Imagine going to a library in search of a specific book with a piece of information needed to finish an assignment. Did we mention you’re on a deadline? Good luck.
This example may seem extreme, but for organizations that don’t have a data catalog, it’s not unfathomable. In either scenario, you might have a general strategy — say, searching alphabetically — but how do you know which book or data point to pull without a summary? And how can you be sure there’s not another resource that’s more up-to-date or comprehensive?
Obviously, this isn’t a fast or efficient process, but it’s also not a very likely one thanks to technology. Just as libraries have book catalogs, organizations with thousands of data assets increase speed and efficiency using data catalogs. So, what is a data catalog? Why do organizations need one? And most importantly, what capabilities are must-haves when deciding on a data catalog tool?
What is a Data Catalog?
A data catalog is an organized inventory of data assets that enables data consumers to locate, access and evaluate data in a centralized location for analytical and business uses. Data catalogs leverage metadata to allow data consumers to quickly search an organization’s entire data landscape, understand the data available to them and operationalize that data for insight-driving analyses.
Metadata is particularly powerful in serving up data that may not have initially been included in a data consumer’s search, but could be relevant to their purpose. This allows for deeper, more informed data analysis. Additionally, data catalogs are evolving entities; the ability to curate data and manage it in a centralized location enriches the data catalog and enhances the insights and results data teams are able to derive from it.
While this describes data catalogs broadly, data catalogs can vary greatly based on what they offer data consumers:
- Connectivity: Some data catalogs connect with a specific public cloud, while others have wide-ranging compatibility across clouds, databases and applications.
- Deployment: Data catalogs can be natively embedded within a data or analytics platform, or they can exist as standalone entities. Some standalone data catalogs are able to be embedded with an API for a more cohesive user experience.
- Audience: Data catalog end users can vary drastically, ranging from an application that pulls metadata from the data catalog, to a data engineering or analyst team, to a diverse set of roles throughout an organization.
The diversity amongst data catalog tools and capabilities can make data engineers’ jobs more challenging, particularly if they don’t own the catalog, by creating silos of business functions that complicate data integration and operationalization. To avoid this, each organization should weigh these factors to determine the best data catalog tool for their needs and end users.
Why Do Businesses Need a Data Catalog?
Research from IBM shows business leaders spend 70% of their time finding data, and only 30% utilizing it. What good is your data if it’s only being used to a third of its potential?
As more data becomes available on a second-by-second basis, industry-leading businesses increasingly rely on data analytics to drive strategic insights and gain a competitive edge. For example, if a company combines in-store shopper data, purchase history and cell phone data, they can serve a geo-targeted ad to a potential consumer at the point of purchase. The consumer is then more likely to buy that top-of-mind brand over a competitor that may be on the same shelf, but hasn’t delivered that time-sensitive nudge. Data catalog tools can help data teams derive these insights, which can then inform digital ad purchasing decisions; so, in effect, the data catalog enables more efficient and effective ad buying that retrieves a higher ROI.
What’s the proof businesses benefit from data catalogs? In addition to improvements in efficiency, collaboration, data security and general management, organizations drove 57% higher profits, 69% more revenue and 72% greater customer satisfaction when they implemented a data catalog and management system.
What Does a Data Catalog Do?
According to Deloitte, organizations rely on an average of 28 different sources for data and metrics. Multiply those 28 sources by the thousands of data sets within each, and it’s easy to understand why businesses need a data catalog. By organizing data from multiple sources into a searchable, centralized platform, data catalog tools enable data teams and other data consumers to locate, understand and utilize data more quickly and efficiently.
How do they do this?
- Searchability: The ability to sift through a data catalog using keywords and/or filters, such as object name, source or date modified, makes locating the right data easier. Many data catalogs automatically sort by relevance or viewing frequency, so the best data is readily available.
- Analytics: Data catalogs connect with platforms like Redshift and Amazon EMR, which access data sets within the catalog to produce data analytics that can be inserted into BI tools for reporting.
- Unified management: Data catalogs eliminate silos by providing a centralized location to categorically house an organization’s entire data collection. This enables a self-service user experience and removes the burden on data engineers to grant access to data consumers on a case-by-case basis.
- Protection: Integrating an automated data governance solution with a data catalog ensures data users can access data compliantly and securely, according to their needs. So, although everyone can access the same data catalog, only data consumers with the right permissions will have access to certain data sets, thereby protecting sensitive data.
How Are Data Catalogs Built?
With all the functionality data catalogs offer, building one may seem daunting. We’ve broken it down into six key steps to take the guesswork and complexity out of the process:
- Assess the metadata across all the organization’s databases to identify data tables, files and databases, then incorporate the metadata into the data catalog.
- Pull descriptions of all data points into the data catalog and create profiles so data consumers can understand data at-a-glance.
- Identify relationships between data across databases to create linkages within the data catalog that can make query results more robust.
- Track data lineage to understand origin data and its transformations over time to its current state. This can help troubleshoot analytical errors.
- Organize data through an intuitive system(s), using tactics such as tagging and/or sorting by user type or usage frequency.
- Implement data security measures, such as fine-grained access controls, data de-identification and data audit logs, to ensure the right users have access to the right information at the right time.
Many data catalogs are able to automate at least some of these steps, making the data cataloging process faster and more efficient.
What are the Must-Have Capabilities of a Data Catalog?
Building a data catalog doesn’t have to be an entirely manual process. In fact, most modern data catalog platforms automate data cataloging to some degree. Still, there are must-have capabilities for data catalog platform:
- Data Access Control: An automated data access control solution that either includes or can integrate with a data catalog will allow you to write policies and apply access controls at scale, accelerating your speed to data access and analytics in a secure, compliant environment.
- Search and Discovery: Searchability is one of the hallmark capabilities of data catalogs; it makes data catalogs self-service, enabling the democratization of data across an organization. Data discovery can also be achieved through tagging and filtering, allowing users to browse the data catalog using existing keywords or parameters.
- Metadata Curation: As organizations increasingly adopt a hybrid multi-cloud environment, in addition to traditional on-premises systems, a data catalog tool that can connect to and extract metadata from multiple databases, data warehouses, ETL, and BI tools, among others, is key to scaling data access in a centralized catalog.
- Automated Data Intelligence: Automated processes within data catalogs that incorporate machine learning and AI help avoid manual data tagging, classification and organization. These technologies can also leverage data usage and queries to link or assign business context to data assets at scale.
- Collaborative Data Use: An accessible data catalog allows even non-technical data consumers to locate and utilize data, enabling collaborative data use across an enterprise. Capabilities such as group projects and data annotation further this collaboration, which enhances user efficiency and data utility organization-wide.
What is an Example of a Data Catalog for Data Engineering Teams?
There are numerous data catalog platforms, but few are all-in-one solutions that seamlessly integrate the five key capabilities mentioned above. Immuta’s active data catalog is built on a strong security foundation with always-on governance and access control, in addition to providing the standard core capabilities of a data catalog tool. This allows democratized, self-service user access to any data — even the most sensitive — while eliminating cumbersome approval workflows and breaking down access silos.
Compared to some data catalogs, Immuta focuses on data sources for analytics use cases with native policy enforcement for data access. It can act either as a standalone data catalog, or as a native integration with other cloud data platforms, including Databricks, Snowflake and AWS. Whether natively embedded or not, Immuta can serve as a centralized location for all your data assets.
Not only does Immuta’s data catalog offer automated tagging and classification, but it also includes sensitive data discovery tags. This means when new data enters your data catalog, it is scanned for sensitivity and, if detected, the proper policies are automatically applied. This takes three essential data catalog features — metadata curation, data intelligence and data governance — and combines them into one seamless process. In practice, this means that after registering data sources with Immuta, data teams can automatically classify and tag direct, indirect and sensitive identifiers for efficient human inspection, instead of having to manually comb through each data source, which introduces a high risk for error.
Immuta primarily serves data engineering teams, but can be deployed as curated data sets to data consumers, or as code that enables transparent policy enforcement. Its data catalog features an integrated project management toolset, which allows data consumers to initiate new data projects and work together in safe collaboration zones. Immuta Projects automatically equalize access rights for all members, who can then safely publish derived data sets back to the catalog. This achieves compliant data collaboration that can accelerate data analytics and speed to insights.
Finally, Immuta automatically applies the right policies to all published data to ensure its continued protection. Self-service workflows enable data consumers to request access to data sources, acknowledge approved usage purposes, request access control changes and propose new data collaboration projects. A unified audit log monitors data access requests and changes to data over time, so there’s never a question as to who accessed what data, when and what purpose. Reports are built into the data catalog capabilities, allowing data teams to answer compliance queries at any time.
With active, secure data catalogs, you can unlock the full potential of your data to drive business results. To increase the value, utility, accessibility and results of your data, as well as improve your teams’ efficiency and reduce manual workloads, investing in a data catalog is a critical business decision with positive, long lasting implications.
Ready to experience Immuta’s active data catalog for yourself? Request A Demo today.