Graph Data Analytics

hurriedtinkleAI and Robotics

Nov 15, 2013 (3 years and 7 months ago)

59 views

Graph Data Analytics


www.globalids.com

Arka Mukherjee, Ph.D.

Global IDs


Arka.Mukherjee@globalids.com


Resolving Complexity at an Enterprise Scale

© 2013 Global IDs

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Proprietary

1

The
“Complex Data
” Context

Current Challenges

2

Governance Methodology

3

Topics

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

Financial

Services

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
)
14
Global IDs Software Products
Metadata
Governance Suite
Master Data
Governance Suite
Enterprise Data
Governance Suite
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12
11
10
9
8
7
6
5
4
3
2
1
Dashboards
Stewardship
Validation
Rules
Monitor
Model
Search
Map
Classify
Profile
Ingest
Discover
Big Data
Governance Suite
Move
Standardize
Create
Transparency
Improve
Quality
Accelerate
Integration
Integrate
Distribute
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16
18
17
Analyze
Measure
Embed
Analytics
Link
Visualize
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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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3
2
1
Deliverables
Under Development Using Hadoop Stack
Objective
Function
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

Multi

Structured

Data Content

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Converging
Data Ecosystems

Social

Data

Enterprise

Data

Machine

Data

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

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

Demo

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

Thank You!