What Is Data Governance?
Data governance is defined as the organizational framework that applies to how data is obtained, managed, used, and secured by your organization. Having a strong data governance strategy empowers an organization to trust the integrity of their applications and databases, including artificial intelligence and machine learning models, by ensuring the data originates from valid sources. Effective enterprise data governance also ensures that machine learning models are programmed to follow the organization’s policies and standards for data management and usage. Since most data used in artificial intelligence and machine learning efforts comes from multiple sources, having established a strong data governance program is essential to the success of any artificial intelligence or machine learning project.
What is the Internet of Things (IoT)?
The Internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines provided with unique identifiers (UIDs) and having the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
The objects may be “dumb” and only activated when they enter a predefined area or require scanning. Or, the objects may be “smart”, communicating and interacting over the Internet. These objects can even be remotely monitored, interacted with, and controlled.
IoT’s Impact on Data Governance
There is a rush to push IoT objects into the market, causing data volumes to continue to grow at a rapidly accelerating pace. The need for effective policies, practices, standards , and processes to manage this data is essential to the ability to realize the potential inherent in the collection of data by the IoT objects.
Much IoT data will not be in our control (e.g. a parent lets their teenager try out their fitness tracker for a week), so the trust for the actual data’s source and associated metadata should be questioned before using this data to make decisions.
There are some mitigating actions data governance can take to reduce the difficulty of managing the data collected by these IoT sources:
Ensure the data governance team is actively working with IoT engineers, and that the engineers understand the importance of designing with data governance / metadata / data quality best practices
Standardize and define the IoT data to be captured, and define the actual uses that data will undergo
Build IoT hardware and software correctly from the start with a focus on the proper management of data (data quality, metadata management, data integration, etc.) to allow an organization to integrate IoT data into their enterprise systems
Enhancing Data Governance with AI and Machine Learning Models
AI-enabled data governance enhances organizational capabilities by identifying and protecting sensitive data while maintaining compliance with global regulations like GDPR. Advanced machine learning models are particularly adept at data validation, flagging corrupted or incomplete datasets that often arise in siloed environments. Through model governance and robust training data strategies, organizations can improve the accuracy and reliability of predictive insights. For example, supervised learning algorithms can assist in detecting anomalies in data pipelines, ensuring seamless integration with enterprise systems. By reducing reliance on manual processes, AI fosters a proactive management approach that enhances efficiency and unlocks new business value from high-quality data.
Artificial Intelligence and Machine Learning Definitions
Artificial Intelligence and Machine Learning are not the same things. Each is a branch of systems development that share some characteristics, but they are separate domains.
Artificial Intelligence (AI) is defined as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention”.
Machines that mimic human behavior is a common vision for artificially intelligent objects, but the reality of creating such “human” robots or systems is still more science fiction than science fact.
According to research, 85% of business leaders believe that AI is a strategic competency for business as they work to discover the relevant business cases for this capability. Many organizations are adopting a strategy for incorporating AI into their business operations , but are neglecting the inclusion of a companion data strategy.
Machine Learning (ML) is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions. ML uses models, decision trees, neural networks, natural language processors, etc. to perform its operations.
Three major types of ML
Unsupervised Learning : learns from test data that has not been labeled, classified, or categorized. Unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.
Reinforcement Learning : enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The model does not have the answer.
Supervised Learning : input variables and an output variable use an algorithm to learn the mapping function from the input to the output. The goal is to approximate the mapping function so well that when new input data (x) arrives, one can predict the output variables (Y) for that data. Supervised Machine Learning, using labeled data with historical examples has demonstrated some success, and could provide a true disruptive force for data management professionals.
Data Governance with Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning revolutionize traditional data governance by automating complex and repetitive tasks, enabling more efficient management of data assets. AI technologies streamline processes such as data classification, policy enforcement, and compliance monitoring, reducing manual workloads. AI-driven models provide real-time alerts for potential compliance breaches, ensuring organizations stay aligned with evolving regulations.
For example, predictive analytics tools powered by AI enhance data quality checks and identify inconsistencies, promoting data accuracy and integrity across the board. By leveraging AI, organizations can dynamically adapt their governance framework to accommodate new data sources and regulations, safeguarding sensitive information and mitigating risks associated with data silos. Ultimately, integrating AI into data governance delivers both operational efficiencies and heightened security, reinforcing trust in enterprise systems.
Data Governance with Artificial Intelligence and Machine Learning
Data governance (and other areas of enterprise data management) helps an organization to manage five things about data: Availability, usability, integrity, and security of their data.
The practice of data governance starts with clearly defined data policies, data standards, data processes, and the identification of data stewards and data owners to support the development and implementation of these policies, practices, standards, for all initiatives. Good data policies manage the coordination of data management across the organization, data standards manage the quality of data and metadata for integrations and data lineage, and effective processes ensure that quality and consistent results can be sustained.
Additionally, following regulatory compliance obligations is an important to building and using a data governance program across the organization. Having a robust data governance program can support compliance with existing laws (e.g., the European Union’s Global Data Protection Regulation (GDPR)).
Managing data properly can save data scientists and machine learning engineers effort and time. Most of the time spent in Information Technology and data science is devoted to cleansing the data, and identifying data that can be used, connecting and integrating the right data. An effective data management program that includes data governance policies, metadata management standards, and data quality dimensions and metrics can reduce the time needed for an IT or data science project significantly.
Data Stewardship teams have the task of understanding data in their functional areas. Many large companies have over 10,000 applications with millions of data elements. To expect data stewards to oversee the data and metadata associated with the thousands of critical data elements is not realistic. Machine Learning, especially the supervised version, holds the promise of dramatically reducing the tasks of the data stewardship teams, allowing them to work on the critical issues instead of the simpler tasks that can be delegated to the machines / algorithms.
The Importance of Compliance and Federated Data Governance
Organizations face increasing pressure to comply with industry regulations, making robust data governance practices a crucial aspect of avoiding legal and ethical issues. Compliance frameworks like GDPR mandate data governance processes to ensure that data ownership is well-defined and access control policies are in place to protect sensitive information. Establishing these policies improves data discoverability for authorized users while safeguarding against unauthorized access.
Addressing data quality issues caused by corrupted data from silos is vital to maintain data consistency and prevent costly disruptions. By adopting federated data governance, businesses can implement solutions tailored to their unique business domain, enabling seamless integration of data sets across departments. This approach reduces operational risks while ensuring compliance with evolving regulations.
Furthermore, robust data governance enables regular audits and data preparation that align with model development and performance metrics in machine learning applications. For industries such as the financial sector, compliance measures help mitigate risks and reduce penalties, securing both operational integrity and business value. Implementing these strategies ensures a forward-thinking, regulation-ready approach to managing organizational data.
Model Governance in AI/ML: Frameworks for Success
Effective model governance is essential for organizations deploying machine learning systems. It ensures model performance is consistently monitored, enabling greater visibility into model behavior in production environments. A robust governance framework incorporates risk assessment strategies, helping organizations identify and mitigate potential issues before they escalate.
To improve data integrity and accountability, model documentation plays a crucial role by tracing decisions made during model development, testing, and deployment. Regular auditing of AI models for performance, bias, and compliance ensures adherence to regulatory standards and minimizes risks such as model drift and data inconsistencies.
Additionally, assigning clearly defined roles and responsibilities fosters collaboration between business units and technical teams, reducing duplication of effort and enhancing overall governance practices. This structured approach is particularly beneficial for enterprise customers in sectors like finance, where data security and model validation are paramount. By maintaining high standards of governance, organizations can effectively align their ML systems with operational goals and ethical guidelines, ensuring sustained business value.
Strengthening Data Governance Through Advanced Model Management
Incorporating machine learning governance into organizational practices is essential to ensure that ML models operate with integrity and transparency throughout their lifecycle. This involves clearly defining data ownership and establishing data definitions that align with enterprise goals and compliance requirements. By maintaining rigorous documentation for model versions, organizations can ensure traceability and prevent issues arising from outdated models.
Key strategies include leveraging model management tools to track and improve model accuracy while integrating advanced analytics to assess performance and uncover actionable insights. Regular testing during ML development helps identify potential biases or inaccuracies, fostering models that are both reliable and ethically sound.
Furthermore, managing data entry workflows effectively minimizes errors, ensuring high-quality inputs for machine learning technology applications. These practices not only enhance the reliability of models but also improve decision-making capabilities across various business functions, setting a foundation for scalable, future-ready AI solutions.
Value of Data Governance in Artificial Intelligence
Effective enterprise data management and data governance can use AI, ML, etc., to:
Reduce time to data cleanse and associate the correct metadata for contextual understanding
Improve organizational reliance on accurate, well-governed data to support expanded capability
Achieve higher quality and precision AI and ML, using good data to train machine learning or AI neural networks to create more precise decisions for the systems.
Faster and more efficient online inference by trained models that have “learned” through the application of the approved policies and standards
Conclusion
Data Governance is critical for a successful development and implementation of artificial intelligence, machine learning, and other emerging technologies. Without an effective enterprise data management initiative grounded in data governance, metadata management and data quality management, the promise of these capabilities will never be realized.
