Companies are accelerating digital transformation for varied business reasons – to drive innovation, build new business and customer engagement models, and enhance operational efficiencies through cloud modernization. The common theme across all these business priorities is the need for trusted data to enable agile, informed decision making. And this trusted data needs to be made easily available to data consumers across the organization. At the same time, they must ensure compliance with a growing number of industry regulations while protecting private and sensitive customer data. Customer loyalty and retention depends on transparent, responsible and ethical use of customer data. So, how do companies manage this difficult balancing act? That’s where metadata management comes in.
What is metadata management and why does it matter?
Today, organizations are dealing with a perfect storm – more data, more types of data, more users and more regulations than ever before. As they modernize in the cloud, the modern data environment is getting even more complex as data gets distributed across multiple clouds, on-premises and applications. To better understand all the information available in your enterprise and unleash its full value, you need context. Metadata provides this valuable context by helping you discover what data you have, reveal data relationships, understand how data gets used, and ultimately enable you to assess the value of the data and risks associated with its use.
Metadata management is the set of best-practice processes and technologies that allow businesses to manage their metadata (i.e., data about data) and derive insights for more effective data management.As data grows in importance as a strategic asset for all transformational business priorities, the ability for all data consumers – business, technical and operational – to easily find, understand and safely use the data they need for their business needs is becoming mission-critical. That’s why metadata management has quickly evolved from being an arcane technical discipline to being a strategic pillar of enterprise cloud data management at scale.
Examples of how metadata management enables intelligent data management
Besides providing valuable context to data users, metadata is also the key to enabling intelligence and automation in data management. Siloed approaches and manual processes will not scale to handle the needs of the modern, cloud-first digital enterprise. You need a holistic, integrated and intelligent approach to cloud data management. Having unified metadata management integrated into all cloud data management processes is the foundational building block you need to address this challenge. When combined with the ability to infer metadata beyond what’s directly collected and overlaid with AI/ML intelligence, it makes all cloud data management processes intelligent and dynamic, helping to drive agile, data-driven decision making at scale. Here are some examples that bring this to life:
- Automatic identification of relationships between data sets: Leverage metadata management to automatically identify data relationships across distributed data silos in an organization (e.g., similar and complementary data sets). For an analyst looking to identify data sets that should be joined together for analysis, this automation can dramatically reduce tedious manual efforts to find the right data sets.
- Transformation recommendations for data engineering: Analyze metadata from the organization’s data pipelines and offer design recommendations for data engineers building data pipelines for AI and analytics. This accelerates development, automates repetitive tasks and enables more types of users to quickly connect and integrate data.
- Automatic assessment of data quality: Automatically identify relevant data quality rules for data sets across distributed data and execute those data quality rules at scale. This is done by leveraging metadata management intelligence to automatically associate data sets with business terms, identify the relevant policies and data quality rules for each business term, and then efficiently apply the relevant data quality rules across the entire data estate.
- Automated sensitive data mapping and movement tracking: Automate the process of tracking, reporting and managing the risks associated with movement of sensitive data across the enterprise. For example, a violation might occur if personal data is moving from a source to a target across geographic boundaries, or if data onboarded for billing processes is now being proliferated to other departments or locations for marketing processes that may be in violation of privacy. Such metadata-driven automation is critical to efficiently supporting data privacy and security compliance requirements.
Seven Metadata Management Best Practices
To scale intelligent data management for the modern multicloud and hybrid data environment, you need seven metadata management best practices:
- Build a unified metadata foundation: Create a common metadata foundation to deliver insight and intelligence across all your data management processes.
- Tap into four major categories of metadata: Harness the full value of your data with metadata-driven intelligence across these four types of metadata: technical, business, operational/infrastructure and social/usage.
- Apply AI/ML to activate your metadata: Make all cloud data management processes intelligent and dynamic by applying AI/ML on top of unified metadata management.
- Leverage an AI-powered data catalog to make metadata visible and consumable for all employees: Capture enterprise metadata and add valuable business context to it so data has value throughout the organization. An AI-powered data catalog scans, catalogs and indexes metadata, helping consumers across your organization easily discover, understand and trust relevant data.
- Ensure breadth, depth and scale of metadata management: Meet modern data landscape demands with metadata management that has breadth of metadata connectivity across multi-cloud, on-premises, applications, ETL tools and BI tools; depth to extract deep metadata and understand detailed data lineage (e.g., by parsing code in databases); and massive scale to support hundreds of millions of metadata assets.
- Enable AI/ML model governance: Operationalize AI/ML models by capturing and managing metadata for the models. This enables governance of AI/ML models to measure performance against business KPIs and avoid algorithmic bias.
- Develop a metadata stewardship program: Include metadata stewards on your data governance team, who can take the high-level metadata management policies and implement them in practice. This is often a key indicator of a successful metadata management strategy.
Get started with metadata management
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