graph data analytics
DESCRIPTION
Graph Data Analytics. Resolving Complexity at an Enterprise Scale. www.globalids.com. Arka Mukherjee, Ph.D. Global IDs [email protected]. Topics. 1. The “Complex Data ” Context. 2. Current Challenges. 3. Governance Methodology. The “Complex Data” Context. The Big Shift. - PowerPoint PPT PresentationTRANSCRIPT
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Graph Data Analytics
www.globalids.com
Arka Mukherjee, Ph.D.Global IDs
Resolving Complexity at an Enterprise Scale
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1 The “Complex Data” Context
Current Challenges2
Governance Methodology3
Topics
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The “Complex Data” Context
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The Big Shift
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The cost structure is unsustainable
The cost of managing information is going up exponentially.
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The Complexity growth is unmanageable
1. Complex data ecosystems
2. Highly dynamic
3. Limited traceability
4. Systemic Risk : Hard to measure
FinancialServices
Institutions
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Question
How can Enterprises handle the cost and complexity of managing complex data landscapes ?
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Global IDs Focus
To organize enterprise data landscapes
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Global IDs: Product Suite
© Global IDs Inc. (2001-2013)
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Global IDs Software Products
MetadataGovernance Suite
Master DataGovernance Suite
Enterprise DataGovernance Suite
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Dashboards
Stewardship
Validation
Rules
Monitor
Model
Search
Map
Classify
Profile
Ingest
Discover
Big DataGovernance Suite
Move
Standardize
Create Transparency
Improve Quality
Accelerate Integration
Integrate
Distribute
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17Analyze
Measure
Embed Analytics
Link
Visualize
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20 Dashboards and Infographics
Graph Databases with Linked Data
KPIs and Trend Metrics
Reporting and Ad-Hoc Analysis
Data Services for Master Data
Integrated Master Data
Enriched Master Data
Data Repositories in Relational Databases or Hadoop
Master Data Governance Portals
RACI Matrix of Data Stewards
Data Quality Metrics
Rules Repository
Change Monitors, Impact Analysis
Master Data Models
Enterprise Search
Business Ontologies
Business Taxonomies
Semantic Metadata Repository
Inventory of External Data Assets
Comprehensive Data Asset Inventory
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Deliverables
Under Development Using Hadoop Stack
Objective Function
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Challenges
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The typical Financial Institution’s
# Databases > 1000
# Tables > 200,000
# Columns > 2,000,000
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Question
How can we understand the relationships across 2,000,000 attributes?
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Converging Data Variety
Structured
Unstructured
MultiStructured
Data Content
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Converging Data Ecosystems
SocialData
EnterpriseData
MachineData
Data Ecosystems
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Current Approaches do not Scale
# Databases > 1,000 > 10,000 > 100,000
Small Average Large
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A New Approach is Required
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5 Utilize Graph Structures for Governance
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Graph Analytics : Use Cases
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Key Challenges
• Vast diversity and volume of metadata and data
• Storage and indexing of metadata to facilitate search and navigation
• Understanding the connection between different pieces of metadata (Crosswalk)
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Utilize Graphs Structures for Storing Complex Data
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Use Case 1:Enterprise Metadata Search with Hadoop
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Use Case 2: Unstructured Data Integration
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Use Case 3: Cross Database Similarity Mapping
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Use Case 4 : Graph Analytics
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Demo
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Methodology
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What we do
1. Scan
2. Analyze
3. Map / Organize
4. Govern
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Automation
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1 : Scan
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2 : Semantic Analysis
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3 Automate Semantic Mapping
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4 Link the Data Landscape
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Thank You!