Metadata management is critically important in enterprise information management, but many companies are making key mistakes in implementing it, often building multiple disparate metadata repositories that lack integration and do not follow an overall metadata management strategy. Doing metadata management right the first time, by implementing a well-architected MME, is always less costly than fixing a fragmented approach later.
Common Metadata Management Issues and Challenges
Metadata management and its use in enterprise information management has become one of the critical information technology (IT) focuses for both global 2000 corporations and large government agencies. As these entities look to reduce their IT portfolio and control their escalating IT costs, they are turning to the technical functionality that it can provide them. The organizations that have built well-architected enterprise-wide MMEs have achieved a tremendous amount of success. Unfortunately, like most popular IT trends, companies are making key mistakes in building and implementing their metadata management investments. One of the chief problems is that they are not building one MME; rather, they are building lots of metadata repositories, none of which speak to each other and do not follow an overall metadata management strategy . There has been a proliferation of un-architected and disjointed metadata repositories, causing many problems.
Advanced Data Techniques
Explore cutting-edge approaches in modern data management and modeling to transform your organization’s data capabilities.
Metadata Issues and Their Impact on IT Systems: From Data Governance to Data Quality
The problem of disparate initiatives is not unique to metadata management; it affects many areas of IT, including data warehousing, enterprise resource planning (ERP), and supply chain management. These systems often suffer from needless duplication and redundant metadata repositories, which leads to significant metadata issues. Without a unified approach, organizations fail to maintain consistent metadata standards across data assets, resulting in poor metadata quality and disconnected data access processes.
The four most common problems associated with disparate metadata repositories are:
Missing Metadata Relationships : Fragmented metadata leads to an inability to track data lineage and relate metadata elements effectively. This lack of connection between data sets affects data usage and the ability to leverage metadata for accurate insights, creating a significant challenge in managing relevant data.Solutions Typically Built by Non-Metadata Professionals : When metadata management practices are designed without the right expertise, it results in poorly defined metadata fields, source code issues, and ineffective data processing. This hinders the ability to identify and store metadata elements in a meaningful way, affecting overall metadata discovery and usability.Costly Implementation and Maintenance : A lack of consistent metadata standards across datasets leads to complex and costly implementations. The duplication of efforts across various formats (structured, semi-structured, and unstructured data) creates inefficiencies, driving up maintenance costs and reducing the ability to establish metadata quality across the organization.Poor Technology Selections : Choosing inadequate tools or platforms for managing metadata results in a failure to meet data security and regulatory compliance requirements. This can expose sensitive metadata to risks and make it difficult to implement proper data access controls, putting data assets at risk and limiting the organization’s ability to track data lineage and ensure accuracy.
To address these challenges, organizations must adopt better metadata management practices by regularly reviewing metadata standards, ensuring data accuracy, and establishing proper metadata discovery processes. These steps are crucial for managing data effectively and aligning with regulatory compliance requirements.
Ensuring Effective Metadata Relationships Through Data Access Controls
There are different types of metadata (see Table 1: “Types of Metadata”) that must be properly managed and linked. For example, it is very valuable for an IT developer to have the capability to go to the MME to look at the technical transformation rules (technical metadata) that apply to a particular physical field name on a report that is being analyzed. Once the developer has reviewed this metadata, they can navigate through the MME to find the business rules defined by the business users for that field.
If a discrepancy between the transformation rules and the business rules exists then the developer could use the MME to contact the data steward who defined the specific business rules and resolve this discrepancy. This is the true power of properly managing metadata. It bridges the gap between business and the IT systems, since business operates through IT systems. When metadata is not managed from an enterprise perspective, this type of click-through analysis is impossible because the relationships between the metadata (both business and technical) are not captured or maintained.
Table 1: Types of Meta Data
Poor Technology Selections
It is common to find many disparate MME or repository-like initiatives. For example, one client has over 14 separate MME initiatives (either in production or under development), while another client has over 25 disjointed repositories. Most of these have significant monetary expenditures associated with them.
Disparate MME initiatives can come in many different flavors, sizes, and shapes. There are large repositories that utilize enterprise-level metadata integration tools or are even custom-built. In addition, there are lower technology metadata efforts that come in the form of spreadsheets. These, along with local databases, are the most popular form of metadata repository technology. Is this statement a surprise? Effective metadata technology solutions should be employed and the selection should be based on an enterprise metadata strategy.
Data Governance Essentials
Discover the foundational elements needed to build and maintain an effective data governance program within your organization.
Costly Implementation and Maintenance
Typically, business units and technical groups need metadata and properly functioning MME to operate their business and IT systems. Therefore, these groups will not be able to wait around for their company to start building their MME. They may not even have such a project in their current IT plans. As a result, they quickly build these disparate metadata repository solutions intended to handle only one or two specific problems/challenges. If executive management knew the cost of these metadata repository point solutions, they would see it far outweighs the cost of a truly sound enterprise-wide MME.
This situation closely follows the path that many companies have taken with data warehousing . For example, the average company needlessly replicates their business intelligence efforts across many lines of business, as opposed to centralizing this function. Experience shows that this approach can increase the costs of BI / analytics by over 300%. Metadata management has similar siloed initiatives with large cost overruns.
The types of organizations that build single-point metadata solutions often are concerned by the cost of building a MME. However, the cost of the MME would pale in comparison to the costs of all of the disjointed metadata initiatives that are currently underway or in production (see Figure 1). Doing it right the first time is always less costly than doing it wrong and trying to fix it later.
Figure 1: Meta Data Management Costs
Built by Non-Metadata Professionals
When government agencies and corporations create disparate metadata repositories, many of these systems are developed by individuals or consultants lacking the proper expertise in metadata management. This is often due to the influx of consulting and software firms entering the field with little experience. Metadata professionals, on the other hand, dedicate their careers to mastering the intricacies of specific metadata and its relationship to data sources, formats, and storage. Managing metadata requires a deep understanding of how raw data and an organization’s data should be properly stored, managed, and linked across other systems.
Database & Warehouse Solutions
Learn about critical database operations and strategies to mitigate risks in your data warehouse implementations.
An MME (Managed Metadata Environment) is not the same as a data warehouse or operational system. If an MME is built incorrectly, it will inevitably require costly rebuilding. In an article written for the Enterprise Information Management Institute in January 2000, the author stated:
“During the 1990s, corporations raced to build their decision support systems as quickly as they could…in their zeal to do this too many of these organizations neglected to build the architecture necessary to grow their systems over time.”
This scenario has now become a reality for MMEs, as companies rushing to implement these environments without a strong foundation will face similar issues.
To avoid the pitfalls of metadata silos and unscalable systems, organizations must adopt a central, enterprise-wide MME with consistent quality rules, privacy regulations, and data types. By using the right tools, regularly reviewing metadata formats, and creating a clear metadata strategy, companies can ensure that their datasets are properly stored, managed, and accessible across various platforms and users. This will provide a better understanding of their data capabilities and position them for long-term success.
155+
Successful Client Partners
25+
Years of Excellence
Transform Your Data Strategy
