Ready to dive into the lake?
lakeFS is currently only
available on desktop.

For an optimal experience, provide your email below and one of our lifeguards will send you a link to start swimming in the lake!

lakeFS Community
Oz Katz
Oz Katz Author

Oz Katz is the CTO and Co-founder of lakeFS, an...

Last updated on April 26, 2024

Enterprises are updating their data platforms and accompanying tooling to meet the rapidly changing demands of data practitioners and build self-service capabilities for business teams. As the volume of data grows by the day, and the number of different data sources grows, so does the metadata connected to the data- and the need to manage it.

What is metadata? Why is it so important? And how do you manage it well? Keep reading to learn more about modern practices in metadata management.

What is metadata?

Metadata refers to data that describes data, encapsulating information about attributes, history, provenance, versions, etc. about a data asset. Teams use it for tracking, categorization, governance, lifecycle management, and analysis. 

Ultimately, metadata helps users and systems understand the meaning of data and plays a critical role in maintaining compliance with rules and data governance activities. 

Metadata contains information such as the data:

  • Origin
  • Meaning
  • Location
  • Ownership
  • Creation
  • Lineage
  • Version

For example, metadata within a digital image may include information such as the image’s size, resolution, time of production, and color depth. This comes in handy for data classification, structuring, labeling, sorting, and searching. 

A metadata repository is where teams store and manage metadata

The primary advantages are:

  • Increased availability of data intelligence that provides better context for insights
  • Reduced time it takes to obtain solutions during analysis
  • Increased efficiency in producing material for impact assessments
  • Removal of uncertainty in data linkages in the landscape
  • Simplification of data views by using meaning, detected redundancies, and relationships

Why do we need metadata? 4 use cases

Metadata has a number of use cases in data engineering and beyond: 

  • Data description and organization
    Metadata describes and organizes data resources in a repository. Teams can create new metadata by registering, cataloging, and indexing. 
  • Utilization and preservation
    It helps track the lifecycle of a data resource. Teams use it to keep track of changes, permissions, and data version control. Data should be subject to a continuous preservation routine and undergo processes such as refresh, migration, and integrity checks to ensure long-term availability. 
  • Search and retrieval
    Appropriate descriptive metadata makes it easier for users to locate and obtain the required metadata and data resource information. It enables teams to group comparable resources and differentiate incomparable ones.
  • Data generation, multi-versioning, and reuse
    Metadata is critical for the long-term preservation and accessibility of data resources. And for resource preservation and upkeep, teams need unique characteristics to identify the origin of digital assets, including a specific version of the originating data sets. 
    Exchanging resources like metadata harvesting and cross-system search is easy for teams that use known metadata schemes, defined transfer mechanisms, and crosswalks across schemes and APIs. 

Types of metadata 

1. Structural

Structural metadata contains information that helps you establish object relationships, with the ultimate goal of comprehending and successfully utilizing the data resource. 

It also contains information about the hierarchical structures that exist between various data resources. A table of contents, as well as page, section, and chapter numbers, are good examples of structural metadata. 

2. Descriptive

Descriptive metadata is useful for locating and identifying a data resource. It explains the what, when, where, and who of a resource – as well as the information on the data’s substance and context. 

It is well-organized and frequently follows one or more recognized standard schemes, such as Dublin Core or MARC. It may also specify the physical properties of the resource, such as its medium type and size. 

Teams use it to streamline processes like searching for and retrieving information at the system level. At the web level, it allows users to find resources by hyperlinking papers, for example.

3. Administrative

Administrative metadata offers information important to resource management and centers around governance, access restrictions, and security. It contains technical information on copyright, rights management, and licensing agreements. 

This may include technical data on: 

  • The development and quality control of works
  • Rights management
  • Access control
  • User needs
  • Action information preservation

Administrative metadata is managed via project-specific processes based on the local needs of the project and may include contract agreements and payment information. The archiving policy for administrative metadata can be used for internal resource management. 

4. Preservation

Preservation metadata is information connected to the management of collections and information resources for the goal of preservation and auditing. It entails documenting the process of maintaining physical and digital copies of resources. This type of metadata also includes all of the information required to manage and safeguard digital assets over time.

Preservation metadata in digital repositories may deal with rights management and provide information on the rights holders who permit such operations. It’s primarily concerned with the analysis and actions taken on a resource after it has been uploaded to a repository. 

5. Definitional 

Definitional metadata is information that offers a consistent vocabulary to facilitate a shared understanding of the data’s meaning. The data’s meaning comprises information on the data’s definitions, rules that control the data’s context, and computations. It may also include details on the reasoning employed while constructing derived data in order to fully comprehend its significance. 

Definitional metadata is divided into semantic and schematic categories. Teams can use textual descriptions or vocabulary to meaningfully characterize structured and unstructured data collections. Data sets from the former can be presented through a database schema. 

6. Provenance metadata

Provenance metadata provides information about a data resource’s origins. It contains information on data ownership, any transformations that the data may have experienced, data consumption, and data archiving. This metadata aids in tracking a resource’s lifespan. 

When you create a new version of a data collection, provenance information is generated, which reveals the link between all the different versions of the data items. Users can query the connection between versions and provide fine- or coarse-grained provenance data on data resources. 

Benefits for Data Engineers 

Metadata applications have multiplied in the past few years owing to technological developments and changes in regulations. This enabled data practitioners to take full advantage of metadata at their organizations and reap these benefits:

Data discovery 

Metadata helps to solve the problem data engineers know really well – answering questions such as: Does the data exist physically in schemas as objects and instances as elements?

It helps teams quickly find data in a single or several application systems-of-record or reference systems such as data lakes and warehouses.

Data administration 

Bringing together data management and governance, metadata enables teams to curate and identify data generation and processing processes. It also opens the door to adding people-related information, such as data owners, businesses, processes, and employees stewarding data. Another perk is that it aids in finding consistency in data ownership throughout an organization to manage context


This benefit corresponds to the notion of data interoperability inside and beyond an organization. Data usage methods include reporting, dashboards, and artificial intelligence models.

Data classification 

Metadata enhances data classification for better management. For example, it denotes the rate of change of data and its application: Master, Reference, and Transaction data.

Teams can use it to divide data into private, sensitive, and special categories. Classification labels may contain national identification, addresses, names, card-related information, and health information.

Rules operations 

These rules are an essential component of corporate metadata that may easily get overlooked in operational metadata procedures. The idea is to enforce better rule classification via business, policy enforcement, derivation and transformation rules, and others.

Data operations 

Metadata helps to understand data consumption for an enhanced data distribution management paradigm.

System privacy

System privacy profiling relates to data protection and privacy management conventions. Metadata helps in areas like application risk classification, including logs, and SOC operations.

Data access control 

Metadata also comes in handy for identifying and managing data entitlements in a single repository. Managing user groups, users, data access regulations, and owners who can give or cancel data access

5 metadata management best practices

Metadata enables you to understand the context, substance, and purpose of your data assets. Organizations use it to identify and use the data they need to make business decisions and achieve their goals – but this is only possible if you have a clear and structured system for managing metadata. 

This is where the management comes in. There are some general best practices that have been industry-proven and help to build a strong management practice, but each data management plan will be unique to the needs of your company.

1. Establish clear objectives and KPIs

Setting metadata management goals and KPIs that correspond with the organization’s vision is more important than you may expect. If you don’t have a goal and measurable milestones, getting buy-in will be hard because tracking your progress will be next to impossible. How can you show the value of metadata management initiatives when you have no point of reference?

Such metrics are key for ensuring that activities are tightly linked to broader business goals. Make objectives SMART (specific, measurable, achievable, relevant, and time-bound). And key performance indicators (KPIs) need to be closely aligned with them – they will demonstrate and prove your progress.

2. Establish a data governance plan

Developing a data governance plan is another important step in the process of defining the scope and direction of your metadata management. Data governance is used to make sure that metadata management initiatives are in line with its broader business strategy and goals. 

A data governance plan outlines how a company will manage and utilize its data and metadata to support its goals and produce value.

Defining the direction and emphasis of metadata management initiatives without a clear data governance plan is tricky. If you do that, you risk a lack of clarity and direction, or coming up with initiatives that are completely separated from the overarching aims and objectives of the firm.

A data governance plan can also specify the specific activities that must be performed to attain the organization’s goals through effective governance. This might involve developing rules and processes for maintaining and using data and metadata, establishing standards for data quality and integrity, and defining data management roles and duties.

3. Create a multi-functional data team

Creating a cross-functional team devoted to metadata management is a smart move. A team that includes members from both the business and IT teams works to develop a metadata process and strategy in line with the needs and goals of the whole organization.

Since businesses are frequently the key users of data and metadata, having their input guarantees that metadata management initiatives are thorough and successful. 

At the same time, the input from the IT team helps to make the process efficient, scalable, and in accordance with industry standards.

4. Adopt uniform standards

Speaking of standards, adopting metadata standards is key to assuring uniformity in the collection, storage, and use of data across the business. In other words, you can get value from metadata if you make sure that it’s uniformly structured and easily readable by all users by using standards.

What if you don’t standardize your data? You’ll quickly run into issues. Other people will struggle to interpret and use your data because they may not grasp what the different fields imply or how the data is arranged. This will inevitably lead to confusion and inaccuracies, increasing the time required to deal with the data. 

The lack of structure here also makes analyzing or interpreting the data more difficult since you may not have all of the essential information or be able to readily compare it to other data sets.

5. Increase the value of your metadata management tool

Most organizations use metadata management solutions that focus on a number of key areas, like search or storage. But make sure to pick a tool that has the capabilities you need instead of adapting your requirements to a solution. 

Start by developing your strategy and processes – and then move on to choosing your tooling. This is how you make the most of fully automated metadata management technologies.

Challenges in metadata management 

Some of the most common challenges include:

Relationships in metadata are missing

Typically, an engineer will go into the metadata management solution and examine the technical transformation rules (technical metadata) applied to a specific physical field name on a report under revision.

After reviewing this metadata, the engineer can check the system to identify the business rules set by the business users for that field. If there is an inconsistency between the transformation rules and the business rules, they can contact the data steward who established the relevant business rules and address the inconsistency.  

Correctly managed metadata bridges the gap between business and IT systems, helping everyone make better business decisions and derive more value from data.

Non-metadata professionals have built the solution

Some management systems are built by data practitioners who don’t specialize in metadata. A professional working in operational systems or data warehousing cannot be expected to operate in the metadata field if they lack the necessary expertise, training, and experience.

A metadata management system is not a data warehouse or system of operations. It’s essential that it’s developed with an architecture that allows these systems to grow. Managed metadata environments built with the future in mind will end up serving businesses for years to come.

Point vs. centralized solutions

Technical teams need functional metadata management systems as soon as possible. As a result, they may fall into the trap of building various separate metadata repository solutions, each of which is designed to tackle only one or two unique problems. 

Such point solutions are fast to launch, but their implementation and maintenance over time can quickly become expensive. Instead of centralizing the job, the organization duplicates its efforts across multiple divisions and sees considerable cost overruns. 


Metadata is the backbone of many complex data-driven features, such as data meshes and fabrics, as well as data lakes and warehouses. 

As people and machines around the world produce more data, metadata helps to keep track of these assets and gives each data collection a distinct identity. Organizations may use this technology to improve tailored services, data-driven security, and other areas. 

To learn more about current best practices in data engineering, check out our series about enterprise data architecture:

  1. OLTP
  2. Analytical Data
  3. Data Warehouse vs. Data Lake
  4. Data mesh
  5. Data governance

Git for Data – lakeFS

  • Get Started
    Get Started
  • Who’s coming to Data+AI Summit? Meet the lakeFS team at Booth #69! Learn more about -

    lakeFS for Databricks