If your data team spends more time cleaning than analyzing, you have a liability, not a pipeline. The fix? Data curation, the process of enforcing structure, resolving inconsistencies, and making raw data reliable enough for downstream use. 

Curation is what separates brittle, reactive workflows from pipelines that scale. Here’s how it works — and how to get it right.

What is data curation?

Data curation is the process of arranging and enriching company data so it’s easier to find and use. It turns messy and disconnected information into reliable, centralized datasets for teams to pull from.

Modern ELT tools oversee moving and updating data across your organization, and these tools automate a lot of heavy lifting with data curation. 

From raw to reliable: The data curation process

Useful data doesn’t appear by chance — if it’s fresh, accurate, and actionable, it’s the result of an active curation process.

These are the key stages of curation throughout the data lifecycle, from ingestion to governance.

1. Data discovery and ingestion

Where do you get your data from? What sources do you rely on, and what tools are connected to them? Data discovery answers these questions, mapping out the data streams your business uses. 

A reliable ingestion process makes sure that none of your data slips through the cracks. Fivetran’s pre-built connectors simplify this step, automatically pulling data from data warehouses, databases, and connected SaaS apps into a single location.

2. Profiling and data quality assessment 

Profiling evaluates data quality by flagging errors, inconsistencies, and duplicated records to check for mistakes, data quality issues, or gaps from incorrect ingestion. By looking for inconsistencies or duplicated information, you can fix these problems early and prevent data analysis issues downstream.

3. Cleaning, transformation, and enrichment

Even data that looks promising may need a little transformation and enrichment to draw out its finer details. The cleaning stage remedies any flagged errors, corrects values, and standardizes data to make it easier to work with in your BI apps. 

Manual cleaning can consume hours of engineering time, especially when thousands of records need correction. Fivetran’s transformation layer makes this process a breeze, automating cleaning and enrichment while keeping data consistent.

4. Metadata creation and linkage

An important part of data curation is adding metadata to datasets that describe what it is, where it comes from, and what it represents. Keep all of your data curators on the same page by creating an internal schema showing what this metadata looks like and what structure is needed.

5. Annotation, tagging, and classification

Tags and classifications are broad labels that make it easier to sort through data. When an employee needs certain data records, these tags filter out the noise to find the most relevant details. You can add sensitivity labels at this stage, connecting to your permission systems to protect any private information.

6. Versioning, preservation, and lineage

The final step in data curation is to make sure you can trace data lineage all the way from its origin to its current state. Using versioning in data architecture shows how datasets evolve — what people added, updated, or removed. Data naturally changes over time, so tracking these metrics keeps you informed on updates.

Benefits of data curation

Data curation transforms scattered, raw data into useful information for teams to interact with and analyze. 

Here are a few additional benefits of data curation:

• Increased discoverability: Curated data is centralized, making it much easier for teams to find the necessary information.
• Facilitation of self-service analytics: When data is accessible and well organized, even someone without technical skills can interact and use it in self-service analytics.
• Decreased duplication: Part of curation is checking for any duplicated datasets and removing them, keeping the data free from redundancies.
• Reduced silos: Standardizing and centralizing data helps improve visibility, undermining data silos and providing a transparent view of company information.

Data curation challenges

Even with the right automation tools in place, there are a few common hurdles when curating useful data:

• High resource requirements: From cleaning individual datasets to tagging every piece of information flowing through your company, manual curation can take hours. Pairing a clear strategy with the right automated tools makes a big difference in the amount of time spent.
• Siloed and distributed data: Without full visibility over your company data, it’s hard to know if the final curated datasets are representative. Before launching into data curation, focus on breaking down data silos and creating a consistent data pipeline system across your business. 
• Evolving metadata requirements: Keep track of new fields, rename old columns, and update definitions in internal documents. Planning ahead is vital, especially since your metadata changes with updates in your business.
• Scalability challenges: A simple system that works perfectly for a handful of sources might scale terribly. Make sure to test systems as they grow, refine where needed, and look for third-party tools to help. If you’re scaling, automation is your friend. 

Best practices for effective data curation

Because data curation touches every dataset and team in your organization, the way you begin sets the tone for everything downstream. Follow these best practices to adopt a clear, well-structured approach from the start.

Establish clear data ownership and stewardship roles

Managing, protecting, and using data responsibly throughout its lifecycle is a data steward’s role. This process requires sharing, maintaining, and collecting data while deciding who has access to it.

Setting clear data ownership and stewardship roles for these datasets creates transparency and accountability for teams, keeping everyone aware of what data belongs where.

Develop and enforce consistent metadata standards and taxonomy

For everyone who interacts with the data, create an internal metadata system to follow. Developing clear rules about metadata labels, structure, and descriptions makes it easier for your entire organization to use the data. 

Automate profiling, extraction, and enrichment

Automation is perhaps the single most impactful strategy you can use when improving curation. Tools like Fivetran let you automatically update datasets, integrate information, and keep data consistent across your entire organization. With automation, you’re increasing the data quality and usability while decreasing the amount of effort curation takes.

Prioritize high-value datasets first

Your business likely comes into contact with thousands of different datasets each day. Although some are important, the vast majority are just noise. Don’t waste time curating data that doesn’t provide value to your company. Start small, focusing on a data curation process that begins with the high-value datasets that will make a difference. Once you have systems in place, you can start to scale up.

Find a data catalog or curation tool

Data curation tools usually come with catalogs for users to inspect data and find whatever information they need. Simplify the process and make sure employees know where to go for company data by using a centralized catalog.

Use cases for data curation

Data curation is more than organizing. The process amplifies data quality while positively affecting any of the downstream systems that rely on those datasets.

When done well, data curation has an enormous range of real-world applications:

• Enabling self-service analytics and data discovery: Curated and labeled data allows information usage without relying on engineers.
• Supporting AI models with clean datasets: Providing clean and consistent data improves AI model accuracy.
• Business intelligence and reporting across domains: Curated datasets ensure data stays up-to-date and relevant for business intelligence tools.

Platforms, tools, and technology for data curation

Data curation efforts are easier when you have the right systems supporting you. During the process, these tech tools come in handy:

• Data management catalogs: Centralize your datasets and let users search through data easily.
• Metadata management platforms: Use schema rules and lineage tracking software.
• Automated profiling and rule engines: Flag any inconsistencies in your data and enforce tagging standards.
• Annotation systems: Tag your data and enrich it with context annotations for downstream use.

How Fivetran enhances data curation workflows

Fivetran automates the movement and transformation of data across your systems, keeping it fresh, accurate, and consistent at every stage. With built-in support for metadata management, schema drift handling, and integrations with tools like Polaris, Fivetran helps you maintain lineage, enforce structure, and scale your curation efforts with less manual overhead.

Get started for free or book a live demo today. 

FAQs

What’s a curated database?

A curated database is a data lake, warehouse, or centralized repository where you store high-quality, organized data.

What does curation mean?

Data curation means collecting, organizing, and structuring data so it’s easy to access and use in analytics.

What’s the difference between data cleaning and data curation?

Also known as data wrangling, data cleaning fixes errors and removes inconsistencies. Data curation, on the other hand, focuses on the broader process of structure and organization.

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