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All You Must Know About Data Curation in '24

Hazal Şimşek
Updated on Jan 3
3 min read

Data curation is an important part of data management. Data curation is the process of collecting, wrangling and preserving data. It allows companies to store sustainable and accessible data to share and apply self-service analytics. Data-driven insights are crucial as data-driven sales strategies enable companies improve their sales productivity by 20 %. 1

However, companies analyze only 12 % of their data on average. Therefore, data scientists are encouraged to curate more datasets and metadata by familiarizing with data curation approaches.

What is data curation? 

Data curation activities (collection, wrangling, preservation) create curated datasets. The main objective is to generate FAIR (Findable, Accessible, Interoperable, and Reusable) and analyzable data. Ultimately, this is to optimize the value of data which is measured by the emerging practice of infonomics.

What does data curation include?

Data curation activities include: 

  • Contextualizing:  In contextualizing, metadata (e.g. relevant sources and attributions) is added to the dataset. The purpose is to show the context regarding how and why the data was generated.
  • Citing the data: Third-party users use appropriate attributions included in the data while citing the data.
  • De-identification: Personally identifiable or protected information is removed or masked.
  • Validating and adding metadata: Information about a dataset is structured in a machine-readable form for search and retrieval purposes. 
  • Validation of data: An expert with similar credentials and subject familiarity as the data creator reviews the dataset. It is performed to confirm the accuracy of the data. 

What is the difference between data curation and data governance?

Data governance is a business strategy that draws the rules, methods, procedures and responsibilities regulating data management practices. On the other hand, data curation is a repetitive process to optimize data and metadata to ensure valuable use of data. Thus, data curation complements a successful data governance strategy.

Why is data curation important?

Incorrect information, knowledge gaps, wrong guidelines are some examples of inaccurate datasets. These datasets can be:

  • Biased: Some AI used for image recognition illustrated gender and racial bias.
  • Inaccurate, unreliable, or incorrectly represented
  • Error-ridden or ambiguous

The absence of processed or curated raw datasets reduces feature quality and restricts the development and applications of the data. Therefore, businesses can leverage data curation for:

  • Data Quality: Data curation organizes, describes, cleans, and preserves data so business analysts work with proper datasets in the long-term. It would not be easy to access, process, and make sense of data without data curation. This would prevent the creation of data swamps. Data swamp refers to the situations when storing and accessing data is not managed correctly and gets lost in unusable data. Data curation enables the segregation of data and keeping good data in the lake. Thus, it can be applied to restore data swamps. 
  • Machine Learning: Machine learning (ML) and artificial intelligence (AI) training data are prepared for processing via data curation. The training data becomes adequately labeled and categorized with data curation techniques, making it reliable, unbiased, and machine-readable.
Source: Big Data Curation

What are the challenges that face data curation?

Data curation can be a costly and challenging process while curating extensive volumes of disorganized data. In such a situation, the data creator considers various data curation approaches and handles high amounts of different data sets.

Furthermore, data has not been accumulated according to its intended usage for decades. Organizations did not know how to implement the data into their strategic decision-making. Companies are expected to leverage their expertise and knowledge concerning the types of data, its value, and why and how to use it before data curation is applied.

What are some of the data curation tools?

Data curation tools optimize the pre-processing steps of data management. These tools assure data integrity and usability. Using AI and ML, these platforms validate metadata and design insights into the accurate repository. To explore some data-related tools, feel free to scroll down our data-driven lists of data solutions.

And if you believe your business will benefit from a data curation tool, let us guide you to choose one:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Hazal Şimşek
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.

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