Data Governance

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• Case Studies
  • Requirements Management
• AI Governance / Algorithm Administration
  • Data Science
    • Benchmarks
    • Data Preprocessing
      • Feature Exploration/Learning
      • Data Quality ...validity, accuracy, cleaning, completeness, consistency, encoding, padding, augmentation, labeling, auto-tagging, normalization, standardization, and imbalanced data
    • Bias and Variances
    • Master Data Management (MDM)
      • Managed Vocabularies
      • Datasets
    • Privacy in Data Science
    • Data Interoperability
    • Excel - Data Analysis
• Visualization
• Hyperparameters
• Evaluation
  • Evaluation - Measures
• Train, Validate, and Test
• Enterprise Architecture (EA)
• Enterprise Portfolio Management (EPM)
• Architectures supporting machine learning

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.