across every industry, this means data governance is more vital than ever.

But AI’s rapid growth also creates new challenges for data governance. For example, it’s important to determine who’s responsible when an AI system makes an error. An organization must also ensure that AI systems are only using and acting on the right data, but how? Organizations must also specify what decisions AI systems are allowed to make with or without human oversight. Other challenges include identifying and evaluating data that feeds into a specific AI model. This can mean ensuring data accuracy, even after it’s been converted to a different format, as well as ensuring data collection methods follow legal standards and keeping logs of who accessed the data, when, and for what purpose.

A strong data governance solution can help your organization meet these evolving challenges around AI and support AI growth by:

  • Ensuring accuracy and quality: Data governance guarantees accuracy and quality by setting up standards and procedures for collecting, managing, and storing data. This helps to prevent errors, bias, and inconsistencies in the data that could lead to flawed AI models.
  • Mitigating legal and ethical risks: By upholding privacy laws and ethical standards, your company can protect the rights of individuals and build trust with your stakeholders. This is especially important as AI solutions evolve to handle increasingly sensitive personal data.
  • Facilitating responsibility, transparency, and accountability: Build trust by documenting data sources, algorithms, and decision-making processes. This can help your customers understand how your AI solutions are used and the impact they can have. 

Want to succeed with data governance and AI? Get change right.

Managing changes within data governance can be tricky. As organizations grow and adopt AI, their data governance strategies need to evolve with them. Here are some best practices to help you make change management successful:

1. Establish a clear vision and goals: Before making any changes, it’s important to have a clear understanding of why the change is necessary and what you want to achieve. Are you looking to reduce customer service wait times and improve customer satisfaction with a chatbot? This will help you create a roadmap for the changes, such as adding features to obtain user consent for data collection and to mitigate bias in chatbot responses.

2. Communicate effectively: Change is disruptive. Communicate consistently and transparently with all stakeholders to manage expectations and address concerns. Consider your audience, channels, frequency, format, and point of contact for questions. By being transparent about changes, you establish a foundation of trust that will help support your organization’s future data and AI decisions.

3. Train and educate employees: It’s key to educate and train employees on new processes so they understand them and can implement them effectively. Ensure those who are first to interact with customers and get feedback — like customer service representatives — know how to collect and use customer data correctly. That chatbot you were looking to add? Your reps can confidently rely on the information it provides when they’re knowledgeable of how the data is being entered.

4. Monitor and evaluate: Change management is an ongoing process. Although predictive AI can help us plan for the future, there are some things about the evolving world of data and AI that we just can’t predict. That’s why it’s important to evaluate and adjust your change initiatives as you go.

Take the next step in creating a trusted, scalable data governance solution.

Go further with your data governance solution, and check out the Expert Coaching Unlocked: 5 Data Governance Essentials for AI Trust webinar. You’ll learn how to build a robust data governance framework, manage risks, and maintain transparency, all while fostering trust in your AI-driven ecosystem. Plus, get the most out of your data governance solution and its many benefits with the Premier Success Plan. With Premier, you’ll get the help you need to build a fast path to success with expert guidance and expedited support, so you can confidently build AI solutions your customers can trust.

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