Organizations must onboard new data sources more frequently and quickly. In this presentation, you will learn about practices that rapidly deliver business value, while shrinking time to business value from months to days. Business decisions are becoming increasingly dependent on analyzing an ever-greater volume of data coming from a growing number of sources. Mobile technology is providing immediate access to data whenever and wherever it is needed. Users, customers, and business partners are waiting for answers, and the organization must reduce the time required to collect, understand, and analyze the data needed to provide those answers. Modern enterprises need to increase the agility, flexibility, and speed with which they can analyze a growing volume, variety, and velocity of data. This presentation discusses a method for rapid data integration and curation: - Techniques for light data integration of new data with existing data assets - Framework for data quality management - Refining data integration through evolutionary modeling - Managing curation processes - Validating business value Timely delivery of new data assets allows users to begin asking questions earlier and getting answers more quickly, allowing the organization to uncover the new insights that drive lasting business benefits.

1. Rapid Data Integration
and Curation
Delivering Business Value in the First 24 Hours
SPEAKER:
Thomas Kelly, Practice Director
Semantic Technology Center of Excellence
Enterprise Information Management
Cognizant Technology Solutions, Inc.
| ©2013, Cognizant

2. Agenda
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BARRIERS TO RAPID DATA INTEGRATION
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DELIVERING BUSINESS VALUE
RAPID DATA INTEGRATION AND
CURATION METHOD

3. We are at an Inflection Point at which Value is Created or
Destroyed
Source : The Motley Fool
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4. Delivering Information Faster Produces Direct, Measurable
Business Value
What Difference Does One Day Make?
A blockbuster drug generates $3M+ in
revenue per day; a one-day delay in
completing clinical trials can generate up
to $500K in additional costs
Banking
A moderate-sized brokerage firm can
generate up to $1M in financial services
revenue per day
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5. Barriers to Rapid Data Integration
Rework is expensive –
must “get it right” from
the start
Fit with the existing
data; avoid data silos
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Reconciling differences
(data formats, coding,
identifiers, etc.)
Managing data quality
(accuracy, precision,
context)
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Knowledge acquisition
takes time; new insights
come from
experimentation
Overcoming process
inertia

6. Evolutionary Method to Data Integration and Curation
Responsive
Data
Approach
• As new information flows into the
enterprise, people and processes are
dynamic in nature
• Questions arising during this phase
are “what to do” and “how to make
the best sense of the new data
source”. Rapid integration tools will
aid in quick prototyping and building
solutions of value
Rapid
Integration and
Curation
Method
• The data is profiled and explored for
value and quality issues.
• A rapid pruning exercise is
undertaken by prototyping and
integrating with in-house data to
evaluate if data is fit for purpose. It
influences in formulating a effective
approach for further phases.
Information
Management
Approach
Time
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Managed
• As we progress, issues with the new
data are identified and managed.
The main focus is on establishing
data quality and adhering to
enterprise standards and
frameworks while building optimal
integration approaches
• The integration process is
evolutionary as further discoveries
are made for optimal design
Evolutionary
• Progressive build based on the new
data.
• Building awareness of the new
platform and fine tuning the
capabilities around the data source
are primary activities
Proactive
• Data management evolves to a morerefined state. A feedback loop is built
to enable proactive decisions around
data organization and access.
• Data integration is efficient and
stable. Verifiable compliance and
security.
• Integrated with the enterprise
information management framework
Predictable
• The services built around the new
data sources are now managed.
• The focus is on evolution of business
processes, based on managed models
Tactical
Progressive
Managed
First 1-5 Days
First 1 -3 Months
After 3 months

7. Leverage Insights and Expertise, Rapidly and Sustainably
Identify and leverage
existing, relevant data
assets and expertise
Ingest new data
sources (light
integration and
curation)
Reuse Expertise
Analyze
Monitor and measure
use and benefits
achieved; identify next
set of priorities
Realize
Benefits
Extend
Create and extend data
relationships,
leveraging insights from
previous study cycles
Govern
Elevate proven data,
relationships, and expertise
to organization-wise
definition
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Refine
Capture insights from new data
analysis cycles, refining
relationships to support new
analytics

8. Can You Help Me With Some Data?
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9. Rapid Data Integration and Curation Method
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Define Preliminary Objectives
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Profile the New Data
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Generate Initial Ontology for the New Data
Generate Initial Ontology for the Existing
Data (if necessary)
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5
Integrate Entities over Common URIs
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Create URI Links
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Add Initial Data Quality Filters
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Analyze Data and Generate Feedback

10. 1. Define Preliminary Objectives
1. Discuss Functional and Timing Objectives, and
Priorities
2. Clarify Immediate, Short-Term, and Long-Term
Business Value (SMART *)
a. Cost Reduction/Avoidance
b. Meet Critical Customer Need
3. Is This the Right Solution?
4. Set Expectations
a. Evolutionary Process
b. Initial Results Quickly
c. Frequent, Active Participation
d. Feedback Critical to Making Refinements
5. Brainstorm Deliverables that Produce Business
Benefits; Define a Few Sample Queries
6. Ask for Commitment to Benefits Realization
7. Start the Clock!
* SMART -- Specific, Measurable, Attainable, Realistic, and Traceable
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11. 2. Profile the New Data
Light Profiling, focusing on
Understanding Key Data Elements
Needed to Meet the First
Deliverable
Identify Initial Data Filtering
Candidates
Capture Insights about Key Data
Relationships
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12. 3. Generate Initial Ontology for the New Data
Reverse-engineer Ontology from
New Data
Load New Data into the RDF Store
(or Create Link to the Data)
Create Business-relevant Synonyms
for High-Importance Attributes
Refinements will be made in
Future Iterations
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13. 4. Generate Initial Ontology for the Existing Data (if necessary)
Map Selected Entities and Critical
Attributes for Existing Data Source(s)
to the Source-specific Ontology
Existing
Data
New
Data
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Add Reference to the Source-specific
Ontology to the New Data Ontology
Refinements will be made in
Future Iterations
New Data Ontology manages
integration with Existing Data until the
ontology is sufficiently mature to be
promoted into an enterprise ontology

14. 5. Integrate Entities over Common URIs
Different URIs, Separately
Maintained
Focus on Key Entities
Equivalence Functions Logically
Integrate the Federated Data
Reduces Query Complexity and
Can Improve Query Performance
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15. 6. Create URI Links
Geography
Customer
cust:ZipCode
JOIN
geo:ZipCode
Geography
Customer
cust:ZipCodeURI
LINK
The Data has Common Values that
can be used in Join Operations, but
Doesn’t have Links
Links Reduce Query Complexity
and Can Improve Query
Performance
Focus on Key Queries, Identify
Complex or Time-Sensitive Joins
Add Linking URI Attribute to
Dependent Entity
Amend Selected Queries to
Leverage the New Link
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16. 7. Add Initial Data Quality Filters and Transformations
Traditional Data Warehouse
Data Quality
Happens Here
Data Quality
Happens Here
Data
Source A
Data
Source B
Data
Source C
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Existing
Data
ETL
New Data
And
Data Here
Warehouse
JIT Data Quality Management,
Everywhere that it is Needed
Data Filtering and Transformation
Rules are Encoded in the Ontology
Focus is on Critical Data
Quality Rules
Rule Updates are Automatically
in Effect, without Reloading All
of the Data

17. 8. Analyze Data and Generate Feedback
Demonstrate Visualization using
Sample Queries
Walk Through Available Data
Sets and Data Organization
Experiment with Data Access
and New Visualizations
Provide Next Steps
Recommendations to Refine the
Data Integration and Curation
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18. Architectural Foundation for Rapid Data Integration and Curation
SPARQL-based Visualization
Relational-to-RDF Mapping
Data Profiling
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Ontology Editor
Automated Ontology Generation
RDF Store
Data Import
RDF Store

19. Capabilities That We Have Introduced
Rapid Response to New Data
Onboarding Needs
Process for Evolutionary Data
Integration and Curation
Flexible Design that is
Responsive to Business Changes
Foundation for Refinement and
Expansion of Ontology Models from
Fit-for-Purpose to Department, to
Business Unit, to Enterprise
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20. Questions?
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21. Thank you!
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22. Speaker
Thomas (Tom) Kelly
Practice Director, Enterprise Information Management, Cognizant
Thomas Kelly is a Director in Cognizant’s Enterprise
Information Management (EIM) Practice and heads its
Semantic Technology Center of Excellence, a technology
specialty of Cognizant Business Consulting (CBC). He has
20-plus years of technology consulting experience in
leading data warehousing, business intelligence and big
data projects, focused primarily on the life sciences and
healthcare industries. Tom can be reached at
Thomas.Kelly@cognizant.com.
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| ©2013, Cognizant