What Is Data Cataloging and Why Is It Important?

Data availability has grown as companies become data-driven. Discover how data cataloging can help.

7

In this day and age, data is king. But with countless terabytes of data existing in businesses all over the world, it’s relatively easy for your data lake to turn into a data swamp. That’s where data cataloging comes into play.

Becoming data-driven is a goal of many modern teams. They need quicker and more accurate analytics without compromising on security. This further highlights the significance and difficulty of data management. The data management process can be simplified, and more needs can be met with the use of a data catalog.

But what is data cataloging and why is it important that businesses look into implementing the process? In this article, we’ll go through all you need to know about the process of data cataloging and even give some tips on what to look for in a great tool.

What is data cataloging?

Before we can discuss data cataloging, let’s first talk about what a data catalog is. A data catalog is a collection of metadata and search and management tools for data. It helps analysts and other data users find the data they need and gives them an idea of what data is available so they can decide if it will work for their purposes. So, in a nutshell, data cataloging is the usage of a data catalog — the process of creating and indexing organized inventories of your data.

Today, with big data and self-service business intelligence (BI), data catalogs are the gold standard for metadata management. The metadata we needed even a decade ago is not as comprehensive as what we require now. As the name implies, a data catalog is primarily concerned with datasets  — as in, the collection of all available data — and the knowledge that can be gleaned by linking them together. A data warehouse, data lake, or master repository enables teams to archive datasets. The majority of small and medium-sized businesses (SMBs), scaleups, and digitally-savvy companies rely on cloud storage to keep their data.

Insights are the biggest benefit of a well-organized data catalog since you can quickly find the information you need. To examine and analyze data sets with efficiency and certainty, it is helpful to have access to a data catalog that lists all of them.

When done correctly, data cataloging provides visibility into all data and serves as a single, authoritative source of truth. A data catalog is essential if your company wants to examine and make use of a massive amount of data that is constantly being created and collected.

Why is data cataloging important?

A recent study found that data professionals spend around 40% of their time gathering and cleaning data. That is, in essence, why data cataloging is a crucial procedure that all businesses should adopt. But there are many other reasons why companies should prioritize data cataloging in their processes.

Here are some of the main reasons why data cataloging is important:

1. Cut wasteful spending

It is possible to halve the time and money spent on data organization with the help of the right data cataloging tools. Don’t get us wrong; data catalogs are definitely investments with real costs to consider. Nevertheless, when used properly to drive the decision-making process at the appropriate time and to get a handle on your company’s data, these tools can save significant amounts of money and even increase business profits. 

Let’s quickly go back to the statistic we touched upon earlier. Imagine being able to cut down the time spent on looking for data by a significant amount — down to 15%, even. The cost of implementing a data catalog may at first seem like a barrier, but in fact, it can produce long-term savings. A data catalog amplifies your team's capabilities and efficiency, which in turn gives the company equipped with more resources concentrated on driving core business insights and getting things done.

2. Ensure consistent data quality

In order for you, your employees, and even your customers to put trust in your data, data quality is crucial. Nevertheless, most organizations still struggle greatly with poor data quality.

The necessity of time-consuming and error-prone manual operations is a major contributor to this situation. A powerful, automated modern data catalog can produce data profiles, classify data (particularly sensitive PII data), and spot duplication, abnormalities, and inconsistencies in data. Scheduled data quality checks can also ensure that your data is constantly up-to-date.

A modern data catalog serves as an organization's single, trustworthy source of truth.

3. Make sure you're compliant with regulations

The regulatory climate is expected to tighten more in light of the increasing rate at which technology is being digitized. According to Gartner, through 2024, 75% of the world will be governed by data privacy laws that include subject rights requests and consent — similar to what we already see with the European Union’s GDPR.

Therefore, data catalogs can be useful tools for data management in the service of guaranteeing regulatory compliance.

Metadata tagging in a modern data catalog allows for the automatic classification of sensitive material and stricter control over who has access to what assets. If you want to make sure that your data satisfies the requirements of regulations like the CCPA, HIPAA, PCI DSS, GDPR, and any other privacy law that may come to pass, compliance officers can work with your data team to keep a close eye on it.

Data cataloging means that any flaws or anomalies with private information can also be identified and fixed. If compliance officers discover that sensitive information is stored in an inappropriate location, for instance, they can rectify the situation by working with the data team to protect the information and reevaluate the company’s security.

4. Improve data accessibility

The data consumer can use a data catalog to perform a search for desired information. When a user needs information, they can get it whenever they want — all with a click of a button.

Data governance — the administration of data availability, integrity, usefulness, and security — is predicated on a set of guiding principles and internal regulations formulated around data. Data catalogs indicate the kind and placement of a company's data resources. Thanks to this, you can more easily track where your data comes from and where it goes.

Maintaining an accurate audit trail throughout an asset's lifetime requires tracking its history or whereabouts within an organization, both of which can be accomplished with the help of a data catalog and its data lineage capabilities. Here, you may keep track of all the updates made to a data asset and how those modifications affect related data sets.

Modern data catalogs also provide role- and asset-level permission granularity. Thus, private data is safeguarded because just the appropriate amount of access is granted to each user. According to a recent report, almost 25% of employees still have access to their accounts in former companies. Furthermore, a 2019 study by GetApp shows that 48% of employees have access to more company data than they need to accomplish their tasks, while 12% of those surveyed report having access to all company data. Honestly, that’s a surprising number. However, with granular permissions in place, you can control who has access to what, protect sensitive data, and make data available to more people who should see it.

5. Enhance data analysis

The greatest benefit of data catalogs may be the way in which they affect data analysis processes. When businesses successfully deploy a data catalog, they can rest assured that their data analysis will benefit greatly in both quality and efficiency. Your data team will be able to find and understand data more easily so that they can spend time using it in analyses rather than searching for what to use. They'll also have more context about the data – such as its popularity, when it was updated, owners, and where it's used – which will make it easier to know that they're using the best data for a given project.

And when done well, data analysis can result in happy consumers, additional users, increased revenue, and fruitful business decisions.

Effects of improper data cataloging

More data is available now than ever before, making it more difficult to find the right kinds of data when you need it. The EU’s GDPR is just one of several laws and standards that have been implemented, with many more in the pipeline for governments everywhere.

Therefore, both data access and data governance are getting more challenging. It is vital to know what kind of data you currently have, who is moving it, why it is being moved, and how it should be protected. You should also be careful not to surround the data with too many layers and wrappers, as this will make it difficult to use. 

That being said, there are risks of improperly cataloging data. Here are a few of those risks:

  1. Time and energy lost trying to locate and retrieve information
  2. Not easy to recycle existing data and information
  3. No method of recording past, inaccurate, or missing data (i.e., tribal knowledge)
  4. The deterioration of data lakes into data swamps

Different data cataloging methods

By now, you’re sure to want to avoid drowning in a data swamp and must be wondering how data cataloging is done. In recent years, data cataloging has gone into its third generation of offerings, and the services that businesses can choose from have only gotten better with each iteration. Overall, data cataloging approaches can be split into three main categories:

1. Internal

The internal approach relates to building data cataloging solutions in-house. Building an internal solution is generally only done when a company has large, sophisticated engineering teams — such as Uber’s Databook. However, some internal solutions, like Lyft’s Amundsen, have become open-source tools that are available for everyone to use. Speaking of open source…

2. Open Source

If your company has an engineering team that can focus on building, implementing, and maintaining a data catalog tool but doesn’t want to start from the ground up, then an open-source solution might be for you. Since open-source tools are built by engineers for engineers, it’s not the simplest solution, but it can solve universal issues faced by data teams and be customized (with your in-house engineers) from there. A dedicated team is required to manage these, as open-source tools tend to require significant time and resources to implement and maintain.

3. SaaS

The final method for data catalog management is through SaaS tools. These are perfect for companies seeking a plug-and-play solution with minimal internal resources required to manage. A great SaaS data cataloging tool takes significantly less time to implement and tends to provide features that can meet your needs out of the box — creating a better user experience overall.

Aspects of a great data catalog

With a clean, quick, and transparent data catalog, analysis is at your fingertips. Your data catalog should give your staff the tools they need to gain deeper insights from data and make rapid, informed decisions. This is the first step toward reaching your company’s data-driven goals.

But don’t just pick out any old data catalog tool! Doing your research is imperative in ensuring that you’re hitting the right data cataloging benchmarks. You should keep an eye out for these features:

1. Search functionality

Data science, analytics, and data engineering all necessitate users to access relevant sets of data quickly. For this reason, a data catalog's search and filtering capabilities should be highly intuitive so  you can explore data asset metadata and find what you need. Allowing users to add technical information, user-defined tags, or commercial phrases can help achieve better search functionality.

2. Metadata management

A good data cataloging system should allow its users to manage their metadata and have visibility of that metadata across the full data stack. This can be in the form of tags, associations, user-defined annotations, categories, ratings, and more. Good data catalogs should be able to ingest existing documentation (e.g., DBT docs), sync back, and allow you to add on top of it.

3. Data Lineage

Having access to a data set's transformation history and original source can give users peace of mind that they know where their data comes from and how it has been prepared for usage. When making critical judgments based on information, knowing where that data came from is crucial. This is why data lineage should be a key feature in the data cataloging tool you choose.

4. Automation and intelligence

Due to the massive amounts of data that must be processed nowadays, automation is becoming an indispensable asset that data cataloging tools need to have. We recommend tools that can automate documentation based on existing assets.

Final thoughts on data cataloging

As we know, the goal of many organizations is to become data-driven. They desire faster, more accurate analytics without compromising governance. And that's what makes data management crucial; however, with more data comes greater chaos. A data catalog makes it simpler to manage across your data stack and satisfy the needs of a business’ growing data lake — without turning it into a data swamp. Implementing a data cataloging tool will help you do exactly that and make your company’s data much easier to manage.

Subscribe to the Castor blog

Castor

Content Team

Linkedin Profil

More From Castor Blog

Get more value from the data you already have

Start your free 14-day trial now or schedule a product tour.
We have a flexible pricing that works for companies of all sizes.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.