Data Governance

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• Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
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• Enterprise Architecture (EA)
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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

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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.

For example, generative AI can be used to identify inconsistencies, duplicates, or errors in large data sets, thereby improving the accuracy and reliability of the data. Moreover, generative AI can help synthesize new data to fill in missing values in a dataset, thereby enhancing completeness.

Problem	Generative AI Solution
Identify inconsistencies, duplicates, or errors in large data sets	Use generative AI to perform data validation and quality checks, and flag or correct any anomalies in the data
Synthesize new data to fill in missing values in a dataset	Use generative AI to generate realistic and plausible data that matches the distribution and characteristics of the existing data
Create synthetic data for testing or training purposes	Use generative AI to produce diverse and representative data that can be used to test or train models or systems without compromising privacy or security
Generate novel and creative data for various domains	Use generative AI to produce original and innovative data such as images, text, music, or code that can be used for entertainment, education, or research

Generative AI can significantly improve how we approach regulatory compliance by automating regulatory compliance processes and identifying potential compliance risks and trends ¹. For example, generative AI models can be trained to understand complex regulations and generate explanations, risk assessments, and recommendations to ensure that data practices align with these regulations ¹.

However, there are potential risks associated with its use, such as partial or incomplete underlying data leading to inaccurate or misleading results ¹.

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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

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(2) Designing data governance that delivers value - McKinsey & Company. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Designing%20data%20governance%20that%20delivers%20value/Designing-data-governance-that-delivers-value-NEW.pdf. (3) What is a Data Governance Model? - Digital Guardian. https://www.digitalguardian.com/blog/what-data-governance-model. (4) Data Governance Frameworks, Models & Best Practices - Claravine. https://www.claravine.com/resources/data-governance-framework/.

Source: Conversation with Bing, 5/28/2023 (1) Generative AI for Compliance | The Ultimate Guide. https://www.xenonstack.com/blog/generative-ai-compliance. (2) Managing generative AI risks: PwC. https://www.pwc.com/us/en/tech-effect/ai-analytics/managing-generative-ai-risks.html. (3) Early thoughts on regulating generative AI like ChatGPT - Brookings. https://www.brookings.edu/blog/techtank/2023/02/21/early-thoughts-on-regulating-generative-ai-like-chatgpt/. (4) Is Financial Regulatory Compliance at Risk from Generative AI?. https://www.eastnets.com/newsroom/is-financial-regulatory-compliance-at-risk-from-generative-ai.

Source: Conversation with Bing, 5/28/2023 (1) Harnessing the Power of Generative AI for Modern Data Governance - LinkedIn. https://www.linkedin.com/pulse/harnessing-power-generative-ai-modern-data-governance-tony-hoang. (2) The AI Era Is Here. Is Your Data Governance Ready? - WSJ. https://deloitte.wsj.com/articles/the-ai-era-is-here-is-your-data-governance-ready-01614196928.

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

Spark+AI Summit 2018 - Data Governance and Compliance FRANCISCO JAVIER SOTO SUAREZ Twillio Sri Esha Subbiah, Sunil Patil, and Jeechee Chen

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.

Vanguard: Empowering data scientists through data governance Learn more about IBM Unified Governance and Integration at https://ibm.co/2lZkMVn. Senior Solutions Architect at Vanguard, Jason Caplan, describes how data governance strategies provide trusted data to data scientists and analysts for use in predictive analytics and new product development within the financial services industry.

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

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

Access Sciences: Data Governance series

Data Governance | About This webinar series provides in-depth information about data governance and why it is critical to organizations. In this series, you will get a discussion of how data governance can help your organization; a clear idea of the benefits and risks; a look at practical, real world examples; and the details of the components of an effective program. In addition, we will introduce the five characteristics of a data-driven culture and the Access Sciences 7-Point Data Governance Model© that can be utilized to design and develop a scalable, fit-for-purpose data governance program for any organization.

Data Governance | Keys to a Data Driven Culture provides in-depth information about data governance and why it is critical to organizations. In this series, you will get a discussion of how data governance can help your organization; a clear idea of the benefits and risks; a look at practical, real world examples; and the details of the components of an effective program. In addition, we will introduce the five characteristics of a data-driven culture and the Access Sciences 7-Point Data Governance Model© that can be utilized to design and develop a scalable, fit-for-purpose data governance program for any organization.

Data Governance | The Value of Governing Data provides in-depth information about data governance and why it is critical to organizations. In this series, you will get a discussion of how data governance can help your organization; a clear idea of the benefits and risks; a look at practical, real world examples; and the details of the components of an effective program. In addition, we will introduce the five characteristics of a data-driven culture and the Access Sciences 7-Point Data Governance Model© that can be utilized to design and develop a scalable, fit-for-purpose data governance program for any organization.

Data Governance | Components of an Effective Data Governance Program provides in-depth information about data governance and why it is critical to organizations. In this series, you will get a discussion of how data governance can help your organization; a clear idea of the benefits and risks; a look at practical, real world examples; and the details of the components of an effective program. In addition, we will introduce the five characteristics of a data-driven culture and the Access Sciences 7-Point Data Governance Model© that can be utilized to design and develop a scalable, fit-for-purpose data governance program for any organization.

Data Governance | Implementing Real Change provides in-depth information about data governance and why it is critical to organizations. In this series, you will get a discussion of how data governance can help your organization; a clear idea of the benefits and risks; a look at practical, real world examples; and the details of the components of an effective program. In addition, we will introduce the five characteristics of a data-driven culture and the Access Sciences 7-Point Data Governance Model© that can be utilized to design and develop a scalable, fit-for-purpose data governance program for any organization.