Earlier in 2023, we discussed The Importance of Metadata here in the DBA Corner column. And, indeed, metadata is more important than ever before because it helps us to understand our data. Data without metadata lacks meaning, making it difficult to be used. Metadata gives data its form and makes it usable by your organization.
Nevertheless, it can be challenging to properly create, manage, and govern metadata. Part of the challenge is coupled to the sheer volume and variety of data being managed by modern applications and systems. The increasing volume of data generated in various formats can make it challenging to manage metadata effectively. As datasets grow larger, the task of cataloging and organizing metadata becomes more complex. And the variety of data in use adds to the challenge with diverse types of data such as structured, semi-structured, and unstructured data, managing metadata for each data type requires different approaches.
Disparate Information Sources
Another significant challenge to managing metadata is the wide variety of sources that make up the corporate data landscape. There is no escaping the fact that a significant portion of every organization’s vital data resides outside of its databases. In order to use metadata effectively, data managers must create a consistent and easy-to-understand format across everything from sophisticated, high-level ETL and BI repositories, down to rudimentary flat files.
Enforcing Business Rules for Metadata
Creating a context of enforceable business rules around the metadata is an important aspect of maintaining data integrity and usability. While most repositories do an excellent job of collecting the metadata, they only provide a two-dimensional view; that is, they can help you understand the data lineage and attributes, but they do not help you understand the relationships around the data—a key piece of clarifying the dependencies associated with the data. For example, a bank would want to ensure that any time an “account” entity is created it would have to have an account type—such as “asset,” “liability,” or “equity” associated with it.
Data Quality and Accuracy
Ensuring that metadata is accurate and of high quality is essential for making informed decisions. But data quality itself is poor, which can make it difficult to establish metadata quality. Only a meager 3% of companies’ data meets basic data quality standards, according to a Harvard Business Review article on data quality.
Inaccurate or incomplete metadata can lead to misunderstandings, misinterpretations, and poor decision-making.
Data governance is a set of practices, policies, and procedures that ensure high data quality, data management, data security, and overall data integrity within an organization. It involves the development and enforcement of policies and standards for managing, using, storing, and protecting data assets. The primary goal of data governance is to establish a framework that guides the proper management and utilization of an organization's data, ensuring that it meets business requirements, complies with regulations, and supports organizational goals.
Establishing and enforcing data governance policies is crucial for maintaining data integrity. Challenges may arise in defining and implementing consistent metadata standards, ensuring compliance, and managing data access and security.
Locating relevant metadata can be a significant challenge. Organizations may struggle with implementing effective metadata search and retrieval mechanisms, leading to difficulties in finding and using the right data.
Data architects and DBAs must effectively communicate with all the internal stakeholders who have access to, or who are using, data. As is evidenced in many different instances, if information about how to use data is hard to find or hard to use, it is likely that the data will either be misused or replicated with different standards and in a different format. Out-of-control application growth, poor standards, and insufficient communication are at the root of most data redundancy and inaccuracy problems.
For these reasons, clear communication is vital to leveraging metadata effectively.
Resistance to adopting metadata management practices and tools can be cultural or organizational. Some teams might view metadata management as an additional overhead, rather than recognizing its value in improving data understanding and collaboration. Appropriate and on-going training in metadata management, including its benefit to the organizations, can be beneficial to ensure that everybody understands the importance of metadata and contributes to its success.
Cost and resource constraints can also be problematic. Implementing effective metadata management solutions requires investments in technology, training, and resources. Budget constraints and resource limitations can hinder organizations from establishing robust metadata management practices.
The Bottom Line
Although there are significant challenges to successful metadata management, enabling a flexible IT organization that can gather, govern, and maintain metadata is the only way to ensure that business and regulatory needs are being met.
Addressing these challenges often involves a combination of technology, processes, and a cultural shift within organizations toward recognizing the importance of metadata in data management and decision-making. The bottom line is that to achieve success requires planning, architecture, and strategy.