What Is Data Curation: Examples, Tools, & Best Practices

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July 23, 2024

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20 min read

Preserving information that can generate impactful insights is an essential step for organizations, especially for those dealing with terabytes of data. Conversely, with vast amounts of data flowing through business, timely deletion of data is also necessary.

One common problem data practitioners face is organizing and maintaining preserved data for a certain period of time before its deletion. This process often involves adding additional information to the data and granting permissions to specific groups of people to access it. Data curation is a valuable practice in this context, offering a structured approach to managing your preserved data for easy access, analysis, and deletion.

This article highlights the data curation process, the steps involved, examples, tools, and best practices.

What Is Data Curation?

Data curation is the process of selecting, organizing, and managing datasets according to the requirements of a specific group. It is an ongoing process that ensures accessibility, sharing, and preservation of information throughout the data lifecycle. This makes the data easy to find and understand for future use cases.

Why Data Curation Is Important?

The number of data sources, from social media to IoT devices, has increased with the latest tech inventions, creating a challenge for organizations. This data often arrives in various formats (structured, semi-structured, unstructured) and can be messy and disorganized when extracted for analysis.

Data curation is the key to unlocking the potential of this messy data. It involves managing and organizing data from all sources, ensuring it is consistent for analysis. By effectively curating your data, you gain a complete and unbiased picture, reducing the time needed to find the information you need. This empowers you to make data-driven decisions based on accurate and reliable insights.

How to Perform Data Curation Process?

The data curation process involves multiple steps, following which you can efficiently manage and access your organization’s data. This section will discuss the key steps to curate data effectively.

Step 1: Identify the Data Sources

The first step of the data curation process involves collecting data from multiple sources. Start by identifying the necessary data required for your analytics and business insights.

Step 2: Develop Data Ingestion Pipelines

After identifying the data sources, it is necessary to define a data ingestion strategy and create a pipeline to systematically extract data.

Step 3: Perform Data Transformations

This step involves creating an outline of how you will clean and transform the incoming data into a format that’s compatible with your destination systems and analysis purposes. The most important steps in transformation include removing duplicates, handling missing values, and standardizing data formats.

Step 4: Initiate Data Integration

Establishing the outline of the data curation process is an essential step for understanding the roadmap to follow to get effective results. Here, you bring your data together from various sources into a unified environment for further processing. There are two main approaches to implementing the data integration process.

You can either manually write custom code to transform and migrate your data or leverage data integration tools like Airbyte to automate the data movement process.

‍Data consolidation achieved through this step provides several benefits, such as enhanced consistency, streamlined analysis, and improved accessibility.

Step 5: Establish Data Governance Policies

During the data curation steps, it is necessary to ensure that the data adheres to your organizational governance policies. This involves designating data governance roles, setting security and compliance measures, managing metadata, and continuously monitoring data movement.

Step 6: Define and Maintain Metadata

Metadata is a summary containing information about your data, making it easier for you to find and use your data. Maintaining metadata in data curation will allow all the users within your organization to understand what the data contains and how it was collected.

Step 7: Establish Data Validation Systems

Implement data validation systems to monitor data accuracy, completeness, and consistency. This helps maintain high-quality data, resulting in better-informed insights.

Step 8: Define and Enforce Data Privacy

Prioritize data privacy by providing secure access to authorized users. This involves establishing robust data governance policies, encryption techniques, access controls, and monitoring tools.

Step 9: Establish Data Lineage

Data lineage describes the origin, structure, and dependencies of the data, which enhances the quality of the data you use. The data lineage step involves tracking the data flow throughout its lifecycle. This step lets you identify and fix any errors during the data transfer process.

Step 10: Regularly Monitor the Data Curation Process

Continuously monitor the data curation process to identify potential issues and fix them promptly. This involves configuring metrics to measure the accuracy of the data and using them to audit the process according to the required improvements.

Tools Used to Curate Data

There are multiple data curation tools that can help streamline your data management journey. This section highlights the four prominent platforms that you can combine to curate your data effortlessly.

Airbyte

‍Airbyte is a cloud-based, no-code data integration and replication platform that simplifies data movement. It allows you to seamlessly migrate data from multiple sources to your preferred destination. This centralized location provides a holistic view of your data, making data curation easier.

Alation

Alation is a data cataloging platform that provides you with metadata management and data lineage features. It allows you to document your data assets, including their location, format, and purpose. This makes it easier for users across organizations to find the data they need and understand what it represents.

Great Expectations

Great Expectations is an open-source data quality tool that enables you to validate, document, and profile your data. This tool automates the process of identifying data quality issues by cross-checking incoming data with the user-defined rules. With this approach, you can quickly fetch data quality issues early on, preventing errors from passing through downstream applications.

Cloudera

Cloudera is a big data management and analytics platform that can work as a central repository to store and process data. It performs distributed data storage and processing using components of Apache Hadoop. Cloudera has built-in ML capabilities that can help you train your model and perform advanced analytics on it to generate impactful insights.

Data Curation Vs. Data Management Vs. Data Cleaning

While all three terms are related to data handling, data curation, data management, and data cleaning serve distinct purposes within the data lifecycle. Here are the key differences you must understand when to use each.

	Data Curation	Data Management	Data Cleaning
Definition	Organizing and maintaining data to make it usable and accessible for specific purposes.	The practice of handling all data throughout its lifecycle.	Identifying and removing the inconsistencies and other quality issues from the dataset.
Objective	Ensure data is findable and understandable.	Maintain data integrity, security, and accessibility across the organization.	Prepare data for storage and analysis.
Processes Involved	Data cleaning, transformation, metadata creation, and documentation.	Data governance, security, storage, ingestion, architectural design, and lifecycle management.	Identifying and eliminating duplicate values, correcting data formats, removing outliers, handling missing values, etc., depending on the use case.
Scope	Focuses on specific data assets as per project requirements.	Data management addresses all the data-related aspects of your organization.	Improves data quality for analysis.

Examples of Data Curation

Data curation is essential for ensuring data is readily accessible, high-quality, and preserved for future use. Let’s examine a few data curation examples that demonstrate how it is utilized in real-world applications.

Advanced Healthcare System

Data curation helps manage patient data, including medical histories, treatment plans, diagnoses, and outcomes. By ensuring the accessibility and accuracy of this curated data, healthcare providers can improve patient care, prioritize attention, and discover improvements in treatment standards.

Scientific Research

Researchers often use curated datasets to conduct and support their studies. These datasets often include preserved data on various subjects. The curated data ensures data quality and helps them discover advanced findings.

Securing Financial Records

Data curation is crucial in the finance domain for safeguarding financial data, such as transaction records, loan information, and investment details. By implementing data curation practices, financial institutions can minimize the risk of fraudulent transactions, providing better transparency and customer satisfaction.

Who Performs the Data Curation Process?

Every individual working with your organization's data can contribute to curating the data using their knowledge and experience. Many professionals, including data analysts, scientists, and business analysts with the necessary skills, can perform data curation. However, there are professionals who specialize in this particular field; they are referred to as data curators. These professionals possess in-depth expertise in data quality management and metadata creation.

The data curators, with their domain-specific knowledge record and share the data, making it easier for analysts to understand the nature of the data they have to work with. By making data meaningful for a specific purpose, they empower analysts to make better decisions.

What Are Some of the Data Curation Best Practices to Follow?

This section highlights the data curation best practices that you can follow to maximize the outcome of your data:

Before starting the curation process, you must understand your data, what it represents, and how your organization will utilize it. This concise picture will help you use the data with maximum efficiency.
You must assess the quality of your data and learn how it needs to be transformed before analysis.
Create detailed metadata that describes your data. This includes information like data definitions, collection methods, update frequency, etc. A robust metadata makes it easier for users to understand the data and use it effectively.
Promote collaboration between data analysts, data scientists, and business users. These professionals will help to extend the meaning of data and its potential applications.
Comply with security regulations to ensure that your data is used within the guidelines of governance standards.

How can ETL tools like Airbyte Help with Data Curation?

Airbyte can significantly aid in the data curation process by automating and streamlining various tasks. Here’s how Airbyte contributes to the data curation process:

With Airbyte, you can effortlessly extract and unify data from various sources into the desired destination with the help of its 350+ pre-built connectors. These connectors eliminate the need for manual intervention and coding, saving time and reducing errors during data collection. You can enable the Change Data Capture (CDC) feature to ensure your curated data remains updated.
Airbyte simplifies the transformation process by allowing you to integrate it with robust data transformation tools like dbt. This simplifies data cleaning and preparation for analysis.
By consolidating data into a centralized system, data practitioners can easily collaborate on data curation tasks. This will provide a clear view of data lineage and facilitate efficient access to curated data.
It provides advanced data security features by adhering to renowned security benchmarks such as ISO, HIPAA, SOC 2, and GDPR. This ensures your valuable data remains protected throughout the data curation process.

By leveraging tools like Airbyte, you can streamline your data integration process, improve data quality, and gain more valuable insights from your data.

Key Takeaways

Data curation is an important part of data lifecycle management, which enables you to prepare and organize your data into high-quality, readily accessible information for analysis and use. It ensures your data is consistent and reliable for decision-making.

FAQs

Who Uses Data Curation?

Data curation is a vital process whose applications span across various domains. Specialists such as data curators, researchers, analysts, and business professionals with the necessary skills utilize data curation in their day-to-day activities.

What Does a Data Curator Do?

Data curators organize and manage data to make it accessible while enhancing its reliability and quality. They store and share the data and help analysts understand its nature to generate impactful insights.