Data Catalog for Business Outcomes: What Should You Consider Before Selecting the Right One?
Last Updated on: May 25th, 2023, Published on: May 25th, 2023
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A data catalog is the backbone of modern data management, enabling organizations to find, understand, trust, and use their data effectively. It is a centralized repository or database that contains metadata about an organization’s data assets. In this blog, we will look into what factors you must consider while selecting data catalogs that drive positive business outcomes.
Let’s jump in!
Table of contents
- Factors to evaluate when selecting a data catalog
- Driving business success: The power of active metadata capabilities in data catalogs
- Data catalogs for business outcomes: Related reads
Factors to evaluate when selecting a data catalog
In this section we will go over some key considerations when selecting a data catalog:
- Aligning with the company culture
- Understanding data governance needs
- Facilitating user adoption
- Offering a relevant and personalized experience
- Building trusting and productive relationships
- Meeting technical requirements
Let’s dive into these topics in detail:
1. Aligning with the company culture
- First, the team needs to identify the company’s cultural traits. Is the company collaborative? Does it have a learning culture, or is it fast-paced?
- For instance, if the company fosters a learning culture, a data catalog with strong educational and support resources, tutorials, and community forums may be highly valued.
- Conversely, if the company is fast-paced and results-oriented, the catalog’s ease-of-use and speed in returning data insights might be the most crucial factor.
2. Understanding data governance needs
- The team should map out their data governance requirements in detail.
- For example, if the company operates in a heavily regulated industry, such as healthcare or finance, the data catalog would need to support stringent security and compliance needs.
- Features such as access control, auditing capabilities, and data lineage may be critical.
3. Facilitating user adoption
- To encourage wide usage of the data catalog, it should be user-friendly and cater to both technical and non-technical users.
- The team can identify user personas within the company (e.g., data scientists, business analysts, or executives) and consider their specific needs.
- For example, data scientists may require a catalog that can integrate with their preferred data processing and machine learning tools. On the other hand, business analysts might prioritize one with robust visualization capabilities.
4. Offering relevant and personalized experience
- A suitable data catalog should be able to adapt to the user’s role and needs.
- This could mean presenting a data scientist with advanced analytical tools, while providing a business analyst with simpler, user-friendly visualization features.
- The group should consider the different roles that will interact with the catalog and evaluate if the catalog can be tailored to these various needs.
5. Building trusting and productive relationships
- The group should seek a data catalog that promotes transparency and collaboration. Transparency features could include tracking data lineage or usage statistics.
- Collaboration features might consist of shared workspaces, commenting or annotation capabilities, or integration with communication tools.
- These features would encourage productive relationships among stakeholders and establish trust in the data.
6. Meeting technical requirements
- This involves conducting a technical assessment of the data catalog against the company’s existing tech stack and future scalability needs.
- This might involve running a pilot or testing phase with the data catalog to ensure it can integrate smoothly with existing tools, handle the current data volume, and scale as data volume grows.
Finally, it’s worth noting that the process will often involve collecting feedback from potential users, and balancing different needs and constraints.
Driving business success: The power of active metadata capabilities in data catalogs
Active metadata capabilities in a data catalog can provide a substantial competitive edge when it comes to driving business outcomes for several reasons:
- Improved decision-making
- Efficiency and productivity
- Risk management
- Optimization of resources
- Better customer understanding
- Data democratization
- Regulatory compliance
- Competitive insights
- Operational agility
Let’s dive into these metadata capabilities that data catalogs should have in detail:
1. Improved decision-making
- Active metadata allows the system to understand and interpret the data it is handling, which can provide richer insights.
- When a business has deeper insights, it can make better, more informed decisions.
2. Efficiency and productivity
- Active metadata, unlike passive metadata, doesn’t sit idle. It is continually on the move, offering context, facilitating analysis, and even driving actions like automated alerts or resource allocation.
- This reduces the manual labor involved in data processing and analysis, increasing the efficiency of data teams and freeing them to focus on other strategic tasks.
3. Risk management
- Active metadata can detect anomalies or changes in data sets that may indicate data quality issues or security breaches.
- By identifying such issues early, companies can reduce their risk exposure, leading to a healthier and more reliable data infrastructure.
4. Optimization of resources
- By monitoring usage patterns and popular assets, active metadata can guide resource allocation and pruning of stale or unused data assets.
- This can lead to significant cost savings and ensure resources are directed towards high-value tasks and assets.
5. Better customer understanding
- Active metadata can help a company better understand its customers’ behavior and preferences, as it adds an additional layer of understanding to customer data.
- This can enable businesses to tailor their services or products to better meet customer needs, improving customer satisfaction and potentially increasing sales.
6. Data democratization
- Active metadata promotes data literacy across the organization by providing context and meaning to data sets.
- It helps users from various backgrounds and with different skill levels understand and interpret the data, fostering a data-driven culture.
7. Regulatory compliance
- Active metadata can aid in regulatory compliance.
- It can track data lineage, maintain audit trails, and provide insights into data usage, enabling a company to demonstrate compliance with data protection regulations.
8. Competitive insights
- By adding context and intelligence to the data, active metadata can provide insights about market trends, competitor behavior, and industry developments, giving a business a competitive edge.
9. Operational agility
- With active metadata, businesses can be more agile, adjusting quickly to changes or new insights derived from their data. This is because active metadata keeps data relevant and up-to-date, facilitating faster and more accurate responses to changing conditions.
In summary, by automating data management processes, enhancing data quality and security, and facilitating richer insights, active metadata can help a business become more efficient, agile, and informed, ultimately driving better outcomes.
A data catalog serves as a reference guide or catalog that provides information about the available data sets, their structure, content, and other relevant details. Ultimately, the chosen data catalog should align with the company’s culture, meet data governance requirements, be user-friendly, provide a personalized experience, encourage collaboration, and meet the technical needs of the organization.
Are you looking for a data catalog for your organization — you might want to check out Atlan.
Data catalog for business outcomes: Related reads
- What Is a Data Catalog? & Why Do You Need One in 2023?
- Essential Features of Data Catalogs
- Enterprise data catalog: Definition, Importance & benefits
- 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
- AI Data Catalog
- Benefits and Features of Snowflake Data Catalog
- AWS Glue Data Catalog: Architecture, Components, and Crawlers
- Airbnb Data Catalog Democratizing Data With Dataportal
- Lexikon: Spotify’s Efficient Solution For Data Discovery And What You Can Learn From It
- Google Cloud Data Catalog Guide - Everything You Need to Know
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