Data Discovery vs Data Catalog: 3 Critical Aspects

Akshaan Sehgal • Last Modified: April 26th, 2023

Data Discovery vs Data Catalog: Featured Image

In a variety of sectors, data-informed decision-making is swiftly taking over, and businesses are working to provide their data and business teams with easier, more approachable methods to access and use data.

Both data discovery and data cataloging intend to help you understand what you have and how it relates to other data. The former finds connections between various datasets both inside and across various (heterogeneous) data sources. It frequently serves as a tool for data asset discovery, test data management, and regulatory compliance (GDPR).

Data catalog tools offer a repository of details about a company’s data assets, including what data is kept, what format it is in, and to which (business) domains is data important to. These tools also employ data discovery behind the covers. The data is automatically gathered, and it can also be further categorized according to time, place, access control, and other factors.

Take a look at the detailed aspects of the data discovery vs data catalog discussion.

Table of Contents

What is Data Catalog?

Data Cataloging: Data Discovery vs Data Catalog
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A data catalog is a well-organized inventory of your data assets from all your data sources. Data cataloging helps companies in improving data discovery, comprehension, and consumption. All of your data, related metadata, and discovery tools are arranged, indexed, and easy to find for both business needs and data users when you have an enterprise data catalog.

A library is a typical metaphor for data catalogs. It serves as the perfect analogy since it is stocked with information assets (like books) and requires a way to manage them. In this example, books serve as the information asset, while details about the books, such as their title, author, ISBN, and genre, serve as their metadata. 

A data catalog is precisely what it sounds like—a catalog that is updated with information about the books, their location, and other things. It enables users to locate the list of available books, organize it according to their preferences, and easily choose the ones they need.

A good data catalog meets the following criteria:

  • Empowers Metadata
    You need data catalogs that can broadly discover, catalog, enhance, and utilize all types of metadata, enabling manual or automated usage of the same throughout your data stack.
  • Assembles and Combines Metadata in One Repository
    Modern data catalogs serve as the uniform access layer for all data sources and repositories and are driven by visual querying capabilities to enable equitable access to both technical and business users.
  • Facilitates Integrated Teamwork
    All data consumers have different tool preferences, skill sets, and procedures. Modern data catalogs are inclusively built to serve them all in their native environments because they realize the power of this variety.

Benefits of Data Cataloging

Data catalog offers a variety of benefits along with robust metadata and enhanced dataset searching. These include:

  • Greater Data Comprehension by Improved Context: With an enterprise data catalog, data professionals may access thorough descriptions of data, including comments from other team members, and better understand how data is relevant to the business.
  • Improved Operational Effectiveness: Data cataloging establishes an ideal division of labor between users and IT, enabling data citizens to access and analyze data more quickly while allowing IT employees to devote more time to activities that are of the utmost importance.
  • Reduced Risk: Enterprise data catalog reduces business risk by ensuring that data is only used for the intended uses and that it complies with company policies and data privacy laws. Additionally, your data professionals can swiftly scan annotations and metadata for empty fields or erroneous values that can affect their analysis.
  • Enhanced Performance with Data Management Programs: The more challenging it is for data professionals to locate, get, prepare, and trust data, the less likely it is that business intelligence (BI) projects and big data projects will be successful. A data catalog assists in finding new opportunities for cross-selling, up-selling, targeted marketing, and more by giving your data and business analysts a single, accurate, and comprehensive representation of their clients. 
  • Faster, More Accurate Data and Analysis: With analysis and solutions based on the most pertinent, contextual data available within your business, your data professionals can respond quickly to issues, difficulties, and opportunities and create a competitive edge.

A data catalog can help you in implementing data lake governance by fostering, streamlining, and automating governance. This prevents data swamps and offers the policy framework for creating, implementing, and supervising AI models with an emphasis on sincerity, accountability, safety, and clarity.

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What is Data Discovery?

Business intelligence has often been the domain of IT departments or domain experts running elaborate technology stacks. Business professionals can now prepare, load, and engage with data without the aid of IT thanks to the evolution of BI reporting and analytics technologies. Data discovery capabilities aid in breaking down data silos, removing bottlenecks, and making it simpler for users to comprehend and explore enterprise-wide information assets.

Data discovery tools enable business users to explore and interact with an organization’s data more easily, while also providing enterprises with a comprehensive view of their information assets. Without needing to acquire data modeling and other new abilities, even non-technical people can have insightful data. Users may engage with data and analytics via drag-and-drop capabilities, natural language interaction, and visual interaction thanks to data discovery technology.

Through visuals and drag-and-drop features, AI help enables people to access, prepare, and engage with data. They can utilize powerful analytics and machine learning frameworks through straightforward visual interaction and search data the same way they would browse the web. 

Users can easily clean up, integrate, and analyze large data sets using data discovery technologies, providing them with the expertise they need to come to insightful findings.

How Helpful is Data Discovery for Me?

Data Discovery: Data Discovery vs Data Catalog
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Data discovery helps firms in educating their workforce and enabling more users to combine and work with data from various sources and perform analyzes. 

With the right data discovery methods in place, data professionals spend less time looking for data and more time obtaining answers to their urgent issues. This reduces the time to insight. Using data discovery, your frontline data users are able to obtain the information they want without the assistance of IT. 

By enabling your decision-makers to combine huge amounts of different data it provides them a wider perspective. They also get to obtain better insights while uncovering new opportunities. Data discovery also fosters innovation and creativity. And ultimately, your business becomes more efficient and insight-driven as your data professionals becomes more comfortable with data.

What are the Benefits of Data Discovery?

Here are a few ways data discovery can benefit your business:

  • Facilitate visualization, merging, and migration of data across various data sources
  • Categorize sensitive data assets from massive amounts of structured and unstructured data automatically
  • Expand the varieties of data that can be analyzed, enabling the addition of more variables to data models
  • Identifying and swiftly analyzing real-time data in making informed business decisions
  • Foster governance and security. Platforms for data discovery can categorize data and pinpoint its organizational context, setting the groundwork for governance and security regulations

Data Discovery vs Data Catalog Comparison

We have broadly divided Data Discovery vs Data Catalog discussion into 3 broad aspects, let’s examine them:

Data Discovery vs Data Catalog Comparison: Working

Data discovery, along with data archival, test data management, data masking, and other techniques is employed when it is crucial to comprehend (referentially intact) corporate data entities that you are maintaining or altering. Because business analysts comprehend data at this level, putting a focus on business entities is also crucial for fostering communication between the business and IT.

Data discovery is crucial for master data management implementation because it makes it possible to find things like matching keys and offers precedence analysis. Understanding data landscapes might be referred to as one of the main use cases for data discovery. This is relevant to really large organizations with hundreds or thousands of databases that wish to comprehend the variety of connections between them.

Modern data catalogs, which may span multiple data sources (relational, NoSQL, and others) and serve to eliminate data silos, were initially created to function in association with data lakes. Users can thus have access to all data held by the company and are not constrained by the location of any data they may be interested in. 

Second, catalogs promote productivity by allowing self-service. They enable data professionals to do targeted information searches without involving IT, provided they have the necessary rights. Third, it prevents wasting a lot of time looking for information by finding it much more rapidly. Last but not least, catalogs help make sense of all this by bringing some order and structure to the environment, allowing users to understand what data is useful and what data is not, given the flood of data that many firms are being swamped with.

Data Discovery vs Data Catalog Comparison: Personnel 

Data discovery is crucial for chief information officers (CIOs) who want to comprehend their data landscape, as well as for anybody worried about data governance and compliance. As per studies, knowing the linkages found by data discovery is critical to data transfer.

Data cataloging is significantly important to the administration of data lakes (IT) and their use since it supports governance on the one hand and data preparation on the other (business people looking to exploit the information in your data lake).

Data Discovery vs Data Catalog Comparison: Latest Trends & Vendor Landscape

Whilst data discovery is an established technology, since the time GDPR was implemented, there has been an increased interest in using it to locate sensitive data. There are several notable variations across data discovery tools in this regard. 

For instance, some vendors can do database stored procedures inspection as part of the discovery process, but other vendors lack such capability. We anticipate that this disparity will close over time. In terms of data catalogs, there are an increasing number of providers who provide both data preparation and data cataloging. Although the former is not a big deal, there is a risk that you may wind up having many catalogs that are incompatible with one another. 

Vendors for data discovery and data cataloging come from a wide range of backgrounds. Data quality, data mobility, data masking, and test data management providers all frequently provide data discovery, while data preparation and analytics vendors, as well as both data governance vendors, may supply data catalogs. 

Many businesses provide a variety of these product categories. Since data cataloging is still a young field of technology, there are many companies on the market that provide either data preparation or production of data catalogs. This covers all providers offering business intelligence and analytics, not just the pure-play ones.

Today, data cataloging is a service offered by almost all data governance suppliers. Although some consolidation has already begun (Qlik acquired Podium Data), more are likely. We anticipate further consolidation since it is impossible to predict how many pure-play providers will remain in business over the long run.

What’s Next for Data Discovery vs Data Catalog?

When you have stringent models, data catalogs function fine, but as data pipelines become more intricate and unstructured data becomes the norm, our perceptions of this data—what it does, who uses it, how it’s utilized, etc.—do not correspond to reality.

Next-generation catalogs, we think, will be able to learn, interpret, and infer data, allowing users to exploit its insights in a self-service way. Data discovery, a novel method for assessing the condition of your dispersed data assets in real-time, must be included in metadata and data management plans in addition to cataloging data.

Data discovery holds diverse data owners responsible for their data as products and for promoting communication amongst scattered data spread out over many sites. Once a specific domain has received and processed data, the domain’s data owners can use the data for their operational or analytical requirements.

Data discovery reveals a real-time awareness of the data’s present state as opposed to its ideal or “cataloged” form, but more like a data catalog, governance rules and tools are federated across various domains (allowing for greater accessibility and interoperability). We believe that in the future we will see the best of both methodologies to overcome their shortcomings in form of a combined platform with all relevant functionalities rolled into one.

Final Thoughts

It is crucial to comprehend the condition and dependability of these most important assets as businesses use data to power digital offerings, drive decision-making, and spur innovation. Data catalogs have been used by companies as the foundation for data governance for many years.

Data discovery technologies can find data anywhere in your distributed infrastructure by automating the process of gathering characteristics about your data assets. This reduces the need for manual work. Additionally, it continuously updates metadata in real-time for both data at rest and data in motion (since data constantly transforms as it travels through your data pipelines). It offers simple-to-understand grades for data health and quality that are in line with the requirements of various users inside your business, in other words, providing more pertinent results for their data queries.

Looking at how the modern data stack is shaping up, it wouldn’t be farfetched to say that the emergence of a platform with ideals and features of data catalog and data discovery coupled into one set is assured. This, we expect would give complete visibility to data professionals to trust and leverage data and turn it into smart decisions.

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