Data, for all the bustling excitement surrounding it, only becomes truly valuable for an organization once it is refined, organized, and transformed from just a random set of values to something usable. Globally, organizations now collect an unprecedented volume of data, with some suggestions that almost 90% of all the world’s data has been created in the past 2 years alone.
With these mindboggling figures, it is easy to see the criticality and necessity of structured data curation and management that ensures data remains an asset and not a liability.
Leveraged properly, data curation has the potential to be a significant differentiator for organizations. Consider Netflix or Spotify, using enriched and well-curated data to power their personalized recommendations, which have helped them become the category leaders in their fields. The stakes become even higher in the modern AI landscape as a modern study demonstrated a marked 28% improvement for AI model performance by applying an ensemble data curation framework (dubbed EcoDatum) over baseline methods.
In simpler terms, data curation is not just a technical task; it is a vital asset that can help organizations unlock trust, value, and governance.
Read on to learn more about what makes data curation so important for organizations, the key challenges involved in it, what the entire process looks like, and most importantly, how organizations can best integrate it into their operations.
Why is Data Curation Important
For an organization that wishes to be truly data-driven, data curation should be at the forefront of its priorities. Organizations that fail to treat it so will find themselves making poorer decisions, based on incomplete, inaccurate, and outdated information. According to a Gartner report, poor data quality can cost organizations almost $13 million annually, due to various reasons ranging from inefficiencies and missed opportunities to downright compliance failures.
Arguably, the most immediate benefit data curation provides an organization is the assurance of data quality and trust. Data that has been cleared, validated, and enriched ensures an organization has the resources needed to make considerate insight-driven strategies. An example would be a bank, where carefully curated transaction-related data ensures banks can detect fraud faster and more efficiently. Doing so not only allows for a better customer experience but also helps banks meet regulatory and industry expectations. The same principle is applicable to other industries such as healthcare, government agencies, as well as data related to IP and PII.
Ultimately, the most critical value proposition of data curation lies in turning chaos into clarity. Businesses are able to extract meaning from the vast volumes of information they have at their disposal. Whether it’s refining product recommendations and improving AI model accuracy to ensuring regulatory readiness, data curation allows for reliable insights, strategic agility, and long-term value optimization for businesses.
5 Stages of the Data Curation Process
The five main stages of the data curation process are as follows:
1. Data Collection
The first stage of data curation is the actual data collection, where data is gathered from various sources. This includes databases, cloud systems, IoT devices, and third-party feeds. Through such diverse data sources, aggregated data is collected into a centralized repository for easier management, visibility, and use.
At this stage, it is important that all data being collected is appropriately assessed for relevance, format, and source credibility to ensure only the most relevant and valuable information becomes part of the curation pipeline.
2. Data Cleaning
Once collected, the data must then be cleaned to remove all errors, duplications, inconsistencies, and incomplete entries. This process not only enhances the overall accuracy and reliability of the resulting analytics and insights but also ensures that the ultimate model being leveraged produces outputs that are trustworthy and reliable.
An example would be cleaning a customer record that appears in multiple systems and consolidating it under one profile. Automated tools can streamline this process by identifying all anomalies and enriching them to transform the raw data into a rich and informative asset that supports analytics and advanced model training.
3. Data Annotation & Enrichment
Once data is collected and cleared, it must be contextualized and enhanced with the right kind of metadata. Doing so not only ensures the data is more meaningful and easier to interpret, but also easier to leverage in its future use cases. The annotation process itself involves labelling the data points with the tight descriptive attributes to make them easier to use in AI training datasets.
Further enrichments add the external and insights needed to ensure completeness. The combination of annotation and encryption ensures that data aligns with both the business and regulatory expectations.
4. Data Validation
Data validation ensures the quality of the dataset fulfills the quality, consistency, and accuracy standards of both the organization itself and regulatory obligations. The process involves the verification of data against the established rules and reference datasets. This not only makes it easier to detect discrepancies and inconsistencies but also eliminates instances of rechecking and manual work required later on.
Done properly, this not only increases the reliability of the dataset itself but also helps build trust in the data-driven decision-making process of the organization.
5. Data Storage & Access
The last stage of the data curation process involves how the data will be stored. Naturally, data storage itself must be secure, with the relevant access controls in place to ensure compliance with regulatory requirements. Data is organized and stored in governed repositories and catalogs, with additional security measures such as encryption and access control measures deployed based on the sensitivity of the data itself, as well as the expected personnel likely to access the data.
Moreover, the storage should facilitate easy browsing to ensure the data is discoverable and retrievable for the various purposes it has been collected for.
Data Curation Challenges
Some of the main challenges when it comes to data curation are as follows:
A. Data Silos & Fragmentation
Data fragmentation is the first major challenge any organization will face as its data assets are usually scattered across a vast array of systems, departments, and in some cases, jurisdictions. This naturally leads to “silos” where it is difficult to gain a comprehensive and singular understanding of the information in the organization’s possession.
To break down these silos, organizations need robust data discovery, integration, and cataloging that can give them the necessary capabilities to unify the data under a singular governance layer.
B. Lack Of Standardization
Inconsistent data formats along with incomplete metadata and varying taxonomies can lead to data inoperability that is poor in nature. In the absence of a standardized format to describe and classify data, it becomes increasingly challenging for the organization to locate, interpret, and use information effectively.
Hence, a clear metadata framework is needed that leverages automated means to help ensure uniformity across the firm.
C. Manual Processes
Traditional data curation methods are completely reliant on manual review and data entry. These are prone to human error and scalability issues. Manually attempting to curate the data resources is not only inefficient but can also be impractical. Moreover, it can lead to issues such as delays in analytics and increased operational costs.
Automated data discovery, cleansing, and enrichment with AI tools ensures the organization can handle the large and complex datasets that are increasingly becoming the mainstay of modern enterprises.
D. Balancing Accessibility With Privacy
Data collected must also be accessible for the organization for it to be truly leveraged in its innovation and sensitive information protection purposes. However, granting broad access to it can be significantly problematic as it exposes organizations to compliance and privacy risks under almost all major frameworks, such as the GDPR, CPRA, HIPAA, and others.
These can be resolved by implementing access controls, anonymization, and sensitivity tagging during the curation process that ensures that the data remains usable while adhering to ethical and regulatory standards.
E. Continuous Maintenance
Data curation is not a one-time thing. It requires a continuous monitoring mechanism that ensures consistent governance. As the data evolves, organizations will find new sources along with changes in the regulations and the datasets themselves.
Maintenance of data quality and lineage over time depends on the adoption of a structured governance framework, with automated quality checks, and collaboration between all the identified stakeholders.
How Securiti Can Help
Securiti’s DataAI Command Center is a centralized platform that enables the safe use of data+AI. It provides unified data intelligence, controls, and orchestration across hybrid multi-cloud environments. Several of the world's most reputable corporations rely on Securiti's Data Command Center for their data security, privacy, governance, and compliance needs.
Request a demo today and learn more about how Securiti can help your organization implement appropriate data security and privacy controls within its operational workflows effectively to ensure all data is effectively managed and protected.
Here are some of the most commonly asked questions related to data curation:
