Highlights:

  • Good data governance is essential for successful AI projects. It ensures reliable data and models, which are the building blocks of any AI initiative.
  • Monitoring data processes and pipelines is vital in today’s data-driven world, uncovering the importance of intelligent data observability.

In recent years, high-performing organizations are trying to be more data and AI-driven. As per the 2022 McKinsey Global Survey on AI, 50% of enterprises have adopted AI in at least one business area, and nearly 25% attribute at least 5% of their EBIT to AI.

Top organizations know that developing and utilizing AI can help them take out value from data to gain positive business results.

However, as organizations move from vision to reality, chief data officers face growing demands from stakeholders who aim to explore data and AI to foster business imperatives.

These stakeholders are searching for answers to questions about compliance, ethical and responsible AI practices, data sharing and democratization, and visibility into AI data pipelines. Modernizing data governance provides the solution to these challenges.

Three Core Principles of Modern Data Governance for Improved Data Quality

Data governance is considered vital for the success of any AI initiative, as it furnishes the foundation of trusted data and models necessary to drive these efforts.

However, with the growing prevalence of predictive and generative AI, a traditional perspective on data governance is no longer recommendable.

To stay competitive, enterprises need to build a modern data governance structure with AI that focuses on:

  1. Risk and compliance
  2. Data sharing and data democratization
  3. Intelligent data observability

  1. Risk and compliance

As data volumes and complexity have increased, so have the challenges related to risk and compliance. Organizations need timely, accurate regulatory reporting across disparate, siloed systems.

They also face new regulations, reporting laws, and mandates related to data protection and environmental, social, and governance (ESG) requirements.

The rise of predictive and generative AI has created additional considerations, that includes:

  • Improving data transparency: Ensuring transparency and explainability of AI decisions is vital for compliance with regulations like the EU’s GDPR, which demands openness in the usability of personal data.
  • Prioritizing data privacy: Enterprises must consider data privacy, as they frequently handle large amounts of sensitive datasets that are vulnerable to security breaches and unauthorized disclosure, with severe consequences.
  • Mitigating bias and drift in data and AI: Resolving bias and drift in AI systems is vital to ensure the reliability and integration of these technologies, which can otherwise hide their adoption and effect.

Finally, to streamline risk exposure without affecting innovation, enterprises must adopt a well-organized and modern data governance approach that furnishes convenient access to reliable data to facilitate business growth.

  1. Data sharing and democratization

If you are trying to unlock the power of AI, then you must break down data silos. Enterprises should make data quickly found, trusted, and used by every person.

This is essential because many data leaders face challenges with their fragmented data. Openly sharing data empowers both AI and human decision-making.

Also, reliable data fuels better AI models, while context helps users trust and leverage AI effectively. Collaboration and open information flow uncover creative problem-solving. Sharing data assets, context, and insights is necessary for success.

  1. Intelligent data observability

Keeping track of data processes and pipelines is very necessary in data-driven world, making intelligent data observability an essential pillar in modern data governance frameworks.

Data observability helps companies keep track of data usage, improve data flow, and ensure data security.

Also, it provides valuable insights into data pipelines for technical and business users, as well as governance teams, validating data integrity and transparency.

These technologies detect data quality problems early, automate data cleaning and verification, and use threshold alerts and scorecards to track performance and compliance. This ensures high-quality data is accessible and sharable.

Maintaining robust data quality is important for tech executives, as poor data can become a formidable obstacle. High-quality, well-curated data is essential for powering effective predictive and generative AI models. Moreover, comprehensive data observability is a vital component in achieving and sustaining this data excellence.

Essential AI-powered Capabilities for Modern Data Governance

AI-powered capabilities are critical for improving efficiency and ensuring data integrity and security.

Crucial AI Capabilities for Modern Data Governance-01-01

  1. Automated data quality and observability

Modernize data governance to automate data quality checks, allowing faster detection of issues in AI pipelines. This ensures reliable data for better decision-making at scale.

  1. Inferred relationships

Machine learning automates data connection discovery, saving time and improving data quality for trustworthy AI development.

  1. Sensitive data and data protection policy management

Data governance solutions with automated data discovery and protection are essential to manage sensitive data and comply with regulations, reducing the risk of data breaches.

Fostering Modern Data Governance with Predictive Data Intelligence

To get success with AI-powered data governance, use predictive data intelligence. This includes exploring metadata insights for recommendations and process automation.

Understanding data usage improves governance strategies and outcomes. Automation and AI streamline data management amid growing data use, connecting producers and consumers more effectively while lightening the workload for data stewards and IT teams.

A self-service data marketplace is needed for reliable data access, allowing users to request and access required data and AI models speedily.

Integration of delivery engines with data quality tools empowers efficient cataloging, governance, and data mastering, fostering easier access to reliable data.

Finally, integrating data intelligence with delivery helps data consumers access information swiftly for predictive and generative AI projects.

It drives new business opportunities, revenue growth, operational improvement, risk reduction, and enhanced customer experiences.

Final Thoughts

Future-proof your data by modernizing data governance. Utilize full-fledged automation and AI to manage risk, open sharing, and ensure data quality.

This empowers your workforce, fuels better AI, and uncovers digital transformation for continuous success.

Enhance your understanding by delving into various data-related whitepapers accessible through our resource center.