Critical Success Factors

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

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Critical Success Factors

  1. 1. Critical Success Factors <ul><li>Use open technology that facilitates tight integration between various systems. DW does not work without integrational synergies </li></ul><ul><ul><li>Healthcare industry is burdened with loss of operational efficient and cost pressures arising out of the use of disparate environments </li></ul></ul><ul><li>Architectural considerations – dimensional model (STAR schema) – provides fast query response and is easily understood by users, and very easily expanded when warehouse grows </li></ul><ul><li>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? </li></ul>
  2. 2. Mechanisms: Consistent Critical Success Factors <ul><li>Planning Processes </li></ul><ul><li>Performance Management </li></ul><ul><li>Partnership and Problem Solving </li></ul><ul><li>Geographic (or Neighbourhood Policing) </li></ul><ul><li>Operational and Demand Management </li></ul><ul><li>Community Management </li></ul>
  3. 3. Critical Success Factors <ul><li>Meta-data management </li></ul><ul><li>Build vs. Buy considerations </li></ul><ul><li>Don’t forget HIPAA and privacy! </li></ul>
  4. 4. Critical Success Factors for M&A <ul><li>Speed – Deliver tangible results as quickly as possible </li></ul><ul><li>Priorities – What needs to be done right away? </li></ul><ul><li>Precision – What exactly will the benefits of the merger be? </li></ul><ul><li>Communicate – It is never too much </li></ul><ul><li>Tools – fast and powerful analysis </li></ul><ul><li>Vision – clear long-term vision for the new entity </li></ul><ul><li>Culture – Avoid risk of losing key talent </li></ul><ul><li>Compliance – internal control environment </li></ul>
  5. 5. 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
  6. 6. Critical Success Factors ( Inputs) <ul><li>. </li></ul><ul><li>Strong Leadership </li></ul><ul><li>Setting out the Vision to move to a Coactive Style of Policing </li></ul><ul><li>Linking the Vision – to Strategy – to Implementation </li></ul><ul><li>Make it Reality not Rhetoric </li></ul><ul><li>Driving an Evidence-led Strategy </li></ul>Strategy & Leadership Raising the Game Raising the Game
  7. 7. Critical Success Factors ( Transforming #1) <ul><li>. </li></ul>Structure <ul><li>Co-terminosity of boundaries with Partners </li></ul><ul><li>Shared and Distributed Activity </li></ul><ul><li>Information Exchange Protocols </li></ul><ul><li>Performance Management Processes </li></ul>Making a Difference
  8. 8. Critical Success Factors (Transforming #2) <ul><li>. </li></ul>Delivery <ul><li>Activity is based on Data Analysis (not data description) </li></ul><ul><li>Problem Solving Approach is at the heart </li></ul><ul><li>Action Plan with detailed responsibilities and timescales </li></ul><ul><li>Joint Tasking and Co-ordination </li></ul><ul><li>Regular Review </li></ul>Making a Difference
  9. 9. Critical Success Factors (Outputs and Feedback Loop) <ul><li>. </li></ul>Culture and Capacity Reinforcing Success <ul><li>Adequate Resourcing – human, financial and technological </li></ul><ul><li>Effective and ongoing Change Management </li></ul><ul><li>Effective and Supportive Human Relationship Management (Note – not human resource ) </li></ul><ul><li>Continuous Improvement </li></ul>
  10. 10. Knowledge Discovery Process of non trivial extraction of implicit, previously unknown and potentially useful information from large collections of data
  11. 11. 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.
  12. 12. 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
  13. 13. 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
  14. 14. (Con’t) • Digital media • CAD and Software engineering • Virtual worlds • Text reports and memos • The World Wide Web
  15. 15. Business Intelligence <ul><li>Business Intelligence is the process of transforming data into information and through discovery transforming that information into knowledge” </li></ul><ul><li>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” </li></ul>
  16. 16. <ul><li>STEPS </li></ul><ul><ul><li>Gathering Data </li></ul></ul><ul><ul><li>Organizing and Storing Data </li></ul></ul><ul><ul><li>Analysis </li></ul></ul><ul><ul><li>Dissemination of Results </li></ul></ul><ul><ul><li>Decision Making and Action </li></ul></ul>Business Intelligence
  17. 17. Business Intelligence Tools <ul><li>Software that enables business users to see and use (analyze) large amounts of complex data. </li></ul><ul><ul><ul><li>Database Related </li></ul></ul></ul><ul><ul><ul><ul><li>Data Storage Software </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Query and Reporting Tools </li></ul></ul></ul></ul><ul><ul><ul><li>Data Warehousing Related </li></ul></ul></ul><ul><ul><ul><ul><li>Data Warehouse/ Data Mart Creation Tools </li></ul></ul></ul></ul><ul><ul><ul><li>Data Mining Related </li></ul></ul></ul><ul><ul><ul><ul><li>Data Mining Tools </li></ul></ul></ul></ul><ul><ul><ul><li>Other Special Purpose Tools </li></ul></ul></ul>
  18. 18. 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)
  19. 19. Data Warehousing <ul><li>Physical separation of operational and decision support environments </li></ul><ul><li>Purpose : to establish a data repository making operational data accessible </li></ul><ul><li>Transforms operational data to relational form </li></ul><ul><li>Only data needed for decision support come from the TPS </li></ul><ul><li>Data are transformed and integrated into a consistent structure </li></ul><ul><li>Data warehousing ( information warehousing ): solves the data access problem </li></ul><ul><li>End users perform ad hoc query, reporting analysis and visualization </li></ul>
  20. 20. Data Warehousing Benefits <ul><li>Increase in knowledge worker productivity </li></ul><ul><li>Supports all decision makers’ data requirements </li></ul><ul><li>Provide ready access to critical data </li></ul><ul><li>Insulates operation databases from ad hoc processing </li></ul><ul><li>Provides high - level summary information </li></ul><ul><li>Provides drill down capabilities </li></ul>
  21. 21. <ul><li>Yields </li></ul><ul><li>Improved business knowledge </li></ul><ul><ul><li>Competitive advantage </li></ul></ul><ul><ul><li>Enhances customer service and satisfaction </li></ul></ul><ul><ul><li>Facilitates decision making </li></ul></ul><ul><ul><li>Help streamline business processes </li></ul></ul>
  22. 22. Data Warehouse Components <ul><li>Large physical database </li></ul><ul><li>Logical data warehouse </li></ul><ul><li>Data mart </li></ul><ul><li>Decision support systems ( DSS ) and executive information system ( EIS ) </li></ul>
  23. 23. 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
  24. 24. DW Suitability <ul><li>For organizations where </li></ul><ul><li>Data are in different systems </li></ul><ul><li>Information - based approach to management in use </li></ul><ul><li>Large, diverse customer base </li></ul><ul><li>Same data have different representations in different systems </li></ul><ul><li>Highly technical, messy data formats </li></ul>

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