using information management to support data driven actions
DESCRIPTION
Traditional SQL and modern NoSQL data management technologies can transform the way we make our decisions.TRANSCRIPT
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
What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
Analytics-Making meaningful business decisions
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
Valu
e
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
Fact verification
Decision making
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
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
What is Information and why is it important to manage it
Data Life Cycle(collection, maturing, securing and managing)
Analytics-Making meaningful business decisions
Creation Acquisition AssessmentQuality
FrameworkIntegration
Delivery & Retention
Archiving Disposition
Data Governance
Data management policies/regulations
Classification Sensitive Vs Non Sensitive Data
Master data elements
Location based
Life CycleWhat 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
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
Metadata Dimensions
Categorization
Level of Detail
Types
Sources
Descriptive, Structural,
administrative
Business & Technical
metadata
IT systems, sources
documents
Contextual, logical,
physical
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
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
4. Quality of Actions
5. Quality of Results
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
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
Do
wn
Classification of elements
Data Quality Strategy
Robust Governance mode
Intended Vs Actual usage
Continuous improvement
Quality as part of SDLC
Regular year long audits
Data
Quality
Value
Control
&
Governance
Business
Processes
Data Movement
Data
Acquisition
Data
Standards
Data
Architecture
Data
QualityMetadata MDM
Data
Security
B2B
Information
Exchange
Mobility
Information
Access
control
Enterprise Content MgtmSocial
Media
SaaS/Web
Publishing
LOB
Data
Liaison-1
LOB
Data
Liaison-2
Data
steward-1
Data
steward-2
DG
AuditorsData owners
Business Sponsorship IT Sponsorship
Sco
pe
Role
sS
pon
sors
hip
Data/Information Life cycle management processes
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)
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
RDBMS
(Traditional structured data
Transform
Text
Analytics
Collection Layer
Business Users
Internal docs Media content Web content Machine Content
25%
75%
Strucured Data Unstrucured Data
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
Classification
Collection
Storage
And storage Geo distribution
Introduce
Structure
Store
Unstructured
Compliance
Analytics
Disposition
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
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
Wisdom
Knowledge
Information
Established KPIs
Transformed Data
Raw DataSilo data capture
& standalone reporting
Data collection, ETL, Storage
Pre built reports &
basic dashboards
OLAP analysis,
visualizations, sharing
Predictive modeling,
Co-relations & decision support
Smart business actions, prescriptive analytics changes
& Results
Total Ignorance
Limited
understanding
Improved
understanding
Insight
Robust
Awareness
Actions/Changes
Bu
sin
ess
Valu
e
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
Business
Analytics
Analytical Skills
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
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
Data
Coll
ecti
on
Data Quality
&
Prepared Data
Data Exploration
Pattern detection
Predictive
Engine
Predictive
Model
Prediction
Information
Action?
VariablesCritical
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