Introduction: The Need for Augmented Data Management

Traditional data management techniques are no longer adequate in today‘s data-driven world. Manual processes cannot scale, leading to poor data quality, inconsistent metrics and fragmented data silos. Augmented data management applies artificial intelligence and machine learning to automate mundane data tasks, enabling organizations to derive maximum value from data assets.

Specifically, augmented data management enhances traditional approaches in 4 key ways:

  1. Automating data quality with AI/ML algorithms
  2. Cataloging metadata and maintaining data lineage
  3. Providing a unified view across disparate sources
  4. Generating master data models and golden records

Let‘s explore each of these in more depth:

Challenges with Traditional Data Management Approaches

As organizations gather data from more systems and devices, traditional data management techniques falter:

  • Data is copied and stored redundantly across systems, leading to inconsistencies and conflicts.
  • With no centralized catalog, organizing and securing data becomes extremely difficult.
  • Menial tasks like backup and cleansing require enormous manual effort and time.
  • Lack of automated lineage between systems causes data silos and fragmentation.

According to a 2018 NewVantage Partners survey, only 24% of firms rated themselves as data-driven. Furthermore, 61.8% cited data quality as a key obstacle.

4 Ways Augmented Data Management Improves Traditional Techniques

1. Automated Data Quality

Maintaining high data quality is an arduous manual task. Augmented solutions apply machine learning algorithms to automatically:

  • Detect anomalies and inconsistencies
  • Identify duplicate, stale or erroneous data
  • Initiate corrective actions like data repairs, normalization and deduplication

Techniques like probabilistic matching, text analysis and semantic integration ensure significantly higher data integrity.

Data Quality TechniqueTraditional DMAugmented DM
Duplicate detectionManualAutomated ML techniques
Anomaly detectionHuman monitoringContinuous automated analysis
Cleansing and repairIT-drivenInitiated automatically by system

According to Gartner, use of AI/ML-driven data quality tools will increase 30% annually through 2023.

2. Automated Metadata Cataloging and Organization

Metadata provides invaluable context about data entities – improving discovery, management and governance. However, manually capturing metadata is incredibly tedious.

Augmented solutions auto-collect metadata from systems using techniques like:

  • Object tagging and annotations
  • Schema and identity mapping
  • Parsing unstructured data with NLP

This metadata builds a knowledge graph depicting relationships between data points. This powers intelligent data catalogs that support:

  • Streamlined search and discovery
  • Impact analysis across sources
  • Granular access control enforcement
  • Proactive data quality monitoring

According to a 2022 Unisphere survey, 95% of organizations have an active metadata management initiative planned or underway.
Metadata management grid showing how augmented solutions enhance metadata organization and use

3. Unified View of Disparate Data

Traditionally, data resides in isolated silos across an organization‘s systems and databases. This fragmentation severely limits data utility.

Augmented data management applies semantic integration techniques to provide analysts with a unified view by:

  • Mapping semantic models across sources
  • Providing integrated access to distributed data
  • Delivering blended data sets via automated ETL pipelines

This enables data scientists to derive unified insights from all data sources rather than just individual silos.

4. Automated Master Data Model Generation

Master data management (MDM) involves consolidating data entities into a "golden record" that serves as a single source of truth. But manual MDM is wildly complex at scale.

Augmented solutions can parse data from multiple systems and automatically generate master data models using entity resolution techniques like:

  • Probabilistic matching of related entities
  • Hierarchical and graph-based relationship modeling
  • Referential integrity and policy mapping

This provides analysts the accurate, holistic information they need – without extensive engineering overhead.

Conclusion

Rather than expend significant resources on tedious data management tasks, organizations must embrace intelligent augmented solutions. Automating data quality, metadata, integration and MDM allows analysts to focus on delivering strategic value from data. With data as the key competitive asset today, adopting an augmented approach is imperative.

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