What Is Data Curation?
Data is an organization’s most valuable asset, but to maximize the value of data, it needs to be used. Organizations have an enormous amount of data, but having vast amounts of data will only provide value if it is analyzed and applied to drive business direction. As organizations are demanding more analysis and reporting to make data-driven decisions, analysts and decision makers need to know what data to use to drive the business.
Why Is Data Curation Important?
Trying to gain insight from a vast sea of data without any definition or guidance is an impossible task. Data curation is important to increase data value across the organization by surfacing the data that is best to use.
- Faster, More Accurate, Data-Driven Business Decisions: Organizations need data to be labeled, classified, defined, and prioritized in order to know which data to use, what the data means, who owns it, and how to properly and responsibly use it.
- Increased Data Trust: Without curated data, business leaders will not have confidence in the data to trust that the proposed results and recommendations are valid to make business decisions.
- Smarter Data Sharing: Organizations wanting to share data across siloed domains or departments need to ensure that the data is properly defined, the best data is available so that users across departments will gain value from the data.
- Efficiency and Time Savings: Without curated data, analysts and data scientists will not know what data to use for analysis and modeling. They will waste valuable time finding and understanding the data to select the data to use – before they can begin any worthwhile analysis.
- Reduce Cost and Risk: Data Curation is important for IT and Security teams wanting to reduce data risk. Curating essential data will also identify data that is non-essential or duplicate, data teams can choose to eliminate duplicate copies of data that the organization does not need to have actively available.
In other words – organizations need to curate data to surface valuable data, ready to use for analysis. Data curation is important to enable data management and deliver trusted data-driven decisions for strategic business results.
Data Curation Process
In enterprise organizations, a dedicated team of people will be responsible for communicating what data to use for analytics. Often they are referred to as ‘Data Stewards’. Data Stewards will identify the available data and define what it means, so that data can be used properly to make valuable business decisions.
Curation organizes the available data in a way that elevates the most useful data for analysis. There are different ways to look at data to identify the most useful or relevant. For example:
- Data Definitions – confirm that the data is well defined so that users know what it means. This is also important in an organization using data across departments so that it is interpreted properly.
- Data Quality – confirm that data is high quality, complete, and accurate so that users can select the best data for analysis and decision makers can trust that business decisions are based on good data.
- Data Lifecycle Management – know how recent the data is to ensure that it is timely and relevant data and also to enforce data retention policies.
- Data Classification – identify sensitive data and label it appropriately to maintain compliance with data privacy regulations.
Who Owns Data Curation?
Data curation is a data governance initiative and not an IT task. In enterprise organizations, curation is often managed by a team of data stewards or data curators. Their role is not to determine and manage the IT systems storing the data, but instead specialize in the content, context, and ownership of the data. Data curation is connected to metadata management because the way that data is defined, tagged, and managed is through the metadata. In some instances datasets may be cleaned and prepared to be ready to use. Here again it is the metadata that will be tagged to describe that the dataset is current, clean, defined, and ready to use to surface that particular dataset or data object.
Data Steward vs Data Curator
Data stewards and data curators are two important roles within the field of data management. Although they have similar responsibilities in ensuring the quality, accuracy, and integrity of an organization’s data, they differ in their specific focus and approach to data management.
A data steward is responsible for defining and implementing policies and procedures for the management and use of data within an organization. They oversee the overall data governance program and ensure that data policies and procedures are followed, including the classification of data, data security, and data privacy.
On the other hand, a data curator is focused on the management of data assets, including the collection, cataloging, and preservation of data. They are responsible for ensuring that data is properly documented, stored, and maintained for future use, and for making sure that the data remains accessible, usable, and understandable.
Data Curation Challenges
Data curation is a critical aspect of data management, but it is not without its challenges. Some of the most common challenges faced in data curation include data volume, data diversity, data quality, and data security.
Data volume is a major challenge in data curation as organizations are generating an increasing amount of data. This requires organizations to have the necessary infrastructure and tools in place to manage and curate large amounts of data.
Data diversity is another challenge, as data can come in many different forms including structured, unstructured, and semi-structured data. This requires organizations to have the capability to handle different types of data and curate them in a manner that is appropriate for their specific needs.
Data quality is also a challenge in data curation, as organizations must ensure that the data they curate is accurate, consistent, and up-to-date. This requires a robust data quality management process to identify, correct, and prevent data quality issues.
Finally, data security is a major challenge in data curation, as organizations must ensure that their sensitive data is protected from unauthorized access, breaches, and theft. This requires organizations to implement strong security measures and ensure that their data is properly secured at all times.
Data Curation Use Cases
Data curation is an important aspect of data management, and it has a wide range of use cases in various industries. Some of the most common uses of data curation include:
Scientific Research: In the scientific community, data curation is used to preserve, manage, and make accessible large amounts of research data. This allows researchers to easily access and reuse data, leading to new discoveries and advancements in the field.
Healthcare: In the healthcare industry, data curation is used to manage patient data, including medical histories, diagnoses, treatments, and outcomes. Data curation ensures that this sensitive information is properly documented, stored, and maintained in a secure and accessible manner.
Financial Services: In the financial industry, data curation is used to manage financial transactions, including investments, loans, and other financial instruments. Data curation ensures that this information is properly stored, managed, and auditable, reducing the risk of fraud and providing greater transparency to financial markets.
Government: Data curation is also used in the public sector to manage and preserve important government records, including census data, legal records, and historical documents. This ensures that the data remains accessible and usable for future generations.
Leveraging BigID in Your Data Curation Strategy
Step 2: Machine learning and automation add intelligence for context about what the data is, find similar and duplicate data, identify sensitive data and tag with related privacy policies, and enable collaboration with data owners through an interactive catalog.
Step 3: Enhance curation with data quality measurements, connect data definitions, enforce and audit retention policies. Proactively remediate data issues and enable the organization with curated data to increase data use and data trust.