Data Stewardship in the era of Generative AI

AI is disrupting data landscape across domain led by rapid adoption of Generative AI. As recent AI systems are capable of creating accurate and convincing content, ensuring correct use of data has becomes even more critical. Data stewardship is a critical and evolving role. It involves managing data throughout its lifecycle, encompassing collection, storage, usage, and sharing.

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Data stewards ensure that data is usable, trusted, secure, and compliant with data policies and procedures across the organization. Data Stewards act as a bridge between the technology organization and business units. ?Data stewardship is all about liaising between a company’s engineering and business sides to ensure data quality, integrity, security, and governance. Responsibilities include identifying new policies, adapting existing policies to new use cases, and overseeing changes that affect existing data policies.

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Generative AI brings productive gains as well as new challenges for Data Stewards. As AI Models and Applications are unfolding, Data Stewards must define their top priorities including new skills and new commitments. Generative AI will make some of the traditional Data Stewards’ tasks simpler as AI offers numerous benefits in data management and in many other tasks. AI can unburden the workload of a data steward with AI-powered metadata enrichment, automated classification, tagging, compliance, and more. Data stewards can leverage automation and Generative AI to scale data asset documentation, classification, quality control, and compliance. Generative AI assisted data stewardship can reduce operational cost involved in data management, data governance & data quality assurance.

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Following are some of the key productivity gains for Data Steward with the application of AI

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Application of AI in Data Management

Generative AI models can be used identify data quality issues by analyzing vast amounts of data and patterns, resulting in fast issue resolution.? It can improve productivity of professional in the area of data management, governance, quality as well as catalog and lineage tasks.

Generative AI can operationalize data stewardship and assist in identifying and addressing privacy risks and anomalies within data sets. By analyzing vast data and patterns, these models can automatically recognize sensitive information or patterns that may violate data governance policies, helping to ensure compliance and maintaining data integrity — and reducing risks, breaches, and reputational damage.

Further to data management, generative AI can play a role in improving human insight on data issues. It offers immense potential for empowering organizations to improve data quality, privacy protection and utility — where data governance is not just a necessity, but also an enabling strategy.

Organizations can unlock the transformative power of generative AI while minimizing associated risks. With responsible practices and a comprehensive approach to end-to-end data management, organizations that are ready for the future can confidently navigate the data landscape, unlock insights and drive innovation in the ever-evolving digital era.

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Application of AI in Improving Data Quality

Without high-quality data, businesses risk making bad decisions. Improving data quality is an area where generative AI excels. It can generate synthetic examples that adhere to predefined standards and data quality rules and can provide recommendations for corrected records through error conditions. This capability is particularly useful when high-quality labeled data is scarce, as generative AI models can create additional instances that meet desired criteria, enhancing the overall data quality.

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Application of AI in Data Verification

Data stewardship will involve Data Verification to ensure the authenticity and integrity of the data to prevent the wrong results or in some specific cases spread of misinformation or manipulation.

Data stewardship will involve regular assessment and verification of data quality to minimize biases and inaccuracies that might influence Applications and Models.

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Application of AI in Data Quality Scoring

Data Quality Scoring is new emerging theme in improving correct usage of data for AI Models and Applications. Data consumed by AI Models and Applications can be of superior quality or inferior quality. However, slightly inferior quality data can be useful and should not be discarded. Therefore, need for Data Quality Scoring which involves quantifying the quality of data based on predefined criteria. It further involves assigning numerical or categorical scores to different aspects of data to measure its accuracy, completeness, consistency, reliability, and overall fitness. The aim should be to provide a standardized assessment to ensure right level of usage of data in AI Models and Applications.

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Application of AI in Data Cataloguing and Lineage

Often, data catalogs are incomplete, not up to date and inconsistent across disparate systems. By learning inherent patterns and relationships within data sets, generative AI models can facilitate automated definitions, categorization, and labeling. This results in better and efficient organization of data. Additionally, generative AI can generate visual representations or summaries of data, providing insights into the structure and content of the data catalog.

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Application of AI in Enhancing Data Architecture and Design

Creating schemas, tables and constraints for data ingestion and defining data transformation rules for common data elements is a time-consuming task. ?Generative AI models can assist in data design, architecture, and modeling and other extremely time-consuming tasks. It can generate controlled and conditioned data samples, allowing to create specific instances for testing, simulation, or compliance simulation. Generative AI can bring productivity by assisting in time consuming & repetitive tasks involved in Data model and ETL design. This will free up Architect to focus on more strategic task.

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Following are key challenges originating from recent advancement in AI

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Evolution of Data Management Practices

Recent advancements in AI Models and Applications introduces unique challenges to data management. Generative AI require new set of practices and standards to ensure ethical, responsible, and effective handling of data. Integration of new practices with existing data management practices is crucial. Compatibility, data integration capabilities and interoperability to ensure smooth collaboration is essential.

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Need of New Skills for New Practices

Re-skilling professionals with skills essential for emerging AI systems is essential for smooth transition and adoption of AI. Availability of required resources and training to leverage AI tools is critical.

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Data Ethics, Privacy & Legal Compliance

In the era of AI applications of data, Data stewardship will come with a new set of responsibilities. Data steward has to define ethical standards and guidelines for data usage in AI application, ensuring compliance with laws and privacy regulations. Evolving AI technologies require ethical best practices to adapt strategies and redefine it regularly.

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Data Governance Challenges

Implementing governance frameworks that outline data access entitlement for specific AI applications, responsible AI practices in data usage, robust techniques to anonymize sensitive data and usage of encryption methods to protect individual privacy are some of the key Data Governance Challenges.

Establishing clear policies, procedures and frameworks ensures ethical use, adherence to data protection regulations, implementing robust privacy protection measures to safeguard sensitive information and ensuring compliance with privacy regulations are critical & challenging.

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Bias Mitigation and Fairness

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Addressing biases inherited by generative AI models is essential to prevent unfair outcomes. Diverse and representative training data set and validation techniques would be required to mitigate biases.

It might require ongoing management and continuously monitor to ensure fairness and prevent discrimination in generated output. Incorporating diverse and representative datasets can reduce biases and enhance the inclusivity of AI-generated outputs.

Establishing robust governance frameworks, regularly assessing biases, and promoting ethical practices for trustworthy and inclusive outcomes are critical

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Security Measures

Robust cybersecurity measures to safeguard data against breaches and unauthorized access is another challenge. Especially considering the sensitivity of training data used in AI model is critical.

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Education and Awareness

Educating stakeholders about the ethical implications of generative AI, fostering a culture of responsible data usage and stewardship are another ongoing challenge.

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Accountability and Traceability

Data stewardship role will include ensuring accountability and transparency in data usage.? Maintain detailed records of data usage and AI model training processes for accountability and auditing purposes. Clearly define roles and responsibilities to ensure accountability.

Transparent and explainable

Transparent and explainable are crucial feature to maintaining trust and accuracy. There is need to develop methods and understanding to explain generative AI systems’ operations and the factors influencing their outputs.

Conclusion

Balancing innovation and responsible data stewardship in the era of generative AI demands a concerted effort from all stakeholders. It critical to uphold ethical standards and safeguard against potential risks associated with AI Systems.