Critical Success Factors
Upcoming SlideShare
Loading in...5

Critical Success Factors



Use open technology that facilitates tight integration between various systems. DW does not work without integrational synergies

Use open technology that facilitates tight integration between various systems. DW does not work without integrational synergies



Total Views
Views on SlideShare
Embed Views



1 Embed 2 2



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

Critical Success Factors Critical Success Factors Presentation Transcript

  • Critical Success Factors
    • Use open technology that facilitates tight integration between various systems. DW does not work without integrational synergies
      • Healthcare industry is burdened with loss of operational efficient and cost pressures arising out of the use of disparate environments
    • Architectural considerations – dimensional model (STAR schema) – provides fast query response and is easily understood by users, and very easily expanded when warehouse grows
    • Address administrative issues – what are we using the DW for? When should data not be added to the warehouse? How will the DW interact and interface with other IS initiatives?
  • Mechanisms: Consistent Critical Success Factors
    • Planning Processes
    • Performance Management
    • Partnership and Problem Solving
    • Geographic (or Neighbourhood Policing)
    • Operational and Demand Management
    • Community Management
  • Critical Success Factors
    • Meta-data management
    • Build vs. Buy considerations
    • Don’t forget HIPAA and privacy!
  • Critical Success Factors for M&A
    • Speed – Deliver tangible results as quickly as possible
    • Priorities – What needs to be done right away?
    • Precision – What exactly will the benefits of the merger be?
    • Communicate – It is never too much
    • Tools – fast and powerful analysis
    • Vision – clear long-term vision for the new entity
    • Culture – Avoid risk of losing key talent
    • Compliance – internal control environment
  • Critical Success Factors Strategy & Leadership For a Coactive Policing Style Inputs Community Leadership & Accountability (Social & Political) Structure Service Delivery Culture & Capacity Outputs Improved Public Outcomes
  • Critical Success Factors ( Inputs)
    • .
    • Strong Leadership
    • Setting out the Vision to move to a Coactive Style of Policing
    • Linking the Vision – to Strategy – to Implementation
    • Make it Reality not Rhetoric
    • Driving an Evidence-led Strategy
    Strategy & Leadership Raising the Game Raising the Game
  • Critical Success Factors ( Transforming #1)
    • .
    • Co-terminosity of boundaries with Partners
    • Shared and Distributed Activity
    • Information Exchange Protocols
    • Performance Management Processes
    Making a Difference
  • Critical Success Factors (Transforming #2)
    • .
    • Activity is based on Data Analysis (not data description)
    • Problem Solving Approach is at the heart
    • Action Plan with detailed responsibilities and timescales
    • Joint Tasking and Co-ordination
    • Regular Review
    Making a Difference
  • Critical Success Factors (Outputs and Feedback Loop)
    • .
    Culture and Capacity Reinforcing Success
    • Adequate Resourcing – human, financial and technological
    • Effective and ongoing Change Management
    • Effective and Supportive Human Relationship Management (Note – not human resource )
    • Continuous Improvement
  • Knowledge Discovery Process of non trivial extraction of implicit, previously unknown and potentially useful information from large collections of data
  • So What Is Data Mining? • In theory, Data Mining is a step in the knowledge discovery process. It is the extraction of implicit information from a large dataset. • In practice, data mining and knowledge discovery are becoming synonyms.
  • What Can Be Discovered? What can be discovered depends upon the data mining task employed. • Descriptive DM tasks Describe general properties • Predictive DM tasks Infer on available data
  • What kind of information are we collecting? • Business transactions • Scientific data (biology, physics, etc.) • Medical and personal data • Surveillance video and pictures • Satellite sensing • Games
  • (Con’t) • Digital media • CAD and Software engineering • Virtual worlds • Text reports and memos • The World Wide Web
  • Business Intelligence
    • Business Intelligence is the process of transforming data into information and through discovery transforming that information into knowledge”
    • Business Intelligence is a discipline of developing information that is conclusive, fact-based and actionable. Business Intelligence gives companies ability to discover and utilize information they already own, and turn it into the knowledge that directly impacts corporate performance”
    • STEPS
      • Gathering Data
      • Organizing and Storing Data
      • Analysis
      • Dissemination of Results
      • Decision Making and Action
    Business Intelligence
  • Business Intelligence Tools
    • Software that enables business users to see and use (analyze) large amounts of complex data.
        • Database Related
          • Data Storage Software
          • Query and Reporting Tools
        • Data Warehousing Related
          • Data Warehouse/ Data Mart Creation Tools
        • Data Mining Related
          • Data Mining Tools
        • Other Special Purpose Tools
  • Business Intelligent Solutions • BIS is a set of software products for: – Visualizing information – Data mining – Query formulation – E-commerce applications – Integrating heterogeneous data (data warehouses, portals)
  • Data Warehousing
    • Physical separation of operational and decision support environments
    • Purpose : to establish a data repository making operational data accessible
    • Transforms operational data to relational form
    • Only data needed for decision support come from the TPS
    • Data are transformed and integrated into a consistent structure
    • Data warehousing ( information warehousing ): solves the data access problem
    • End users perform ad hoc query, reporting analysis and visualization
  • Data Warehousing Benefits
    • Increase in knowledge worker productivity
    • Supports all decision makers’ data requirements
    • Provide ready access to critical data
    • Insulates operation databases from ad hoc processing
    • Provides high - level summary information
    • Provides drill down capabilities
    • Yields
    • Improved business knowledge
      • Competitive advantage
      • Enhances customer service and satisfaction
      • Facilitates decision making
      • Help streamline business processes
  • Data Warehouse Components
    • Large physical database
    • Logical data warehouse
    • Data mart
    • Decision support systems ( DSS ) and executive information system ( EIS )
  • Characteristics of Data Warehousing 1 . Data organized by detailed subject with information relevant for decision support 2 . Integrated data 3 . Time - variant data 4 . Non - volatile data
  • DW Suitability
    • For organizations where
    • Data are in different systems
    • Information - based approach to management in use
    • Large, diverse customer base
    • Same data have different representations in different systems
    • Highly technical, messy data formats