Metadata Management 101: The guide for data leaders
Gartner stated that : “By 2021, organizations will spend twice as much effort in managing metadata compared with 2018 in order to assess the value and risks associated with the data and its use.”
In this article, learn all you need to know about metadata management.
Definitions of Data and Metadata
For the majority of people, the concepts of metadata and data are unclear. Even though both are a form of data, their uses and specifications are completely different.
Data is a collection of information such as observations, measurements, facts, and descriptions of certain things. It gives you the ability to discover patterns and trends in all of an enterprise’s data assets.
On the other hand, Metadata, often defined as “data on data”, refers to specific details on these data. It provides granular information on one specific data such as file type, format, origin, date, etc.
The types of metadata
Technical metadata: describe the structure of a data set and storage information.
Business metadata: apply business context to datasets. Examples of business metadata include descriptions (context and use), the owners and referents, tags and properties, etc.
Operational metadata: they make it possible to understand when and how the data was created or transformed: Statistical analysis of data, date of update, provenance (lineage), volume, cardinality, identifying the processing operations that created or transformed the data, the status of the processing operations on the data, etc.
How do metadata bring value to an enterprise?
Metadata addresses different subjects, gathered within four different categories: Data Trust, Regulations & Privacy, Data Security, and Data Quality.
The implementation of a metadata management strategy depends on finding the balance between the identified business needs within the company and the regulations associated with data risks.
In other words, where should you invest your time and money? Should you democratize data access to your data teams (data scientists, data engineers, data analysts or data experts) to increase in productivity or to concentrate on the demands of regulatory bodies such as the GDPR, to avoid a hefty fine?
The answer to these questions is specific to each enterprise. Nevertheless, these categories were identified as top priority cases by CDOs, where metadata management should be the key:
- Data governance
- Risk management and compliance
- Data analysis
- Data value
Learn more about how metadata brings value to an enterprise
Why should you implement a metadata management strategy?
Here are, among others, benefits of metadata management:
- A better understanding of the meaning of enterprise’s data assets,
- More communication on a data’s semantics via a data catalog,
- Data leaders are more efficient, leading to faster project delivery,
- The use of data dictionaries and business glossaries allow the identification of synergies and the verification of coherent information,
- Reinforcement of data documentation (deletions, archives, quality, etc.),
- Generate audit and information tracks (risk and security for compliance).
How to successfully launch a metadata management strategy
Before starting a metadata management project, here are some elements to consider:
Accepting failure
As strong as this title may be, fearing failure won’t avoid it. Being aware of risk and knowing how to integrate it into the approach is a crucial part of launching a metadata management platform. To accept failure is to admit that the road will not be paved with simple and obvious steps.
Experimenting with your data environment
Metadata management is built gradually and no revelation will strike the team at its initialization. Only experimentation will make it possible to validate decisions. In order to control the costs of these various experimentations, the most appropriate approach is to progress step by step.
Aligning with enterprise objectives
The objectives of your metadata governance can be local or global. The approach may concern only a limited perimeter in the enterprise, and reflect a very local initiative, whereas conversely, it may be intended to apply to the enterprise as a whole.
Prioritizing
Among the benefits of such an approach is, as mentioned above, better risk control. But there is another obvious benefit: the possibility of a faster return on investment. The first effects should be noticeable as soon as the first iteration is complete.
Selecting the useful information
Being selective about the nature of the information characterizing the data helps with identifying your useful information. The temptation of an overly ambitious metamodel could actually be detrimental to the qualitative effort required of Data Stewards profiles.
Capitalize on your metadata!
Last but not least, your experimentation will result in local initiatives that may give rise to reflections on the generalization of all or part of the achievements.
Get more details on how to successfully launch a metadata management program
How do I start metadata management?
In many organizations, metadata management is still a manual, extremely time consuming task undertaken by more technical profiles for technical profiles.
As a result, metadata management as a discipline has gone largely unnoticed by data and analysis stakeholders. The ability of teams to explain its benefits or demonstrate its value has been and continues to be difficult.
Download our guide to start your enterprise metadata management journey!
In this white paper, we share our advice and expertise on implementing iterative metadata management optimized for your context.
Download now → https://info.zeenea.com/the-effective-guide-to-metadata-management/
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About Zeenea
Founded in 2017, Zeenea is a smart data catalog that connects an enterprise’s data storages to an intuitive and easy-to-use platform. It automatically imports and updates the metadata into a central data repository, identifying the relevant data and curating them. The tool allows anyone — with the allotted allowances — to discover, understand and trust in the enterprise’s data assets. Zeenea uses artificial intelligence and human collaboration to promote data-driven decision making. Zeenea promotes the concept of data-fluency: Be Data Fluent!
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