Data Governance

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Generative AI is not an incremental improvement to Data Governance but a radical transformation of how we manage, protect, and leverage data
- The Artificial Intelligence Podcast | Tony Hoang


Data is the lifeblood of modern economies and societies, and how we manage this data effectively, responsibly, and ethically is crucial. To meet this challenge, a comprehensive framework called data governance has emerged. Data governance ensures that data is available, usable, secure, and of high quality and relevance, so that it can generate value for various purposes. Generative AI is a game-changer for data governance. It enables us to improve the quality of data, comply with regulations more easily, protect data from breaches and misuse, respect Data Privacy rights and obligations, and make better decisions based on data insights.


Most governance programs today are ineffective. The issue frequently starts at the top, with a C-suite that doesn’t recognize the value-creation potential in data governance. Leading firms have eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. Data governance is one of the top three differences between firms that capture this value and firms that don’t - Designing data governance that delivers value | McKinsey


Generative AI can significantly improve how we approach Data Governance. Generative AI can automate data governance processes such as data classification, data lineage tracking, and data access controls. For example, generative AI algorithms can automatically assign metadata tags to data assets based on their content, making it easier to categorize and manage data. Here is a list of key challenges with data where Generative AI can be applied:

  • Data quality control: Generative AI algorithms can analyze patterns and relationships in data to identify inconsistencies, duplicates, or errors. For example, generative AI can detect duplicate customer records by comparing attributes such as names, addresses, and contact information.
  • Data integration: Generative AI can automatically map and transform data from different formats and structures, facilitating the integration of disparate data sources. For instance, generative AI algorithms can learn to align and reconcile data from different databases with varying schemas and merge them into a unified format.
  • Data privacy compliance: Generative AI can generate synthetic data that retains the statistical characteristics of real data while ensuring Privacy. This enables organizations to share and analyze data without exposing sensitive information. For example, generative AI can generate synthetic customer profiles that accurately represent the demographics and behaviors of real customers while protecting their identities.
  • Data security: Generative AI can generate realistic and plausible data to identify potential security vulnerabilities. By simulating various attack scenarios, generative AI can help organizations identify weaknesses in their systems and proactively enhance security measures.
  • Data lineage tracking: Generative AI can assist in tracking the origin, movement, and transformation of data. By analyzing data flows and transformations, generative AI can ensure transparency and accuracy in data lineage. For instance, generative AI algorithms can track how customer data flows from different systems and identify any potential gaps or discrepancies in the process.
  • Data cataloging: Generative AI can generate metadata and annotations for data assets, facilitating data discovery and improving cataloging efforts. For example, generative AI algorithms can automatically extract relevant information from documents and assign descriptive tags to enable better search and retrieval.
  • Data quality assessment: Generative AI can analyze data patterns and identify anomalies that indicate data quality issues. For instance, generative AI algorithms can identify outliers in a dataset that may indicate measurement errors or data entry mistakes.
  • Data standardization: Generative AI can learn from existing data to develop rules and algorithms for standardizing data formats, schemas, and structures. For example, generative AI can analyze and transform data from different sources into a common format, enabling seamless data integration and interoperability.
  • Data deduplication: Generative AI can identify and eliminate duplicate records or instances in datasets. For instance, generative AI algorithms can compare data attributes across records and flag duplicates for further review or removal.
  • Data cleansing: Generative AI can assist in identifying and cleansing incomplete, inaccurate, or outdated data. For example, generative AI algorithms can analyze missing values or inconsistent entries and suggest data imputation or correction techniques.
  • Data lineage visualization: Generative AI can generate visual representations, such as flowcharts or diagrams, that illustrate the data lineage. This helps data stakeholders understand and navigate the movement and transformations of data across systems and processes.
  • Data anonymization: Generative AI can generate synthetic data that preserves the statistical properties of real data while removing personally identifiable information. This enables organizations to share data for analysis and collaboration while protecting Privacy. For example, generative AI can generate synthetic medical records that retain the statistical distribution of real patient data but do not reveal specific identities.
  • Data anomaly detection: Generative AI can analyze data patterns and identify unusual or anomalous data points that may indicate issues or fraud. For instance, generative AI algorithms can detect anomalies in financial transactions that deviate significantly from normal behavior
  • Data validation: Generative AI can perform data validation checks to flag anomalies or inconsistencies in the data. For example, generative AI algorithms can identify data outliers or discrepancies that do not conform to expected patterns, helping to ensure data accuracy and reliability.
  • Data discovery: Generative AI can assist in discovering new and relevant data sources by generating queries or keywords based on user intent. For instance, generative AI algorithms can suggest search terms or generate queries that help users explore and find relevant data sets or sources.
  • Data augmentation: Generative AI can generate synthetic data to augment existing datasets. For example, generative AI algorithms can create additional training samples, such as images, text, or audio, to enhance the diversity and size of a dataset, thereby improving the performance of machine learning models.
  • Data interpretation: Generative AI can interpret complex data models and generate explanations or summaries that are understandable and actionable. For instance, generative AI algorithms can analyze the results of a predictive model and generate explanations of how certain variables or factors influence the model's predictions.
  • Data visualization: Generative AI can generate visually appealing and informative data visualizations. For example, generative AI algorithms can create interactive graphs, charts, maps, or animations that highlight patterns, trends, and insights in the data, making it easier for users to interpret and communicate the information.
  • Data collaboration: Generative AI can facilitate collaborative data analysis by generating interactive visualizations or providing shared access to synthetic datasets. For instance, generative AI algorithms can create collaborative environments where multiple users can interact with and analyze data while preserving Privacy and security.
  • Data governance policy enforcement: Generative AI can help enforce data governance policies by automatically identifying and flagging data that violates established rules or guidelines. For example, generative AI algorithms can scan data sets for sensitive information or non-compliant data attributes and trigger alerts or notifications to ensure policy compliance.
  • Data context understanding: Generative AI can analyze the context of data, such as temporal, spatial, or relational aspects, and generate insights that enhance data understanding and interpretation. For instance, generative AI algorithms can analyze time-series data and identify seasonal patterns or correlations between variables.
  • Data change detection: Generative AI can monitor and detect changes in data patterns or distributions, alerting stakeholders to potential shifts or anomalies in the data. For example, generative AI algorithms can compare current data with historical data to identify significant changes or deviations, which may indicate data quality issues or evolving trends.
  • Data governance risk assessment: Generative AI can assess the risks associated with data governance practices, identifying potential vulnerabilities or compliance gaps and suggesting mitigation strategies. For example, generative AI algorithms can analyze data access logs and identify unauthorized or anomalous access patterns that may pose security risks.
  • Data governance performance monitoring: Generative AI can monitor and evaluate the performance of data governance processes, providing insights for continuous improvement and optimization. For example, generative AI algorithms can analyze data governance metrics, such as data quality scores or compliance rates, and identify areas that require attention or improvement.
  • Data lineage impact visualization: Generative AI can generate visual representations, such as graphs or diagrams, that illustrate the impact of data lineage changes on downstream processes. This helps stakeholders assess the potential consequences of data changes and make informed decisions. For example, generative AI algorithms can visually depict how modifications in a data source affect the results of analytical models or reports.
  • Data governance workflow automation: Generative AI can automate workflows and processes related to data governance. For instance, generative AI algorithms can automatically handle data request approvals, manage data access provisioning, or enforce data policy compliance, reducing manual effort and ensuring consistency and efficiency in data governance activities.
  • Data domain knowledge acquisition: Generative AI can learn from existing data to develop domain-specific knowledge and expertise. For example, generative AI algorithms can analyze large volumes of scientific research articles to extract insights, trends, and relationships within a specific field, enabling more accurate and context-aware data management decisions in scientific research.
  • Data classification and tagging: Generative AI can automatically classify and tag data based on its content, facilitating data organization, search, and retrieval. For instance, generative AI algorithms can analyze text documents and assign relevant labels or categories based on the topics or themes discussed in the text.
  • Data storage optimization: Generative AI can analyze data patterns and compress or optimize data storage, reducing storage requirements and costs while maintaining data fidelity. For example, generative AI algorithms can identify redundant or repetitive data patterns and apply compression techniques to store data more efficiently.
  • Data stream processing: Generative AI can analyze and process data streams in real-time, generating insights or predictions that enable timely decision-making. For instance, generative AI algorithms can continuously monitor streaming data from sensors in industrial settings and detect anomalies or predict equipment failures to support proactive maintenance.
  • Data analytics automation: Generative AI can automate data analysis processes, such as data preprocessing, feature extraction, and model selection. For example, generative AI algorithms can automatically clean and preprocess data, identify relevant features, and select appropriate machine learning models, reducing the manual effort and time required for data analytics tasks.
  • Data Privacy impact assessment: Generative AI can assess the potential Privacy risks associated with data handling and sharing practices. For instance, generative AI algorithms can simulate the re-identification risk of anonymized datasets and provide insights into the likelihood of individuals being re-identified, helping organizations evaluate and mitigate Privacy risks.
  • Data governance policy recommendation: Generative AI can analyze data governance policies, regulations, and best practices to provide recommendations and guidelines for data management. For example, generative AI algorithms can assess the impact of new Privacy regulations on data handling practices and suggest policy changes or compliance measures to ensure adherence to the new requirements.
  • Data governance audit and compliance: Generative AI can assist in auditing data governance practices and ensuring compliance with regulatory requirements. For instance, generative AI algorithms can analyze data access logs, data usage patterns, and user permissions to identify potential compliance violations or access control gaps.
  • Data quality prediction: Generative AI can analyze historical data quality metrics and patterns to predict the quality of new or incoming data. For example, generative AI algorithms can identify common data quality issues and their impact on downstream processes, enabling proactive measures to improve data quality.
  • Data similarity analysis: Generative AI can analyze the similarity between different data records or entities. For instance, generative AI algorithms can determine the degree of similarity between customer profiles or product descriptions, aiding in data deduplication, clustering, or matching tasks.
  • Data recommendation: Generative AI can generate personalized data recommendations based on user preferences, historical behavior, or contextual information. For example, generative AI algorithms can suggest relevant datasets, variables, or features for a specific analysis or modeling task.
  • Data-driven decision-making: Generative AI can support decision-making processes by generating insights and recommendations from large and complex datasets. For instance, generative AI algorithms can analyze data from multiple sources, identify correlations or patterns, and provide actionable insights to inform strategic decisions.
  • Data governance knowledge sharing: Generative AI can generate informative and educational content related to data governance. For example, generative AI algorithms can create documentation, articles, or training materials on data governance best practices, helping organizations disseminate knowledge and promote consistent understanding of data governance concepts.


Generative AI - The Big Change in Data Management
ChatGPT has become one of the main topics for nearly every online community, not just AI and Data Management communities. Thanks to Karin for sharing her journey with ChatGPT. How much has Generative AI changed my view of data management? Let's work through the points that ChatGPT raises about the change to data management

Unlocking the Power of Generative AI and Navigating AI Governance
Kevin and Lyn, discuss the implications of AI on Data Privacy and job roles. They also talk about ethical AI principles and demonstrate various technologies that affect Privacy.

Data Governance in Artificial Intelligence in 2019
How data Governance can help your AI transformation? What is the purpose of data governance in Machine Learning and Artificial Intelligence? In this video, we will describe and give you examples why data governance is critical to the success of your business, organization, and government. AI is not one-time learning, it is a long-term competency. Mind Data AI https://MindData.org

Data Governance and AI Governance | Transform Using Data & AI | Cognizant
To help organizations transform using data and artificial intelligence, Cognizant recently conducted a lively and thought-provoking conversation featuring Bret Greenstein, SVP and Global Head of Data & AI for Cognizant with featured guest J.P Gownder, Vice-President of Forrester Research Inc. View fresh thinking on how enterprises can approach data modernization to adapt, stay competitive and drive business growth. Watch the full webinar: https://cogniz.at/2UyEzcy Learn more about Data Modernization: https://cognizant.com/datamodernization

What AI Means for the Future of Data Governance and Big Data
Datum LLC Machine learning and artificial intelligence may well be the next frontier in business strategy. As companies across the globe race to understand how to operationalize AI concepts, vendors from every corner of technology are reacting by building (or claiming to have built) these capabilities. But before artificial intelligence can move past the hype, it must be measured from a business value and ROI standpoint and it must be trusted. This means it still requires data governance. We conclude our series by covering the future of data governance and Big Data in an AI world. Everyone has heard how important data is to the training and learning process so the efficacy of the data will separate winners from the pack, but few have identified how to truly connect it to value. In this video we’ll touch on four key points to know before getting started with AI, including the single most important factor for success, the critical skills practitioners will need to develop (yes, humans are still part of the picture), and how to make AI truly scale in the enterprise.

Data Governance and AI: New Dimensions in Privacy and Compliance | Dataiku & GigaOM
This webinar walks through practicalities of governance in the age of AI, including governance “checkpoints” for data scientists; the relationship between data regulations, ethics, and AI; and making ML models compliant, both with government regulations and corporate policy.

Data governance in the age of AI
Alpharithm Technologies

Non-Personal Data Governance Framework: Impact on AI Based Businesses
Voices at Esya Centre In this video we speak with Saket Gupta, Technical Architect at GreyOrange on the Non-Personal Data Governance Framework.

Implementing Data Governance with Knowledge Graphs

Data governance is the overall management of the availability, usability, integrity, and security of data used in an enterprise or organization. Data governance helps enterprises manage their information assets, ensure compliance with regulations, and generate value from data. However, traditional data governance approaches are often inadequate to cope with the increasing volume, variety, and velocity of data in the digital era. They may also fail to address the needs and expectations of various stakeholders and users of data. Therefore, a more powerful and flexible solution is needed for modern data governance. Knowledge graphs are a promising technology that can enable data governance by providing a holistic, dynamic, and semantic representation of data and its relationships. Knowledge Graphs for Data Governance.

  • Knowledge graphs are a form of artificial intelligence that use graph structures to store and query data and metadata. They can capture the meaning and context of data using ontologies, which are formal models of concepts and their relationships. They can also integrate data from multiple sources and formats using common identifiers and standards. Knowledge graphs can support data governance in three aspects: executive, operational, and technical. Executive governance involves creating controls, processes, and policies for data management. Operational governance involves implementing and enforcing the data governance policies and rules. Technical governance involves ensuring the quality, security, and availability of data. Knowledge graphs can help with all these aspects by providing a unified and consistent view of data across the enterprise, enabling data discovery and exploration, facilitating data analysis and insights, and ensuring data compliance and privacy.


Knowledge Graphs have great potential to transform Data Governance making AI increasing accuracy and making AI more effective efficient agile adaptable and scalable.


  • Implementation Steps and Best Practices: Implementing data governance with knowledge graphs requires a systematic and iterative approach that involves the following steps and best practices:
    • Establish a clear vision and scope for the data governance program. Define the goals, objectives, scope, and expected outcomes of the data governance program. Identify the key business problems or opportunities that can be addressed by data governance. Align the data governance program with the enterprise strategy and culture.
    • Identify the stakeholders and roles involved in the data governance process. Determine the roles and responsibilities of various stakeholders in the data governance process, such as data owners, data stewards, data consumers, data providers, etc. Establish a governance structure and communication channels for collaboration and coordination among stakeholders.
    • Choose a suitable knowledge graph technology and platform. Select a knowledge graph technology that meets the requirements and constraints of the enterprise, such as scalability, performance, interoperability, etc. Choose a knowledge graph platform that provides the necessary tools and features for building and managing knowledge graphs, such as data ingestion, integration, querying, visualization, etc.
    • Define the data governance policies and rules using a semantic model or ontology. Use a semantic model or ontology to define the concepts and relationships that represent the domain of interest for data governance. Specify the data governance policies and rules using formal logic or natural language. Ensure that the policies and rules are consistent, complete, and accurate.
    • Collect and organize the data sources and metadata using a data catalog or a master data management system. Identify and catalog the relevant data sources and metadata for the knowledge graph. Use a master data management system to ensure that the data sources are standardized, harmonized, and deduplicated. Enrich the metadata with additional information such as provenance, quality, lineage, etc.
    • Build and populate the knowledge graph using data ingestion and integration techniques. Use data ingestion techniques to extract, transform, and load (ETL) the data from various sources into the knowledge graph. Use data integration techniques to map, link, and merge (MLM) the data from different sources using common identifiers or standards. Ensure that the knowledge graph is updated regularly with new or modified data.
    • Enable self-service access and interaction with the knowledge graph using natural language processing and conversational agents. Provide user-friendly interfaces for accessing and interacting with the knowledge graph. Use natural language processing techniques to enable natural language queries and responses on the knowledge graph. Use conversational agents or chatbots to provide guidance and assistance to users on how to use the knowledge graph.
    • Monitor and evaluate the performance and outcomes of the data governance program using analytics and dashboards. Measure and monitor the key performance indicators (KPIs) and metrics of the data governance program. Use analytics techniques to generate insights from the knowledge graph on how to improve or optimize the data governance program. Use dashboards to visualize and communicate the results and outcomes of the data governance program to stakeholders.


How Implementing Data Governance with Knowledge Graphs Enables Enterprise AI
Artificial Intelligence (AI) and Machine Learning (ML) are umbrella terms for a wide set of algorithms, technologies, and approaches that make software seem “smart.” It is now commonly understood that knowledge graphs can help with many enterprise needs such as addressing key challenges of data governance. It is also becoming widely accepted that Knowledge Graphs are excellent at guiding and focusing ML and at serving as a unifying fabric for different AI algorithms. In this webinar we: • Provide a brief history of Knowledge Graphs • Demonstrate how they address key challenges of data governance • Give a concise overview of AI and ML technologies • Discuss how knowledge graphs provide a powerful platform for both integrated data governance and strategic enterprise AI/Machine Learning • Showcase specific real-world examples of how knowledge graphs support rules and learning that add new knowledge that can support further learning in a virtuous cycle

Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a data-driven organization
As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management. The Data strategy at JP Morgan intends to: a) generate business value b) adhere to regulatory & compliance requirements c) reduce barriers to access d) democratize access to data In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer: 1) What Data do I need? 2) What Data do we have? 3) Where does my Data come from? 4) Where should my Data come from? 5) What Data should be shared most? Presentation by Aftab Iqbal, JP Morgan Information Architect, at Connected Data London 2019

How does data governance help in the functioning of AI?

Data governance plays a crucial role in the functioning of AI by ensuring the availability, quality, integrity, and security of data used in AI systems. Data governance provides the necessary framework and guidelines to manage data effectively, ensuring that AI systems operate ethically, securely, and in compliance with regulations. It fosters trust in AI by enabling organizations to make informed decisions based on reliable data while mitigating potential risks.Here are some ways in which data governance helps in the functioning of AI:

  1. Data Quality Assurance: Data governance helps maintain the quality of data used for training and refining AI models. It establishes processes to ensure that data is accurate, complete, consistent, and relevant. By maintaining data quality, data governance enhances the reliability and performance of AI systems.
  2. Data Privacy and Security: Data governance frameworks incorporate Privacy and security measures to protect sensitive information. This is particularly important when dealing with personal data, as AI systems often require access to large volumes of data. Data governance helps define and enforce policies for data anonymization, encryption, access control, and compliance with data protection regulations.
  3. Ethical Considerations: AI systems must adhere to ethical standards, and data governance can help ensure that these standards are met. By defining guidelines and principles for data collection, usage, and decision-making, data governance helps prevent biases, discrimination, and unfairness in AI algorithms. It promotes transparency and accountability, making AI systems more trustworthy and responsible.
  4. Data Integration and Interoperability: AI systems often require data from various sources and formats. Data governance facilitates data integration and ensures interoperability by defining standards, formats, and protocols for data exchange. This enables AI models to access and combine diverse datasets effectively, enhancing the accuracy and breadth of insights generated by AI systems.
  5. Regulatory Compliance: Data governance helps organizations meet legal and regulatory requirements related to data usage. AI systems may involve sensitive or regulated data, such as financial, healthcare, or personal information. Data governance frameworks provide mechanisms for data classification, consent management, auditing, and compliance reporting, ensuring that AI systems operate within legal boundaries.
  6. Data Lifecycle Management: Data governance establishes processes for managing the entire lifecycle of data, including data acquisition, storage, retention, and disposal. By defining data retention policies and data archival strategies, data governance ensures that AI systems use relevant and up-to-date data while managing data storage costs and complying with legal requirements.
  7. Risk Management: Data governance helps identify and mitigate risks associated with AI systems. It establishes risk assessment frameworks to evaluate potential risks, such as data breaches, algorithmic biases, or regulatory non-compliance. Data governance also defines mechanisms for monitoring and auditing AI systems to detect and address emerging risks proactively.


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