What is Data Curation?

Explore the realm of data curation, a critical process in the age of big data. Our comprehensive guide delves into its definition, key steps, benefits, and real-world examples. Learn how data curation transforms raw data into high-quality information for decision-making, strategic planning, and machine learning.

What is Data Curation?

There's an adage that's been doing the rounds in the tech industry - data is the new oil. The comparison might seem a bit extreme at first glance, but consider this: we're living in the age of big data.

Companies, institutions, and even individuals are generating mountains of data every day. But, like crude oil, raw data is often of little use in its original state. This is where the crucial process of data curation comes into play.

The Art and Science of Data Curation

Data curation is an iterative and complex process that involves the collection, organization, annotation, and maintenance of data. It's not just about storing, organizing data, or archiving it. It's about making it ready for usage, enhancing its value, and ensuring its longevity.

Data curation brings out the best in your data. It helps in turning it from a raw, untamed entity into a polished, high-value resource. So as to make it ready for analysis and decision-making for data analysts.

It's similar to turning coal into a diamond. As much as the raw material might be valuable, the end product is where the true value lies.

Data curators transform raw data into high-quality information for decision-making, strategic planning, and machine learning.

Who is a Data Curator?

Let's now shift our focus to the people who make data curation happen, the data curators. They work diligently to ensure that the data is in its best possible form.

A data curator usually comes from a background in information science, computer science, or a related field. They possess an array of skills including a strong understanding of databases, programming, and data modeling, and an eye for detail. They must also be adept at collaborating with data scientists, data stewards, and other stakeholders. In order to understand their needs and provide the data in a useful format.

Their primary responsibilities include data cleaning, data transformation, quality control, metadata creation, and maintaining data standards. They play a pivotal role in the data lifecycle, bridging the gap between data creation and data usage.

Data Curation Steps

It is not an arbitrary process. It involves multiple stages:

  1. Data Collection: Gathering data from various sources.
  2. Data Cleaning: Removing inconsistencies, redundancies, and errors from the data.
  3. Data Annotation: Adding metadata to make the data understandable and searchable.
  4. Data Preservation: Storing the data securely to ensure its availability over time.
  5. Data Review: Regularly review the data to ensure its accuracy and relevance.

Data Curation vs. Data Management

Data Management Data Curation
Definition Data management refers to the administrative process that includes acquiring, validating, storing, protecting, and processing data. Its objective is to ensure the accessibility, reliability, and timely delivery of data for its users. Data curation involves the process of organizing, integrating, cleaning, and enhancing raw data to create a high-quality dataset. This data can then be used for further analysis, decision making, or machine learning training.
Key Activities The key activities under data management include data storage, data backup, data retrieval, and data security. It's about maintaining the integrity and security of the data, safeguarding it from loss, and ensuring its availability when needed. Data curation involves data cleaning, data transformation, adding metadata, maintaining data standards, and regularly reviewing the data for accuracy and relevance. It's about enhancing the value and usability of the data.
Purpose The primary purpose of data management is to handle the operational aspects of managing data. It's like stacking books on the shelves in a library - ensuring they are safe and can be retrieved when needed. The primary purpose of data curation is to improve the value of the data. It's like cataloging books in a library - making them easily searchable, summarizing their content, and helping users find exactly what they need.
Benefits Data management leads to improved data security, better data availability, and compliance with data regulations. It ensures that the right data is available at the right time to the right users. Data curation leads to improved data quality and reliability, better data accessibility, and compliance with data standards. It allows organizations to derive valuable insights from their data, aiding in decision-making and strategic planning.

Data Curation Tools

Some of the industry-renowned tools:


Alation is a comprehensive data cataloging tool. It offers machine learning data discovery, data stewardship, and extensive metadata management, providing a single source of reference for your data.


Talend is a robust tool that integrates, cleans, and transforms data from multiple sources. Its user-friendly interface and a wide array of features make data curation seamless.


Secoda is a new kid on the block offering a tool for data documentation. It helps to catalog, classify, and document data assets, making it easier to find and understand data.


Collibra is an enterprise-wide data governance platform that offers capabilities like data cataloging, data privacy, and data quality. It empowers users to find, understand, and trust their data.


CastorDoc is a modern data catalog that syncs metadata across your data stack. Built for viral adoption, CastorDoc enables your whole team to find, understand and trust your data.

Benefits of Data Curation

Effective data curation comes with a host of benefits. It leads to improved data quality, making it more reliable for decision-making. It also enhances data accessibility, reducing the time spent on locating and understanding data. Furthermore, it enables better compliance with data regulations, reducing the risk of penalties.

From a strategic perspective, data curation can be a game-changer. It allows organizations to extract valuable insights from their data, driving innovation, enhancing customer experience, and gaining a competitive edge.

Data Curation Examples

A few examples of data curation in the real world:

Scientific Research

In the realm of scientific research, data curation is crucial. Let's take an example of a global climate change study. Researchers from different parts of the world contribute data in varying formats and from disparate sources. Some of them are - ice core samples, ocean temperature measurements, atmospheric carbon dioxide levels, and more.

A curator collects this diverse data, standardizes it, and annotates it with relevant metadata. It then makes it accessible for scientists to analyze and derive insights. This way, individual data points collected by numerous researchers transform into a cohesive dataset that drives understanding of climate change.

Health Care

Healthcare is another field where data curation is invaluable. Consider a large hospital system with multiple locations, each using different electronic health record (EHR) systems. A patient's data might be scattered across different systems, making it challenging to provide seamless care.

Here, data curators step in to integrate, clean, and standardize the patient data from data sources. This ensures a single, comprehensive view of the patient's health record.


In the e-commerce sector, data curation is essential for providing a personalized shopping experience. For instance, an e-commerce giant like Amazon collects data from millions of transactions and user interactions. Data curators refine this massive dataset, organizing, cleaning, and categorizing it. The curated data helps drive personalized recommendations, enhancing the customer experience and boosting sales.

Wrapping Up

In summary, data curation is a vital process in today's data-driven world. Transforming raw data into high-quality, usable information, empowers organizations to make data-driven decisions and thrive in the digital age.

As the data continues to grow exponentially, the role of data curation will only become more prominent. The need for skilled data curators and robust data curation practices is bound to rise!

Subscribe to the Newsletter

About us

We write about all the processes involved when leveraging data assets: the modern data stack, data teams composition, and data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation.

Or data-wise for the Fivetran, Looker, Snowflake, DBT aficionados. We designed our catalog software to be easy to use, delightful, and friendly.

Want to check it out? Reach out to us and we will show you a demo.

New Release

Get in Touch to Learn More

See Why Users Love CastorDoc
Fantastic tool for data discovery and documentation

“[I like] The easy to use interface and the speed of finding the relevant assets that you're looking for in your database. I also really enjoy the score given to each table, [which] lets you prioritize the results of your queries by how often certain data is used.” - Michal P., Head of Data