Data Governance and Lineage

Apiro addresses aspects of data storage, governance, and lineage. They enhance security, privacy, flexibility, and overall data management capabilities in a complex data processing environment.

Internal NoSQL Data Store:

  • Flexibility for Unstructured Data: NoSQL data store provides flexibility for managing unstructured or semi-structured data, accommodating diverse data formats and structures.
  • Scalability: NoSQL databases are often designed for horizontal scalability, enabling the platform to handle growing volumes of data efficiently.
  • Schema-less Design: The schema-less design of NoSQL databases allows for agile development and accommodates evolving data requirements.

Metadata Support at the Data Point Level:

  • Granular Metadata Management: Metadata at the data point level enables granular management, providing detailed information about each piece of data.
  • Improved Data Discovery: Detailed metadata enhances data discovery, making it easier for users to understand and locate relevant data points.
  • Impact Analysis: Data point-level metadata supports impact analysis by identifying dependencies and relationships between different data points.

PII Classification:

  • Compliance with Privacy Regulations: PII classification helps ensure compliance with privacy regulations by identifying and safeguarding personally identifiable information.
  • Risk Mitigation: Enables the platform to implement appropriate security measures and access controls to protect sensitive PII data.
  • Data Governance Enhancement: PII classification strengthens overall data governance efforts by highlighting and managing sensitive data elements.
  • Data Masking:

    • Privacy Protection: Data masking protects sensitive information by replacing or obscuring actual data with masked values.
    • Secure Testing Environments: Supports the creation of secure testing environments by masking sensitive data, allowing testing without exposing confidential information.
    • Risk Reduction: Reduces the risk of unauthorized access to sensitive data, enhancing overall data security.
  • Data Encryption at Rest:

    • Confidentiality Assurance: Encryption at rest ensures the confidentiality of stored data by encrypting it on disk or storage media.
    • Regulatory Compliance: Meets regulatory requirements for protecting data, especially in sensitive industries like healthcare and finance.
    • Risk Mitigation: Protects against unauthorized access or data breaches by securing data even when it’s not actively in use.

Synthetic Data Generation:

  • Data Privacy in Testing: Enables the generation of synthetic data for testing purposes, preserving data privacy and compliance.
  • Performance Testing: Synthetic data supports performance testing scenarios with large datasets without exposing real data.
  • Useful for Training Models: Synthetic data can be valuable for training machine learning models when real data is limited or sensitive.

Comprehensive Data Change Log (Data Point Level):

  • Auditability: A comprehensive change log at the data point level provides detailed audit trails, enhancing accountability and compliance.
  • Troubleshooting: Facilitates troubleshooting by tracking changes in data, aiding in the identification and resolution of issues.
  • Historical Analysis: Supports historical analysis by capturing every change, allowing users to analyze the evolution of data over time.

 

Metadata Processors:

  • Automated Metadata Management: Metadata processors automate the extraction, transformation, and loading of metadata, reducing manual efforts.
  • Consistency: Ensures consistency in metadata across the platform, preventing discrepancies and improving overall data quality.
  • Integration with Governance Policies: Metadata processors can be configured to align with data governance policies, ensuring metadata adherence to defined standards.

 

Bitemporal Data (Time Machine):

  • Temporal Analysis: Bitemporal data enables temporal analysis, allowing users to analyze data changes over specific time periods.
  • Historical Reconstruction: Facilitates historical reconstruction by preserving and managing historical versions of data.
  • Regulatory Compliance: Supports regulatory compliance by providing a clear timeline of data changes and activities.

 

Data Deduplication:

  • Storage Optimization: Data deduplication reduces storage redundancy by identifying and eliminating duplicate data.
  • Data Quality Improvement: Enhances data quality by preventing the storage of redundant or inconsistent information.
  • Efficient Resource Utilization: Optimizes resource utilization by minimizing the storage footprint of duplicated data.

 

Fuzzy Logic:

  • Flexible Matching: Fuzzy logic enables flexible matching of similar or approximate data, accommodating variations or errors.
  • Improved Data Matching: Enhances data matching and linkage, even when exact matches are not available or feasible.
  • Data Quality Enhancement: Supports data quality by allowing for more accurate and comprehensive data matching.

 

Global Asset ID Generation:

  • Unique Identifiers: Global asset ID generation ensures the creation of unique identifiers for assets, avoiding conflicts and ensuring consistency.
  • Cross-System Integration: Enables cross-system integration by providing a standardized way to reference and identify assets globally.
  • Traceability: Enhances traceability by associating a unique ID with each asset, facilitating tracking and auditing activities.

Apiro’s data platform provides data integration, transformation and distribution features from and to any source or destination and format.

 

Makes sense?