Introduction

Data governance is becoming increasingly important in the age of artificial intelligence (AI) because organizations have come to rely more heavily on data-driven insights and automated decision-making. However, many companies realize they are not there yet. According to Precisely, a global leader in data integrity, “76% say data-driven decision-making is their #1 goal for data programs, yet 67% don’t completely trust their data.”

In my 10 Key Components of Data Governance Program, I claim, “Data governance is the foundation of all data management programs. It is an essential discipline that supports all other data management knowledge areas like Data Warehousing, Business Analytics, Big Data, Master Data Management, etc.” Because all of these knowledge areas have grown so complex over these past decades, the best tool for simplifying complex data environments — AI — has become a requirement for businesses rather than something that is nice to have.

In their Artificial Intelligence for the Real World, Don’t start with moon shots, Thomas H. Davenport and Rajeev Ronanki recommend companies “look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.” Both automating business processes and gaining insight through data analysis can greatly improve data governance.

Key Pillar of Data Governance

For EWSolutions, the three key pillars of data governance are:

  1. Data Quality: Ensuring that data is accurate, complete, and reliable.
  2. Data Management: Implementing processes like data mapping and classification to manage data effectively across systems.
  3. Data Security: Protecting data from unauthorized access and ensuring compliance with expanding data privacy regulations.

These three pillars should be the cornerstone of an organization’s overall data governance goals. They safeguard data integrity, ensure regulatory compliance, and facilitate self-service, data-driven analytics. AI enhances all three of these data governance pillars.

Dr. Marco’s YouTube “Why is AI Governance Important”

First Data Governance Pillar: Data Quality

Without complete, accurate, and reliable data, an organization will never run as optimally or as profitably as possible. High-quality data provides a reliable foundation for decision-making. It is essential for organizations to make accurate, informed decisions and drive business success. Data quality is the cornerstone of analytics, without it data modeling is not trustable. When a company’s data is accurate and trustable, organizations can gain valuable insights into their operation. This allows them to make business decisions with confidence. This can reduce business risk and as well as verifiably improve business outcomes.

In his article Observations on Data Quality, Sid Adelman claims, there are a number of indicators of quality data including, the data is accurate, the data has integrity, it is consistent, not redundant, and the databases are well designed. AI can enhance data quality by identifying and correcting inconsistencies or errors during the data integration process. It can keep the data accurate, consistent while ensuring it has integrity and isn’t redundant. Automated data cleansing and validation can ensure the integrated data is accurate and reliable, which is crucial for effective analysis and decision-making.

When business users have confidence in their data, they are more likely to rely on insights from these systems rather than making decisions based on gut feelings. Good data quality creates trust in the company’s analytics tools and business intelligence dashboards. It underpins an organization’s ability to make informed decisions, operate efficiently, and deliver customer value.

Second Data Governance Pillar: Data Management

AI tools can automate the mapping and transformation of data from different formats and structures into a more consistent format. This capability allows AI to understand relationships between datasets across a company’s departments, facilitating seamless integration. As AI learns from the data it processes, it continuously improves its mapping accuracy, further enhancing integration efficiency. AI provides contextual insights by analyzing the relationships between different datasets. This understanding allows for more accurate integration and interpretation of data, enabling organizations to leverage integrated information more effectively.

AI can bridge the gap between modern and legacy systems by adapting to various data formats and protocols. This adaptability ensures that organizations can seamlessly integrate historical data with new information, preserving valuable insights while modernizing their data infrastructure.

Robotic Process Automation

Robotic Process Automation (RPA) is an AI software application running on systems connected to an enterprise network that automates repetitive tasks helpful to data quality and data management. Gartner defines it as “a productivity tool that allows a user to configure one or more scripts (which some vendors refer to as ‘bots’) to activate specific keystrokes in an automated fashion. The result is that the bots can be used to mimic or emulate selected tasks (transaction steps) within an overall business or IT process.”

According to Precedence Research, “Robotic Process Automation Market Size, Share, and Trends 2025 to 2034, “The global robotic process automation market size was valued at USD 18.41 billion in 2023 and is expected to reach USD 178.55 billion by 2033, anticipated to grow at a noteworthy CAGR of 25.7% over the forecast period 2024 to 2033.”

RPA can significantly enhance corporate data governance by automating repetitive tasks related to data management, ensuring compliance with data policies. This can improve data quality. By integrating RPA into a data governance framework, organizations can streamline processes such as data classification, data monitoring, and even reporting. This will reduce the risk of errors, ensuring regulatory compliance, while also enabling more efficient data handling across all corporate departments.

RPA can also enhance data mapping by automating the extract, transform, and load (ETL) processes involved in data integration. It ensures accuracy and consistency by minimizing human error during data handling, standardizing data formats across systems, and executing complex data mapping tasks that adapt to varying data schemas without manual intervention.

AIOps

Coined by Gartner in 2017, ‘AIOps’ is a portmanteau of ‘Artificial Intelligence’ (AI) and operations (Ops). Gartner sees AIOps as a platform that utilizes “big data, modern machine learning and other advanced analytics technologies to, directly and indirectly, enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight.” The platform enables “the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies,” says Gartner.

AIOps processes and analyzes system data. It learns about a company’s day-to-day operation, proactively fixing what it can, and alerting others about any issues it can’t easily resolved. AIOps keeps a company’s system functioning properly while also proactively fixing any potential operational issue that might potentially arise.

By leveraging machine learning algorithms, AIOps platforms analyze vast amounts of data from various sources. It enables proactive risk management and continuous improvement of security measures, ultimately reducing the burden on human resources. AIOps can also enhance data security by automating threat detection and incident response. Security teams can then identify anomalies and respond to potential threats in real-time.

AIOps enhances data mapping by automating the collection, aggregation, and analysis of disparate data, providing a unified view that simplifies the data mapping process. By leveraging machine learning algorithms, AIOps can identify patterns and relationships within the data, ensuring accurate mappings and facilitating real-time updates to data structures as systems evolve, ultimately improving operational efficiency and decision-making across an organization.

Third Data Governance Pillar: Data Security

Cybersecurity threats, including malware, ransomware, phishing, distributed denial-of-service attacks (DDOS), man-in-the-middle attacks, drive-by-download attacks, exploit kits, SQL injection attacks, and social engineering scams are everywhere today. These threats will continue to evolve as long as there are bad actors in the world.

Thankfully, AI can considerably help with cybersecurity. Machine learning models can identify both known and unknown malware variants by analyzing data behavior patterns. In its article 6 Ways AIOps Optimizes Cloud Security, Vertis, a global IT solutions and services provider, says, “Behavior analysis is a promising use case of AIOps for cloud security. Analyzing endpoint and network behavior patterns helps security teams swiftly identify the subtle indicators of system compromise. This enables them to detect attacks and respond quickly to prevent breaches from occurring or restrict damages by confining attacks in the earlier stages.”

Proactive Risk Detection

AI can analyze historical data and real-time inputs then can predict potential vulnerabilities and future attack vectors. Machine learning can uncover hidden trends and patterns that may indicate weaknesses in an organization’s security posture. Embedding ML into cloud security can help uncover data abnormalities that might expose a threat to routine data management tasks. Data governance can be leveraged to monitor performance activity against external threat intelligence feeds. This includes vital information on malware, rogue code, ransomware, and suspicious internet protocol addresses across their cloud environments.

AI algorithms can rapidly analyze massive volumes of data to identify suspicious behavior and potential threats in real-time. By creating baselines for normal user and device behavior, AI can flag deviations that may indicate an attack, such as an unusual login attempt or weird data access patterns. This allows for immediate automated responses that can help mitigate cybersecurity risks.

AI-powered security systems constantly learn from the data they process, enabling them to adapt to continually evolving threats. ML models are regularly updated with information from recent attacks to improve threat detection capabilities. This allows the system to stay one step ahead of an ever-changing antagonist. AI can also back test a server’s secure status once it has been compromised. This creates detailed data forensics that can reveal where the problem lies and what type of breach occurred. Important information that can help build future cybersecurity defenses.

AI offers important benefits, but it’s important to note that today’s attackers are getting more and more sophisticated. Adversarial attacks even try to fool current AI cybersecurity systems. It’s important to strike the right balance between AI and human oversight in order to maximize the advantages of AI in data security. AI’s ability to learn and adapt by itself is a great advantage to have in the ongoing cybersecurity struggle.

Conclusion

As data volumes and network complexity grow, AI’s use in data governance will continue to grow. AI supports data governance by automating compliance, monitoring data usage to ensure that integration practices align with organization’s data guidelines. It provides the scalability needed to keep up with a corporation’s growth without sacrificing speed or accuracy. It also enables real-time processing of data, allowing organizations to integrate information as it is generated. This capability ensures that decision-makers have access to the most current insights, which is essential for timely responses to rapidly changing business environments.

AI technology continues to evolve, and data governance must adapt to keep pace with today’s fast-moving data environment. AI-specific considerations should be kept in mind when companies develop their data governance policies and processes. With all AI initiatives, collaboration is imperative. Cross-functional corporate teams should be created to govern any AI implementations. These should include members from IT, legal, as well as all business stakeholders.

Davenport and Ronanki claim, “AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.” Organizations leveraging high-quality data can uncover opportunities faster than competitors and better anticipate their customers’ needs. This gives them an edge in the market. By implementing robust data governance, organizations can unlock the full potential of AI for innovation and competitive advantage. For businesses today, data governance is not only becoming increasingly important in the age of AI, but an indispensible part of their operations.