Revolutionize Data Management with Data Fabric

Data Fabric

Data fabric transform raw inputs into organized datasets suitable for analytics, AI and transactional systems through validation, enrichment, aggregation and integration techniques.

Data Validation

Verify records contain expected attributes formatted per defined standards and flag outliers for remediation through automated verification rules and checks.

Data Cleansing

Detect and resolve common quality issues like mismatches, duplicates, inconsistencies or invalid entries through algorithms leveraging pattern analysis and machine learning.

Data Enrichment

Augment core records by joining reference details from internal or external sources to provide fuller context for insights.

Data Aggregation

Consolidate fine-grained transactional data into higher-level metrics organized by relevant dimensions for summaries and trends.

Case study 1

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Go to Use Case Title

What Are the Advantages You Should Expect?

Reliability

Uniformly prepared datasets yield robust, reproducible analytics impervious to minor source variations over time.

Usability

Higher quality data simplifies consumer tasks from exploration to modeling.

Governance

Metadata tracks processing lineage enabling audits and maintenance of data provenance.

Efficiency

Automated routines keep data fresh with a fraction of manual effort.

Insight

Richer dimensionality reveals relationships and patterns previously obscured by noise or lack of scale.

Data Fabric

Data fabric transform raw inputs into organized datasets suitable for analytics, AI and transactional systems through validation, enrichment, aggregation and integration techniques.

Case study 1

Go to Use Case Title

What Are the Advantages You Should Expect?

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