Data cataloging, data governance, and data quality: All you need to know
The pace at which data is growing is exponential. With new data sources being created and connected, enterprises are overwhelmed with data management. Furthermore, integrating insights from AI and IoT and harnessing all the data for business outcomes becomes even more challenging. However, everyone across all organizational levels, including CDOs, data analysts, and customer support, wants to harness data strategically to drive their organization ahead. In this evolving business landscape, organizations increasingly rely on data to make better decisions, forging us into a data-driven era. However, data on its own is of little value unless it is treated and managed well to derive meaningful information. This is where data cataloging, data governance, and data quality come into play.
According to IDC’s projection, global data will reach over 175 Zettabytes by 2025, marking a phenomenal fivefold rise from 2018. Parallelly, the Big Data analytics market is expected to surpass USD 745 billion by 2030 with a CAGR of 13.5% from 2023 to 2030. This exponential growth makes it more challenging for businesses to store, manage, and harness the data efficiently.
This blog is a comprehensive guide on effective data management via data cataloging, data governance, and data quality, encompassing the principles, key components, and importance. It will also cover how the three aspects of data management are interconnected, overlapping, and essential.
What is data cataloging?
As per an IDC Report (2019), ineffective tasks take up 44 percent of data professionals’ time. This results in a 47 percent loss of preparatory time and a 51 percent loss of search time.
Data catalog is a well-organized inventory of all your data assets from all your data sources. It is just like how a library works. A library has several information assets or books that need a method to manage them so readers can access them easily. It is done by cataloging the essential details about the books, such as the title, author, genre, etc., which behave as their metadata.
What does a data catalog do?
Similarly, a data catalog is a collection of metadata, along with data management and search features. It supports analysts and other data users to find the data they require, act as an inventory of all the available data, and include information to assess the suitability of the data for intended uses.
A modern data catalog’s foundation is a robust metadata collection combined with features of identifying and describing shareable data. It would be impractical to catalog data manually. Whereas using automation for initial cataloging and discovery of new datasets is vital. Additionally, collecting metadata powered by AI and ML is crucial for efficiency. Robust metadata enables several functions, such as
- Efficient data search and retrieval
- Data lineage tracking
- Data governance enforcement
- Data quality assessment.
Without a catalog, analysts would waste time searching for data through documentation, tribal knowledge, or simply with “close enough” datasets. With a data catalog, they quickly find, evaluate, and use datasets, reducing data searches from 80% to 20%. This improves analysis quality and boosts organizational capacity without hiring more analysts.
What is data governance?
To harness the data to do the work for an enterprise, first, you have to ensure that the data is clean, available, and managed well. Especially with the upsurge in data sprawl, it must be secure, or rather, governed.
Data governance is thus a critical business process in the realm of data management within an organization. This process can be broadly categorized into two approaches: defensive and offensive.
Defensive data governance involves safeguarding data and ensuring compliance with the various governing bodies.
- Industry-specific compliance: This ensures compliance with industry regulations such as HIPAA, BCBS 239, and FERPA. For instance, a bank must provide accurate financial data, proof of data lineage, and consistent definitions.
- Privacy compliance: This enforces enterprises to adhere to data privacy regulations, including GDPR, CCPA, and other emerging international regulations, protecting consumer data.
- Data security and Protection: It sets policies for collecting, accessing, and securely storing personally identifiable information (PII) and confidential data, including masking data to limit unauthorized access.
- Emerging compliances and data methodologies: Data governance can adapt to the new compliance regulations and data methodologies for effectively combatting threats and supporting innovative data practices.
On the other hand, Offensive Data Governance focuses on maximizing the value derived from your organization’s data assets. It leverages the outcomes of defensive approaches to drive business success:
Trustworthy data-driven decisions
This method utilizes high-quality, standardized, and certified data for informed decision-making, enabled by secure and well-organized data governance processes.
Efficient data engineering
Data governance also enhances data engineering efficiency through improved data quality monitoring and detailed impact analysis, aiding the creation of superior data models.
Data adoption
Not only does data governance promote data accessibility and self-service capabilities in an organization, but it also fosters a culture of data-driven innovation and reduces the burden on IT departments.
Both defensive and offensive approaches are essential for a holistic data governance strategy. Also, when adopting emerging new data methodologies such as data ops, data mesh, and data fabric, having a robust data governance strategy in place can work as a strong foundation. Effective data governance is like the bedrock that enables organizations to unleash the true potential of their data assets and thrive in the competitive business landscape.
What is data quality?
Data quality is the extent to which data is accurate, complete, timely, and consistent in aligning with a business’s specific needs. There are several factors to take into account when analyzing data quality. These factors’ significance may vary depending on the organization’s priorities and needs. The factors include compliance, consistency, integrity, latency, and recoverability.
Intersecting with data governance
On the other hand, data governance involves the organization, protection, management, and accessibility of data through established methods and technologies. Its aim is to maintain its correctness, consistency, and availability to authorized users. It’s crucial to note that while data quality falls within the purview of data governance, they are both critical individual pillars in data management frameworks. Both intersect in the area of compliance. In fact, organizations can bridge them both to align data quality initiatives with the objectives outlined in the data governance standard, creating a cohesive approach.
Effective data governance frameworks incorporate data quality procedures and processes to ensure data remains reliable and fit for purpose across the entire enterprise.
Conclusion
In today’s digital world, zettabytes of data are generated at every digital touchpoint. Thus, organizing data (cataloging), ensuring quality, and governance – all three pillars of data management are crucial. Intertwining and overlapping, these three aspects ensure that your data is reachable, accessible and genuine, consistent, compliant, secure, and ready for leveraging.
You can foster a data-driven culture, encourage data sharing within the organization, and harness the new gold (which is data) responsibly and effectively. Want to jumpstart your journey to be a data-intelligent enterprise? Check out our data services at Saxon AI and get started now.
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