What is a data catalog? Why is it essential for businesses?

Business / Tech

In this article, we have compiled a list of the reasons why you should buy a data catalog.

Today, data is more complex than ever.

Even a decade back, reporting tools were not simple to use. We required qualified, technical individuals to access and manage datasets and develop valuable reports from practically nothing. Back then, security, governance, and organization of data were frequently controlled by the data analysts and businesses.

With the evolution in technology, however, data became easily available, and so did the tools utilized to report this data. The technological advancements in the field of IoT, cloud, and artificial intelligence increased the available data sources. But, privacy regulations and security guidelines also transformed. Currently, ensuring the security of the data structure and preserving sensitive information from hackers is a Herculean task.

Further, even with its easy availability, data management is becoming intricate, with emerging rules such as GDPR.

According to a statistics report, the data governance market is expected to boom and reach a staggering USD 2234.7 million by the year 2021. Undoubtedly, data catalog holds the biggest share in this market.

Considering the plethora of regulations and security guidelines, it is impossible to maintain compliance without a data catalog. GDPR alone is known to impose fines of up to €20. You need to justify why you are collecting certain data and how it is being used by your organization. Therefore, without organizing the data itself, how is it possible to justify the terms of GDPR and other such regulations?

In this article, we have compiled a list of the reasons why you should buy a data catalog.

data catalog

1. Automated, Intelligent Population of the Catalog

A study by Forrester Research says that most of the efforts of the businesses are spent on data integration and profiling/identifying sources of this data – 80% to be specific. This also means that you may be wasting 64% of your efforts in just searching for the right data for a particular project.

In reality, it is not humanly possible to tag all the data to the business terms. There is just too much data to handle manually. Even if you crowdsource this task, these individuals will have to take datasets you have acquired and tag them with attributes related to your business. This solution will also leave dark data behind, which has not been visited in the recent past.

A problem like this can be effectively solved with the help of technologies like artificial intelligence and machine learning. These technologies have the ability to process huge amounts of data and profile them. The relevant metadata and tags would be added to your data, and this data would then be efficiently cataloged for you to utilize at any time.

2. Ability to Ensure Tagging and Metadata Freshness

You can’t just tag your data once and then sit back and relax. Every minute, new data is being ushered through your doors, and you need to recurrently and incrementally tag this incoming data. To ensure that your data tags are up-to-date, your data catalog evaluates the data and tags it relevantly.

One of the major aspects related to data tagging is security tags, which include several security policies that are automatically populated by the data catalog software. This would help you scan the data in your enterprise for sensitive information, which should follow certain rules and regulations.

It is also possible to use tagging to improve your business collaboration. For instance, the marketing head of your organization is planning a campaign, and therefore, starts searching for datasets. After searching through a bundle of sets, she finds some datasets which may be relevant to the upcoming campaigns. Using data tagging or freeform tagging, she can tag the dataset, which can be later used by the team, without much hassle.

3. Enterprise Scalability

Scalability of a data catalog is an unavoidable feature. Fortunately, some technologies such as Hadoop, Spark, and Solr provide the functionality to scale. Initially, you can update and tag huge amounts of data without facing many hurdles. You can scale the needs of your organization based on the incoming data at any time. These technologies have an impeccable ability to process, profile, and automatically tag large data volumes in fewer hours.

Additionally, data catalogs support several data sources and datasets. For instance, cloud, on-premise, or hybrid data storage, and relational, unstructured, and semi-structured datasets.

Moreover, as your data catalog increases and enhances, more metadata and tags are included in it. You will require search scalability to effectively search through the catalog without lag. And an effective data catalog based on Solr can provide just that.

4. Data Lineage

The ability to analyze data, know its origin and generation, along with information related to datasets, is crucial for understanding data in the data catalog. It is through this information that we develop trust in data, believing that it will provide relevant insights whenever required. However, when you import data with the help of tools, like ETL, Apache Atlas, or other Hadoop platforms, the data lineage suffers from gaps.

These gaps leave loopholes in the analysis and evaluation of the data, which can’t be removed without filling in the gaps.

A strong data catalog system helps in resolving this bottleneck by suggesting the missing lineage dataset aspects. Once these gaps are filled and removed, you can search and find out how datasets were derived, and every other related aspect of this dataset.

5. Data Protection

Every industry has specific security guidelines and regulations regarding data use and storage. For instance, regulations like GDPR define a lot of factors related to stored data.

  • Till when can you keep certain data?
  • Who, including individuals and organizations, can access certain data classes?
  • What measures are being taken to protect this data?
  • Under what circumstances, you need to destroy data?
  • What privacy guidelines you should follow related to this data?

Thankfully, a data catalog integrates the relevant rules required for security architecture. Therefore, you are no longer required to implement a management structure to control authorizations related to data.

Conclusion

You most probably may already have an architecture and structure to manage security, tagging, and compliance of data. But, wouldn’t it be better if you have a data catalog which takes care of global rules and regulations as well?

You would gain the ability to manage, profile, collate, search, query, and browse this data to drive business excellence and thereby enhance productivity.

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