Data Cataloging Process: Challenges, Steps, and Success Factors
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Investing in a data catalog is a significant first step toward harnessing your organization’s data effectively. However, to truly ensure its effectiveness, you need a solid data cataloging process.
In this article, we’ll delve into the best practices for the data cataloging process.
Table of contents
- What is a data cataloging process?
- Key challenges in the data cataloging process
- Essential Steps in Data Cataloging Process
- Measure Success in Data Cataloging
- Data Cataloging Process: Related Reads
What is a data cataloging process?
A data cataloging process is a systematic method of organizing, managing, and locating all your organization’s data sources into a single, searchable repository.
The quality of this process can significantly impact the effectiveness and value derived from the data catalog.
The end result of this process is a comprehensive and continuously updated data catalog.
Key challenges in the data cataloging process
Three challenges can affect the data cataloging process:
- Finding all the data
- Creating a path to future onboarding
- Getting people to adopt the data catalog
Let’s explore each challenge further.
Finding all the data
One of the foremost challenges in data cataloging is wading through data silos and discovering the right data.
Given the sheer volume and diversity of data sources, coupled with the issue of data silos within large organizations, data identification can be daunting.
These silos also affect the understanding of changes to data and the impact on connected applications.
“Silos of data usually prevent organizations from understanding that changing one part of the system affects other, potentially remote parts of their system. Because the changes in the data do not flow from one dataset to the next, one observes change but is unable to track why and how changes cascade.”
Also, read → How do large organizations avoid data silos?
Creating a path to future onboarding
Another hurdle is creating a sustainable path for the future onboarding of data.
Integrating the data catalog with existing systems is crucial to ensure that any new data is immediately cataloged upon creation. However, existing systems and their connections can be complex.
That’s why choosing a data catalog that’s compatible with the rest of your data stack and offers a seamless user experience without compromising data security or privacy is essential. You should also look at the catalog’s scalability, extensibility, fee structure, and vendor support.
Getting people to adopt the data catalog
The success of a data catalog is also dependent on its usage by the stakeholders in the organization. It’s not uncommon for teams to resist adopting new technologies, even when these technologies can enhance their work.
“There are always some people who have their routines, and they just don’t want to change. That [attitude] persists as long as the organization permits it.”
Anticipating and overcoming these potential points of resistance requires comprehensive onboarding and training programs.
The goal is to acquaint users with the data catalog’s functionality and value and to address technical and cultural aspects of data cataloging.
Essential Steps in Data Cataloging Process
In response to these challenges, here are seven key steps to create an effective data cataloging process.
- Identify data sources
- Establish data ownership
- Define data contracts
- Configure your catalog
- Onboard data sources
- Foster a culture of data collaboration
- Integrate the data cataloging process into your workflows
1. Identify data sources
The initial step in the data cataloging process involves identifying and documenting all data sources.
This can be achieved through various methods. These include maintaining a spreadsheet with the details of each data source or adopting an Agile, sprint-based approach to data discovery.
The latter provides a dynamic, iterative method for data discovery, allowing for continuous updating and refinement of the data sources. Here’s how McKinsey advises data teams on using an Agile approach to data discovery:
“Identify the most important customer characteristics and activities across a range of business domains. Next, rank-order the opportunities identified and consider, for each, the levels of data governance, architecture, and quality required.”
Collaboration is crucial during this stage. Engage IT professionals, data engineers, and other data-related roles in your organization during the discovery process. Their intimate knowledge of the data landscape will aid in unearthing existing data sources, including those that may not be apparent at first glance.
2. Establish data ownership
After identifying data sources, the next step is to establish clear ownership for each data asset. Assigning an individual or a team with the chief responsibility for cataloging and maintaining a given resource ensures accountability and facilitates decision-making regarding that data asset.
This essential step not only delineates roles but also reinforces the value and significance of each data asset, highlighting its potential to contribute to the organization’s overall data strategy.
Also, read → Applying product thinking into data
3. Define data contracts
A data contract is a crucial agreement between the producer and consumers of a data product.
Similar to business contracts, data contracts define and enforce the functionality, manageability, and reliability of data products, ensuring clear expectations and preventing unanticipated issues.
According to dbt’s Tristan Handy, “contracts on top of interfaces and automated mechanisms to validate them are the foundation of cross-team collaboration.”
This is especially important in today’s context, where a shift toward distributed data ownership demands accountability from domain teams for their products.
4. Configure your catalog
The configuration of your data catalog involves the meticulous setup of several parameters to suit your organization’s unique data governance needs.
Defining user personas, business domains, and data projects is crucial. Personalization and curation can significantly improve user experience with the data catalog, enabling users to interact with the data in ways most relevant to their roles and tasks.
Next, it’s important to establish your data governance policies and procedures. These policies are instrumental in managing and protecting data assets. Proper configuration ensures that your data catalog is well-structured and user-friendly, thereby encouraging widespread adoption.
5. Onboard Data Sources
The onboarding process involves bringing each identified data asset into the catalog. This stage requires close collaboration between data owners, the data governance team, and data engineering to resolve any issues that may arise.
When onboarding data sources, you’ll also onboard metadata. Active metadata provides indispensable context for your data, such as data types, the data owner, time last updated, and other data type-specific attributes.
Furthermore, you will need to define data classifications to categorize the data based on specific attributes, such as sensitivity or business function. This enhances data discoverability and helps enforce data security policies.
A business glossary is also beneficial during this stage. The glossary defines business-specific terms and jargon, providing a common language across the organization.
6. Foster a Culture of Data Collaboration
Cultivating a culture of data collaboration involves defining clear procedures for how individuals and teams within your organization can work together on data assets.
These collaborative activities can range from editing queries and generating reports to defining new metrics. The key is to encourage teams to jointly contribute to and benefit from the data catalog, promoting a sense of shared ownership and responsibility.
To further support collaboration, consider leveraging features like embedded collaboration for direct communication within the context of the data asset in question.
Another approach to foster a collaborative environment is to enable the crowdsourcing of assets such as the business glossary. This encourages all stakeholders to contribute their knowledge and expertise. That, in turn, enriches the content, making it more reflective of the organization’s unique language and conventions.
Finally, consider defining approval workflows for managing the potential influx of contributions to maintain glossary content quality.
7. Integrate the Data Cataloging Process into Your Workflows
For your data catalog to be truly effective, it must be integrated into your daily workflows. This integration may involve critical changes to how data is handled. The aim is to keep metadata in your catalog updated with an active metadata management approach. This process can even be achieved through automated means in some cases.
Another essential part of the process is fostering a data-centric culture in your organization through training and data literacy initiatives. Beyond just the technicalities of using the data catalog, also emphasize the value of data and the role it plays in decision-making and innovation. Guides on data catalog training, data literacy, and fostering a modern data culture can be immensely helpful in this regard.
By integrating the data cataloging process into your workflows and fostering a data-driven culture, you ensure that the catalog is a living, evolving tool for data maturity.
Measure Success in Data Cataloging
Determining the effectiveness of your data cataloging process is critical to its ongoing improvement and success. You can gauge the success of your cataloging process using a variety of metrics, such as:
- Adoption Rate - A high adoption rate demonstrates that users find value in the data catalog and are incorporating it into their workflows.
- The number of Data Sources - This number demonstrates the catalog’s reach within your organization’s data landscape. Tracking the number of teams planning to onboard data sources in the next 3, 6, or 9 months can provide insight into the catalog’s future growth.
- Awareness: Surveys or informal discussions can provide insights into how well employees understand the purpose and value of the data catalog.
Monitoring these metrics over time can provide a clear picture of the catalog’s performance and help identify areas for improvement. Remember, the goal is not just to have a data catalog, but to have a data catalog that is effectively used and continually enriched.
Building and maintaining a data catalog is an essential process for any data-driven organization. Starting with the identification of data sources and establishing data ownership, we’ve explored how to set the foundation for your data catalog. The process includes defining data contracts and configuring your catalog to suit your organization’s unique data governance needs.
A well-crafted data cataloging process is more than just a series of steps. It’s a journey toward empowering your organization with the knowledge it needs to make data-driven decisions, drive innovation, and achieve strategic goals. It’s about turning your data from a raw resource into a highly valuable asset.
Data Cataloging Process: Related Reads
- What Is a Data Catalog? & Do You Need One?
- AI Data Catalog: Exploring the Possibilities That Artificial Intelligence Brings to Your Metadata Applications & Data Interactions
- 8 Ways AI-Powered Data Catalogs Save Time Spent on Documentation, Tagging, Querying & More
- Data Catalog Market: Current State and Top Trends in 2023
- 15 Essential Data Catalog Features to Look For in 2023
- What is Active Metadata? — Definition, Characteristics, Example & Use Cases
- Data catalog benefits: 5 key reasons why you need one
- Open Source Data Catalog Software: 5 Popular Tools to Consider in 2023
- Data Catalog Platform: The Key To Future-Proofing Your Data Stack
- Top Data Catalog Use Cases Intrinsic to Data-Led Enterprises
- Business Data Catalog: Users, Differentiating Features, Evolution & More
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