Data Curation

What is Data Curation?

Curation is the end-to-end process of making good data through the identification and development of resources with long-standing value. In information technology, it refers mainly to the controlling of knowledge throughout its lifecycle, from the foundation and initial storage to the time when it’s archived for future study and analysis, or becomes outdated and is removed. The goal of knowledge curation within the enterprise is double: to make sure compliance in which data are often retrieved for future research or reuse.

Why do we need data curation?

Companies spend seriously in big data analytics with data volumes growing from time to time, alongside the increasing variety and heterogeneity of knowledge sources, getting the info you would like ready for analysis has become an expensive and time-consuming process. Duplicate data and blank fields need to be eliminated, misspellings fixed, columns split or reshaped, and data need to be developed with data from additional or third party sources to provide more context.

  • Effective Machine Learning

Machine learning is algorithms of artificial intelligence (AI) that provide systems the capability to automatically study and improve from knowledge without being explicitly programmed. The AI consists of “neural networks” that collaborate, and can use Deep Learning to identify patterns. Curations are about where the people can add their information to what the machine has automated. 

  • Dealing with Data Swamps

A Data Lake strategy allows users to easily access data, to think about multiple data features directly, and therefore the flexibility to ask ambiguous business-driven questions. But Data Lakes can end up Data Swamps where finding business value becomes like a big milestone. Well, data curation here can save your data lakes from becoming the info yards.

  • Ensuring Data Quality

Data Curators clean and undertake actions to make sure the long undertake actions to make sure the long-term preservation and retention of the authoritative nature of digital objects.

Steps in Data Curation

Data curation is the method of turning autonomously created data sources (structured and semi-structured data) into combined data sets ready for analytics, using domain experts to monitor the method. It involves:

  • Identifying

One must identify different data sources of interest (whether from inside or outside the enterprise) before they begin performing on a drag statement. Identification of the dataset is as important a thing as solving a drag. Many of us underestimate the worth of knowledge identification. But, when one does data identification the proper way, one can save on tons of your time wastage which may happen while optimizing the answer of the matter.

  • Cleaning

Once you’ve got some data at hand, one must clean the info. The incoming data may have tons of anomalies like spelling errors, missing values, improper entries, etc. Most of the info is usually dirty and you would like to wash it before you’ll start working with it. Cleaning data is one of the foremost important tasks under data curation.

  • Transforming

Data transformation is the process of converting data or information from one format to a special, usually from the format of a source system into the specified format of a replacement destination system. Data curation also takes care of the info transformation.

Challenges and Best Practices

Data is considered to be an asset for any organization, be it financial, airline, e-commerce, or universities. However, even with the availability of data at one’s disposal, it is only viable unless and until the Data is organized, managed, and retrievable with little efforts. Big Data is around for decades; however, it’s rolling in the mainstream for the last 5 to 10 years. The organization got revolutionized with the decisions made over the availability of data. Data Curation is a term often referred to as to manage the data through its life cycle, which starts from data creation, consumption, archiving, and deletion. The data during its lifetime passes through numerous phases of transformation, and the purpose of data curation is to provide surety that the Data is stored in secure, reliable, and efficiently retrievable.

Data Accuracy is one of the biggest challenges that is faced during the entire data life cycle. If the data at the primary source is not accurate, the whole building block build over the data will fall like Jenga blocks. The decisions done over the data will prove out to be a disaster for any management, and this is what the majority of the organization faces in current times. 

Annotation and Labeling is another good practice that enriches the data by adding metadata. The accurate tagging will lead to proper transformation and processing of data for later stages. 

Duplication is another challenge being often faced when the same set of information is placed over the different data sources. The data transformation might change one source and leave the other, and using the wrong data, later on, can turn out to be another disaster in the waiting.

Security & Privacy in this digital age plays a vital role in any organization. With hacking, data infringements, and data break-ins on the rise, the company’s management are losing their sleep to protect the data. To ensure security & Privacy of the data, Encryptions do a viable job to keep the data protected even in cases if it falls into the wrong hands.

Storage Architecture Engine should be in place from the very first day. The anticipation of data volume generated over time usually is kept insight to ensure the optimal performance to store and retrieve the managed data. With the latest open-source tools like NoSQL, Kinesis, and Kafka, it is not much of a challenging job to build a highly scalable distributed data engine. With a state of the art in-memory caching Hierarchical Storage, it is made sure that Data is optimized for retrieval at all times. As Data is written once; however, it is retrievable most of the time during its lifetime; the architecture is expected to be built in a way to utilize both cache and in-memory databases. 


In conclusion, the organizations are to ensure higher data protection, data integrity, and accuracy by implementing best practices for data curation, leading to better decision-making. Data curation observes the utilization of knowledge that specializes in how context, narrative, and meaning are often collected around a reusable data set. It creates trust in data by tracking the social network and social bonds between users of knowledge.