Data Privacy

The graph database of the Adaptive platform allows customers to record not only metadata but also operational data itself. This unique and powerful capability enables the analysis of operational data within the context of the metadata, which provides both business and technical meaning to the data.

• Provide a comprehensive record of all Personally Identifiable Information (PII) data, detailing where it is stored, how it is moved, and which systems and applications utilize it.
• Relate data design to goals and processes, to ensure that the data is minimally sufficient.
• Define each relevant category of Personal Data, as a data classification, as defined by internal and external authorities. Privacy data classifications are made at the business level and automatically inferred at the logical and physical levels. This can be done using a bottom-up approach (e.g., identifying fields that resemble a Social Security number) or a top-down approach (e.g., fields named “SSN”; or “Social”).
• Capture essential metadata for each data element, to manage privacy policies:
  • Data subject
  • Date collected.
  • Authorized purpose(s)
  • Origin (person directly, purchased database, calculated, etc.)
  • Date updated and origin of update.
  • Date checked/confirmed and how (e.g., checked against an external database)
  • Next review date
  • Target disposal date
  • Data owner, steward, consumer, and other relevant roles
  • Browsable privacy regulations (GDPR or other relevant policies) can be traced to business terms.

Data Catalog Creation & Governance

• The data catalog functionality allows users to search, comment, rate, recommend, and subscribe to data sets. Users are automatically notified of any changes and other activities relating to the data sources and data sets they have subscribed to.
• Data profiling and usage statistics can be provided for data sets.
• The data catalog enhances the experience for BI analysts by helping them discover and utilize internal “approved” data sets that meet their requirements. It also facilitates collaboration in the overall data set on-boarding process.
• Power data users can leverage the catalog’s capabilities to manipulate arbitrary combinations of data across multiple data sets. This enables them to view personalized information while the data catalog provides essential characteristics of the data at-a-glance, such as certification level, quality level, ownership/stewardship, and content. This knowledge empowers users to combine diverse data sets for running specific and more sophisticated BI analyses.

Data Quality Management

Data quality rules and metrics can be defined at the level of business terms, eliminating the need to redundantly define them for every physical data element. Utilizing traceability relationships, Adaptive’s data quality rule execution capability can then automate the execution of these rules against all operational data where those business terms are implemented.

Manage Critical Data Elements

Data elements can be classified as critical and other significant classifications can be made, such as confidentiality, data retention, private, and others. This allows key data management processes like data governance and data quality to focus their efforts on the most important data elements. Search and query results can easily be filtered by selecting a classification such as critical to help users select the most important elements to work on. Diagrams generated to visualize data relationships can also be highlighted or filtered to focus on elements with a critical (or any other) classification.

Reference Data Management

• Manage a comprehensive list of reference data items, including designated responsibilities and usage locations.
• Establish and maintain new codes and code sets to ensure consistent versions are used throughout the system (golden source capability).
• Function as the operational source for real-time reference data access by other operational applications.

Data Lineage Analytics & Forensics

Adaptive bridges can automatically harvest the metadata of data transformations from ETL tools and other transformation technologies and languages. This enables lineage analysis to be performed and visualized automatically to include interactive diagrams that support drilldown. Lineage analysis helps users comprehend the origin of data, as well as the sequence of transformations and movements made to it until it is incorporated into a report or analytic.

Business Glossary (Single Point of Truth)

• Manage and store business terms and definitions in their respective business glossaries, and apply classifications to understand their specific purposes, usage, and characteristics.
• Map each term to an enterprise glossary, industry ontology, or business taxonomy to compare it with standardized definitions.
• Trace each term to its logical and physical data elements that implement it, enabling business users to quickly and easily discover all data storage locations that implemented the corresponding term.
• Provide immediate visibility to the business term(s) defining the meaning and purpose of a data element for technical users such as software developers, DBAs, and BI report writers.

Metadata Management

Metadata describing data in various systems, databases, data warehouses, data lakes, and cloud environments can be captured. Developers and business analysts can utilize the search, browse, and query functionality to find reusable data elements with ease. The search features encompass free text, wildcards, classifications, and creation/modification date, which expedites new development, minimizes redundancy, and enhances reuse.

Workflow

Automate the change approval process to ensure that any proposed modification to a business, logical, or physical data element undergoes the appropriate sequence of review and approval. Each reviewer is notified of a pending review via automated email subscriptions.

Accountability

Document all relationships (owner, steward, consumer, stakeholder, etc.) for each key data element, ensuring that everyone is aware of their responsibilities and roles in managing the data.