Everything You Need to Know About Data Curation

Category: Best Practices

Published date: 23.01.2024

Read time: 7 min

In our data-driven world, the sheer volume of information generated daily is staggering. Businesses, researchers, and individuals rely on data for decision-making and insights. However, the abundance of data comes with its challenges. This is where data curation plays a pivotal role. Data curation involves the selection, organization, and maintenance of data to ensure its reliability, relevance, and usability. In this article, we will take a closer look at data curation to learn about its importance in today’s digital landscape.

What is Data Curation?

Data curation is the process of creating, organizing, and maintaining data sets so they can be readily used by people looking for information. For users inside an organization, a group, or the wider public, it entails gathering, organizing, indexing, and categorizing data. Data might be selected for a variety of reasons, including academic requirements, scientific research, business decision-making, and more.

To curate data is a step in the process of managing data generally, and it can also be included in data preparation tasks that prepare data sets for usage in analytics and business intelligence applications. In other situations, the curation process could use prepared data for further upkeep and administration. In companies without dedicated data curator jobs, the function may be filled by data stewards, data engineers, database administrators, data scientists, or business users. Now that we know what is curated data, let’s move on to see why it is important. 

The Importance of Data Curation in ML and AI

Curating data means gaining a deeper understanding of the data you have. Their primary duty is to gather, organize, and exhibit a collection of books or artwork so that others can easily view it. The same concepts apply to data curation in machine learning. The machine learning model will find it difficult to learn from data that is not properly filtered, distributed, and annotated. 

Data curation is the process of creating, organizing, and managing data collections so anyone who’s looking for them can find them. It comprises collecting, organizing, filtering, indexing, annotating, and classifying data for users that are part of the public or users within a firm. The active and ongoing management of data during its whole life cycle is known as data curation.

Data Curation vs. Data Governance  

To better understand the difference between database curation and governance, we need to understand the roles of the data curators and data stewards. To give data users additional context, data curators are the owners of data sets and the metadata that goes with them. They deal with datasets, not the organization’s database, data pipeline, or data process. The data stewards are owners and maintainers of databases, data processes, and the overall vision of the organization as to how data aligns with their business goals. They put a lot of effort into prioritizing, establishing, and maintaining access rules and data governance, as well as connecting data to business needs.

Top Benefits of Data Curation 

As you may have guessed from the description of data curating, there are several advantages to this approach:

  • Ease of access – When your data is better structured, it is readily available and accessed by the data science team or any other team members who need access to your curated datasets. 
  • Increase in production speed – Having a curated dataset instead of large volumes of disjumbled data allows your team members to optimize data ingestion and utilization, which, in turn, helps speed up production. 
  • Redundancy and bias detection – Raw data has various biases that can distort the overall quality of your product. Data curation can help you identify those biases and remove them before they make it into your product.

Challenges of Data Curation 

While there are many benefits to data curation, there are several challenges as well. 

These include:

  • Data accuracy – If the original source of the data is not accurate, then all the subsequent decisions and actions made by the end product will also be inaccurate. This can cause significant delays in the development of the product. 
  • Security and privacy – CIOs and CTOs are always on guard because they are finding it harder to protect their data in the face of increasing hacking, data breaches, and infringements. News reports about companies losing millions of identities due to hacking are not unusual these days.

Curating Data With Mindy Support 

Mindy Support can help you with all facets of data curation. This includes cleaning or wrangling data, evaluating if the data is free of missing values, assigning information representation, and ensuring an appropriate data structure or file. Rebalancing distributions, utilizing synthetic data or further data collection to fill in missing data, and correcting label errors are a few other examples. We also search for issues related to data bias, data distribution, label accuracy, missing values, and quality assurance and usability issues. It also entails making an effort to find and retrieve potentially valuable “hidden information” from data. 

Trust Mindy Support With All of Your Data Curation Needs 

Mindy Support is a global provider of data annotation services and is trusted by Fortune 500 and GAFAM companies. With more than ten years of experience under our belt and offices and representatives in Cyprus, Poland, Ukraine, Bulgaria, Philippines, India, and Egypt, Mindy Support’s team now stands strong with 2000+ professionals helping companies with their most advanced data annotation challenges.


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