2024: Domino Containers - The Next Step. News from the Domino Container commu...
Using information management to support data driven actions
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
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