Simplify your governance projects with this end-to-end solution for data discovery, profiling, and cataloging that uses semantic intelligence and a collaborative glossary
All individuals who work with data require it to have maximum accuracy, consistency, and context to ensure that it is trusted and fit for purpose. One key step to ensuring that your data is fit for use is cataloging and profiling files and data stores and creating models of your metadata.
With Spectrum Discovery you can easily classify, locate, and tag data for quick access, collaboration, and streamlined data insight. Familiar functionalities, such as “tag” and “suggestive filtering,” make it easy to search for information across the enterprise and create a catalog for quicker access and collaboration. Run machine learning-based semantic classification to automatically discover your data. For example, you can easily classify personally identifiable information (PII) – even if it’s never been labeled as such.
Spectrum Discovery also helps map your logical entities with your physical data store to gain a complete enterprise view of your data.
Collaborative business glossary
Help users understand context by providing a comprehensive view of all business terms with related data and metadata to enable a common understanding of core concepts, data definitions, policies, and the rules that govern data collaboratively throughout the enterprise. Communicate core concepts and data definitions for more effective mapping, models, lineage and impact analysis.
Efficient to deploy and easy to manage, the glossary sets a foundation for effective data governance. Spectrum Discovery’s intuitive web interface helps you easily create, edit, delete, and re-use business entities. Business users can agree together on definitions, business rules, and policies in a dedicated workflow and share them with all data users to facilitate collaboration and data democratization.
Semantic classification uses artificial intelligence to simulate how people understand language and process information. Using pre-defined business entity definitions with semantic intelligence, you can quickly align data categorizations to your unique business needs. Rest assured that it will evolve with your needs while making data governance and analysis more efficient.
Spectrum Discovery’s Entity Wiki provides a central location for entity definitions, business rules, data quality rules, and stakeholder collaboration, giving data producers and consumers greater transparency around critical business entities for self-service consumption.
Monitor quality with scorecarding
You can’t fix data quality issues until you know with certainty what they are. Spectrum Discovery helps you monitor data quality KPIs regularly – based upon defined business rules – to pilot your approach, take appropriate action and share your progress with key stakeholders.
Automatically assess your enterprise master data compliance with the specific business rules you’ve defined at the field or data set level.
Get a complete evaluation of your data by proactively scorecarding your entire stock of data (rather than using a specific data set) to understand root cause and take immediate action. View scores in a browser-based dashboard or export as a PDF to share progress and provide insight across your organization.
Scorecard provides the needed KPI to regularly measure the Quality of your Data based upon defined Business Rules over a given period of time.
Lineage and impact analysis
Spectrum Discovery improves compliance by providing end-to-end lineage, so you have all the insights you need on data origins, movements, traceability, characteristics, and quality.
Investigate the lineage of any data asset down to the column level, across the data lifecycle. Easily view linkages to external data sources and targets, and gain insight into data structure and flow across the enterprise to trace data usage and measure the impact of system changes. By understanding the consequences of these changes, you’ll more easily guide decisions around data governance, business projects, and compliance.
Data lineage also helps you troubleshoot potential data issues faster by understanding the full journey your data has taken.
Column level lineage describes what happens to data from source to consumption to provide traceability of data assets.