Data Governance

Data Governance is about changing organizational culture by implementing process to ensure the proper inventory and oversight of data semantics and quality ,transparency into data controls and metrics, accountability for data and timely resolutions of data related issues and questions

Technologies

Data Governance

Services

Data Literacy

Improve data literacy in your organization through structured programs and tools like the Business Glossary

Data Quality

Improve the quality of data with the Data Quality Improvement Lifecycle

Privacy Compliance

Companies can ensure that they are 100% compliant with privacy laws like the GDPR & CCPA

Lineage, Impact Analysis

Data Engineers can understand the impact of any changes they are due to make

Data Catalog

Everyone can easily find and understand data from dispersed data sources

Data Access Management

Implement unified access in thousands of data assets and millions of attributes

Everyone can easily find and understand data from dispersed sources

  • Search using natural language to find a specific dataset.
  • Find and work on multiple datasets
  • Get notified when anything of interest to you changes.
  • Obtain a holistic understanding of data by looking at accurate definitions, statistics, relationships, lineage, top users, your custom metadata, and much more.
  • Automatically create a centralized repository for all data sources.
  • Manage the curation process by assigning work to stewards and monitoring their progress

Implement unified access in thousands of data assets and millions of attributes

  • Implement a shopping cart experience for your end-users
  • All of your users can easily find, understand and retrieve data from dispersed sources
  • Manage your data into different groups and provide access to different roles
  • Use human-assisted, AI-driven classification to classify millions of attributes into PII, Confidential, Secret, and more
  • Automatically mask and restrict data based on classification
  • Centrally manage access to multiple data platforms using unified policies
  • Connect to all major data warehouses and data lakes like Snowflake, Databricks, S3, Redshift, and Big Query

Data Engineers can understand the impact of any changes they are due to make.

  • Compare your schema by environment or by date
  • Visualize the impact of any attribute graphically, upstream or downstream
  • Track the changes of multiple data objects using an intuitive Excel-style tool
  • Understand data flow at the schema level, table level, and column level
  • Notify the right stakeholder—downstream consumer/decision maker—when a tiny change might impact them, like the removal of a field .
  • Automatically connect data from one source to another at the column level using state-of-the-art code parsing algorithms

Improve data literacy in your organization through structured programs and tools like the Business Glossary

  • Easy to use Business Glossary to search, navigate and collaborate
  • The Business Glossary is integrated with BI tools, so definitions are available when you need them most
  • Manage the entire workflow of approvals and keep the communication within context
  • Use version control, workflows, RACI, and many more features to operationalize the Business Glossary
  • Build agreements about term definitions using tools like:
    • Search & Discovery
    • Impact Analysis
    • Various KPIs and Metrics

Companies can ensure that they are 100% compliant with privacy laws like the GDPR & CCPA

  • Use human-assisted, AI-driven classification to classify millions of attributes into PII, Confidential, Secret, and more
  • Maintain classifications with alerts and monitoring to keep them up to date
  • Create a PII data report for your auditor, including all PII data, its lineage, responsibilities, and more
  • Support "Right to Know" and "Right to Forget" using innovative features like Governed Data Query
  • Customize workflows to support Record of Processing Activities (ROPA)
  • AI algorithms are built on top of a Data Catalog, taking advantage of curated information and data lineage
  • Full support for any data asset, including files, tables, reports, and more

Improve the quality of data by implementing a proactive and reactive data quality improvement approach.

  • Implement Data Quality Rules to identify any violation of pre-configured protocols
  • Integrated catalog for end-users to report data quality problems
  • Workflows for routing the data quality problem to the right stakeholders
  • Automatic alerts and communication to decision makers in case of data quality issues
  • Dashboards help users understand the overall quality of data and its trends
  • DQ DAMA Dimensions to measure the Data health
  • Business Driven Data quality KPI’s based on need