Key Features of a Machine Learning Data Catalog
Data intelligence is crucial for the development of data catalogs. At the center of this innovation are machine learning data catalogs (MLDCs). Unlike standalone tools, machine learning data catalogs have features like:
- Data search
- Data stewardship
- Business glossary
Machine learning improves the functionality of each feature. According to the Forrester Wave: Machine Learning Data Catalogs, Q4 2020, “Alation exploits machine learning at every opportunity to improve data management, governance, and consumption by analytic citizens. Every data catalog function is underpinned by intelligence that learns from data patterns, queries, and data professional search and interaction.”
As data collection and volume surges, machine learning eases the burden of search & discovery, governance, and curation; it continuously learns from user behavior to “intelligently harmonize data management, governance, and analytics” (Forrester). In this way, machine learning is driving the evolution of the catalog industry.
The key ingredient to data intelligence is user behavior. Alation’s Behavioral Analysis Engine (BAE) spots behavioral patterns and leverages artificial intelligence (AI) and machine learning to make data more user friendly. For example, search is simplified by highlighting the most popular assets; stewardship is eased by emphasizing the most active data sets; and governance becomes a part of workflow through flags and suggestions.
Benefits of Using an MLDC
An MLDC brings many benefits, like:
- Enhanced data management
- Increased self-service analytics efficiency
- Data governance streamlining
- GDPR/privacy functionality
- General improvements to developmental, administrative, and governance tasks
The automation of these processes supports the larger goal of data-driven decision making within the enterprise.
Organizations now have the ability to collect and store more data than ever. MLDCs improve upon traditional metadata management systems by injecting intelligence. Now, MLDCs make it possible to understand how data is being used internally, where it came from, who is using it, and how it’s related to other data sets.
Tracking and Scaling Data Lineage
MLDCs remove pressure from data consumers by taking on responsibility for data quality, find-ability, and reliability. Without an MLDC, a data analyst faces many obstacles in their quest to find and analyze good data. Simply finding what they need, and ensuring it’s quality, trustworthy data can take months. Machine learning shrinks this timespan to seconds.
An MLDC maps data sources and origin; so, should a problem arise, users can check data lineage to assess quality and spot other related errors. An MLDC flags potentially inconsistent data, alerting a data steward to track back to the point of error and fix that data source for future uses.
As an MLDC is used, it gains more insights, which in turn strengthen its ability to improve on these processes across the company. Because the data is centralized, each catalog improvement allows for enterprise-wide implementation. This lineage process also helps data analysts see the impact of internal changes after fixing an error.
Automating Data Discovery
Data discovery and search are crucial catalog components. Like Google, MLDCs learn from human behavior to serve the most relevant search results, increasing efficiency for all data users. MLDCs leverage AI to learn from user interactions and cut down on time spent. This helps data users get relevant data faster and with more accuracy. A virtuous cycle emerges: the more people who use the data catalog, the more data the BAE will have to improve search and discovery capabilities.
Simplifying Data Accessibility
Data management takes time. As data volume grows, manual data catalog tagging methods can no longer keep pace with the efficiency of the MLDC. As privacy also becomes a growing concern, the demand for catalog software that can provide data governance solutions — while scaling search, discovery and evaluation efficiency — is growing.
MLDCs spot human patterns that signal a governance process at work. Data stewards are then alerted and the process is formalized transparently. This allows data to be more accessible without compromising governance, creating a system of data democratization.
Improving Data Quality
Data governance leads to higher quality data. Machine learning supports both governance and quality data by:
- Expanding available data variety
- Simplifying data accessibility
- Standardizing data semantics
- Improving trustworthiness of the data
The MLDC tracks data consumption and information use in order to automate the discovery process. Automation entails rooting out duplicate, irrelevant data and prioritizing the data that meets the needs of the searcher. In this way, the data catalog continuously refines its ability to provide relevant data to the right person, evidenced by features like real-time quality alerts to the analyst as they work.
Improve and Regulate Data Privacy and Security
Regulations around the usage of data, especially in the medical industry, call for enhanced data governance to meet compliance. The demands of compliance laws, such as GDPR and CCPA, are nearly impossible to meet without the proper software.
MLDCs, however, can lighten the burden of navigating these regulations. MLDCs assess metadata to determine which data attributes are personal or private and which sets contain sensitive data that may be subject to regulatory compliance. Governance is continually applied as data users interact with data. This “data governance in action”, embedded at point of use, is a key feature that separates MLDCs from standard data catalogs.
Scaling Data Intelligence
Once enterprise data is centralized, the intelligent catalog can collect information about internal data usage. This allows the catalog to continuously learn and improve governance functions: data management, search & discovery, and data quality.
An MLDC boasts a bird’s eye view of all data across the enterprise. So if an automation is effective in one data set, the MLDC can zoom out to find other relevant datasets and apply that automation for them, too.
Each efficiency improvement leads to further benefits for the catalog and company. Analysts spend less time hunting and more time interacting with the data, which gives the AI more information so that it may further optimize. This has a butterfly effect for all users involved.
Machine learning data catalogs leverage AI, but take their cues from real human behavior. MLDCs monitor user interactions to learn how data is used — and optimize processes for all.
Folks already govern their own data; an MLDC translates these patterns into a data governance process that keeps all teams in sync. This leads to improved discovery and superior quality, which means analysts can more easily find the data they need, trust the data they use, and meet compliance standards with as little manual effort as possible.
Are you interested in data catalogs for your business? Consider your options with the help of our evaluation guide, How to Evaluate a Data Catalog. And for a deeper dive into the Forrester analysis of MLDCs, check out our blog, Alation is the Clear Leader in The Forrester Wave: Machine Learning Data Catalogs, Q4 2020.