Traditional SQL and modern NoSQL data management technologies can transform the way we make our decisions.

1. By Manoj Vig
manojvig@gmail.com
http://www.linkedin.com/in/manojvig

2. 1 What is Information and why is it important to manage it
2 Data Life Cycle(collection, maturing, securing and managing)
3 Analytics-Making meaningful business decisions

3. What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
Analytics-Making meaningful business decisions

4. Wisdom
Knowledge
Information
Data
 Information makes sense of data
 Information is a message
 Brain reacts to Information demand
 Information guides decision making
 Information is everywhere
 Information can “manage” you
DIKW Source - Wikipedia
Value

5.  Guess based decisions are too risky
 Enough information to support facts
 Brand value and credibility
 Prediction and control
 Follow facts/data and not opinions
Performance management- You can
not control what you can not measure
Data
Collection
Data Maturity
Process
Information
Creation
Analysis and
Exploration
Decision
making
Fact
verification

6. Metadata
• Business
• Technical
Master Data
• Customers
• Products
• Accounts
• Location
Operational
Data
• Internal
• External/Cloud
Unstructured
Data
• Emails
• Scanned docs
• Vendor data
Analytical Data
• Historical
• Transformed
• Strategic
 Metadata is the foundation of complete reference model
 Master data will enable “Single Version of the truth”
 Operational data reflects actual business transactions
 Unstructured data is untapped wealth of information
 Analytical data will eventually be used to make strategic decisions

7.  One of the biggest data centric business domains
 Fuel for innovation
 Patient safety and wellness
 Regulations and compliance
Discovering new opportunities
 Risk reduction and mitigation
Critical business processes and velocity of information changes
 Competitive intelligence
Dependencies on external data (e.g. Call activity, physician usage, IMS data)
 Influx of new information sources and explosion of data

8. What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
Analytics-Making meaningful business decisions

9. Data management policies/regulations
Creation Acquisition Assessment
Quality
Framework
Integration
Delivery &
Retention
Archiving Disposition
Data Governance

10.  Classification
 Sensitive Vs Non Sensitive Data
 Master data elements
 Location based
Life Cycle
 What to retain and archive
 How long to archive
 Value assessment policies
 Disposition
Security
 Storage/masking
Ownership and usage
Mobile usage management
Delivery
 External distribution
 Governance policies
 Analytics/Reports
Classification
Security
Life Cycle
Delivery

11.  More then “data about data”
 Metadata management strategy
 Holy grail of consistency
 Realization of Data governance vision
 Risk management and IT agility
 Applications
Data lineage
Impact analysis
Delivery speed
Business glossary and source identification
Categorization
Metadata
Dimensions
Level of
Detail
Types
Sources
Descriptive, Structural,
administrative
Business & Technical
metadata
IT systems, sources
documents
Contextual, logical,
physical

12. What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
 Data Quality – Building trust in data and information
The Impact of Unstructured data
Analytics-Making meaningful business decisions

13.  Encourages Fact based decision making
 Trusted data is a true asset
 Business and IT interaction
 High cost of opportunity
 Proactive risk management
 Regulations & audit requirements
1. Quality of
Data
2. Quality of
information
3. Quality of
Decisions
5. Quality of
Results
4. Quality of
Actions

14. Assess Define Act Learn
 Define Data Map
 Data Standards
 Profile Data
 Identify Sources
Classification
 Rules
 Policies
 Tolerance
 Rule Ownership
 Validation process
 Standardization
 Rule application
 Measurement
 Quality reports
 Trend dashboard
 Policy dashboard
 Domain dashboard

15.  Preventive technique
 Improves ROI and reduces TCO
Data anomaly detection
 Data Quality Rule identification
 Data Reverse engineering
 Metadata Analysis
 Domain discovery
 Classification of Issues
Drill Down

16.  Classification of elements
 Data Quality Strategy
 Robust Governance mode
 Intended Vs Actual usage
 Continuous improvement
 Quality as part of SDLC
 Regular year long audits
Value
Data
Quality
Control
&
Governance
Business
Processes
Data
Movement

17. Data
Acquisition
Data
Standards
Data
Architecture
Data
Quality
Metadata MDM
Data
Security
B2B
Information
Exchange
Mobility
Information
Access
control
Enterprise Content Mgtm
Social
Media
SaaS/Web
Publishing
LOB
Data
Liaison-1
LOB
Data
Liaison-2
Data
steward-1
Data
steward-2
DG
Auditors
Data owners
Business Sponsorship IT Sponsorship
Scope Roles Sponsorship
Data/Information Life cycle management processes

18.  Improved Business insight
 Information/Data ownership
 Establishing Decision points
 Securing critical information
 Compliance with regulations
 Better alignment with objectives
Organizational
Culture
• Align with business model
• Assess organizational maturity
• Consider cross functional agenda
Sponsorship
• Strong executive sponsorship
• Business should own the framework
• IT should manage the framework
• Tie with real benefits (e.g. reduction in cost)
Execution
• Establish a hybrid implementation approach
• Can start small and expand
• Establish clear roles and authorities
• Integrated process (with SDLC)
• Constantly educate people (IT + Business)

19. What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
 Data Quality – Building trust in data and information
The Impact of Unstructured data
Analytics-Making meaningful business decisions

20. RDBMS
(Traditional structured data
Transform
Text
Analytics
Collection Layer
Business Users
Internal docs Media content Web content Machine Content

21. Strucured Data Unstrucured Data
25%
75%
Less or no control
More Control
 Amount of data/Information
 Lack of Control
 Growth Projections
 Impact of Web content
 360 degree view
 Significant improvement in business
insight (Structured +Unstructured)
 Competitive intelligence

22. Disposition
Analytics
Compliance
Storage
Introduce
Structure
Store
Unstructured
And storage Geo distribution
Collection
Classification
Architectural
• Create a Reference
Architecture
• Define integration
processes
• Establish storage
framework
• Select appropriate
technology
Governance
• Establish ownership
• Metadata integration
points
• Establish Quality
business rules points
• Govern raw,
transformed and
analytical usage
Compliance
• Establish social
media policy
• Compliance with
FDA and other
regulatory
• Sensitivity towards
internal regulations

23. What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
 Data Quality – Building trust in data and information
The Impact of Unstructured data
Analytics-Making meaningful business decisions

24. Wisdom
Knowledge
Information
Smart business actions, prescriptive analytics changes
& Results
Established KPIs
Transformed Data
Predictive modeling,
Co-relations & decision support
OLAP analysis,
visualizations, sharing
Pre built reports &
basic dashboards
Data collection, ETL, Storage
Raw Data Silo data capture
& standalone reporting
Actions/Changes
Robust
Awareness
Insight
Improved
understanding
Limited
understanding
Total Ignorance
Business Value

25. Query, reporting
Pre defined
questions
OLAP Analysis,
Drill downs, Power
analysis
Predictive analytics,
scenario modeling,
visualizations
Prescriptive Analytics,
Fact based recommendations,
Something
happened
Why did it
happen
What will happen?
What can we do
To make it happen

26. Analytical
Skills
Business
Analytics
Business
Knowledge
Statistical
Knowledge
Technical
Knowledge
 Business analytics is a function
 It is ever evolving
 Should be seen as a strategic asset
 As good as domain knowledge of
resources
 Technology should follow Analytics
strategy and not other way around
 Depends on Data quality &
information delivery layer
 Requires Analytic/Information
governance

27. What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
 Data Quality – Building trust in data and information
The Impact of Unstructured data
Analytics-Making meaningful business decisions
 Predictive Analytics

28. Data Collection
Data Quality
&
Prepared Data
Data Exploration
Pattern detection
Predictive
Engine
Predictive
Model
Prediction
Information
Action?
Variables
Critical
 A framework to predict the likelihood of events
 Depends on established statistical models and avoid guess work
 Creates an experience of personalization
 PA is different from traditional BI but can be an extension
 Reporting/dashboards can tell you what happen & why it happened
 PA can use same data and many variables to “forecast” what may happen