Critical Success Factors
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1 Critical Success Factors Critical Success Factors Use open technology that facilitates tight Use open technology that facilitates tight integration between various systems. DW does integration between various systems. DW does not work without integrational synergies not work without integrational synergies Healthcare industry is burdened with loss of Healthcare industry is burdened with loss of operational efficient and cost pressures arising operational efficient and cost pressures arising out of the use of disparate environments out of the use of disparate environments Architectural considerations – dimensional Architectural considerations – dimensional model (STAR schema) – provides fast query model (STAR schema) – provides fast query response and is easily understood by users, response and is easily understood by users, and very easily expanded when warehouse grows and very easily expanded when warehouse grows Address administrative issues – what are we Address administrative issues – what are we using the DW for? When should data not be using the DW for? When should data not be added to the warehouse? How will the DW added to the warehouse? How will the DW interact and interface with other IS interact and interface with other IS initiatives? initiatives?
description
Use open technology that facilitates tight integration between various systems. DW does not work without integrational synergies
Transcript of Critical Success Factors
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Critical Success FactorsCritical Success Factors Use open technology that facilitates tight Use open technology that facilitates tight
integration between various systems. DW integration between various systems. DW does not work without integrational synergies does not work without integrational synergies Healthcare industry is burdened with loss of Healthcare industry is burdened with loss of
operational efficient and cost pressures arising out operational efficient and cost pressures arising out of the use of disparate environmentsof the use of disparate environments
Architectural considerations – dimensional Architectural considerations – dimensional model (STAR schema) – provides fast query model (STAR schema) – provides fast query response and is easily understood by users, response and is easily understood by users, and very easily expanded when warehouse and very easily expanded when warehouse growsgrows
Address administrative issues – what are we Address administrative issues – what are we using the DW for? When should data not be using the DW for? When should data not be added to the warehouse? How will the DW added to the warehouse? How will the DW interact and interface with other IS interact and interface with other IS initiatives?initiatives?
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Mechanisms: Mechanisms: Consistent Critical Consistent Critical Success FactorsSuccess Factors Planning ProcessesPlanning Processes Performance ManagementPerformance Management Partnership and Problem Partnership and Problem
SolvingSolving Geographic (or Geographic (or
Neighbourhood Policing)Neighbourhood Policing) Operational and Demand Operational and Demand
ManagementManagement Community ManagementCommunity Management
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Critical Success FactorsCritical Success Factors
Meta-data managementMeta-data managementBuild vs. Buy considerationsBuild vs. Buy considerationsDon’t forget HIPAA and privacy!Don’t forget HIPAA and privacy!
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Critical Success Factors for Critical Success Factors for M&AM&A
Speed Speed – Deliver tangible results as quickly as – Deliver tangible results as quickly as possiblepossible
PrioritiesPriorities – What needs to be done right away? – What needs to be done right away? PrecisionPrecision – What exactly will the benefits of – What exactly will the benefits of
the merger be?the merger be? CommunicateCommunicate – It is never too much – It is never too much ToolsTools – fast and powerful analysis – fast and powerful analysis VisionVision – clear long-term vision for the new – clear long-term vision for the new
entityentity CultureCulture – Avoid risk of losing key talent – Avoid risk of losing key talent ComplianceCompliance – internal control environment – internal control environment
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Critical Success Critical Success FactorsFactors
Strategy & Leadership
For a CoactivePolicing Style
InputsCommunity Leadership
&Accountabilit
y(Social & Political)
Structure
Service Delivery
Culture &Capacity
OutputsImproved Public
Outcomes
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Critical Success Critical Success FactorsFactors((Inputs)Inputs)
..
Strong LeadershipStrong Leadership
Setting out the Vision to Setting out the Vision to move to a Coactive Style of move to a Coactive Style of PolicingPolicing
Linking the Vision – to Linking the Vision – to Strategy – to Strategy – to ImplementationImplementation
Make it Reality not Make it Reality not RhetoricRhetoric
Driving an Evidence-led Driving an Evidence-led StrategyStrategy
Strategy & Leadership
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Critical Success Critical Success FactorsFactors
((Transforming #1)Transforming #1) ..
StructureStructure
• Co-terminosity of boundaries with Partners
• Shared and Distributed Activity
• Information Exchange Protocols
• Performance Management Processes
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Critical Success Critical Success FactorsFactors
(Transforming #2)(Transforming #2) ..
DeliveryDelivery
• 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
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Critical Success Critical Success FactorsFactors
(Outputs and Feedback Loop)(Outputs and Feedback Loop)
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Culture andCapacity
Culture andCapacity
• Adequate Resourcing – human, financial and technological
• Effective and ongoing Change Management
• Effective and Supportive Human Relationship Management (Note – not human resource)
• Continuous Improvement
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Knowledge DiscoveryKnowledge Discovery
Process of non trivial extraction ofimplicit, previously unknown andpotentially useful information fromlarge collections of data
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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.
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What Can Be DiscoveredWhat Can Be Discovered??
What can be discovered dependsupon the data mining task employed.
•Descriptive DM tasksDescribe general properties
•Predictive DM tasksInfer on available data
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What kind of information are What kind of information are we collectingwe collecting??
•Business transactions •Scientific data (biology, physics, etc.)
•Medical and personal data •Surveillance video and pictures
•Satellite sensing •Games
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(Con’t)
• Digital media• CAD and Software engineering• Virtual worlds• Text reports and memos• The World Wide Web
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Business IntelligenceBusiness Intelligence
Business Intelligence is the process of transforming Business Intelligence is the process of transforming data into information and through discovery data into information and through discovery transforming that information into knowledge” transforming that information into knowledge”
Business Intelligence is a discipline of developing Business Intelligence is a discipline of developing information that is conclusive, fact-based and information that is conclusive, fact-based and actionable. Business Intelligence gives companies actionable. Business Intelligence gives companies ability to discover and utilize information they already ability to discover and utilize information they already own, and turn it into the knowledge that directly own, and turn it into the knowledge that directly impacts corporate performance” impacts corporate performance”
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STEPSSTEPSGathering DataGathering DataOrganizing and Storing DataOrganizing and Storing DataAnalysisAnalysisDissemination of ResultsDissemination of ResultsDecision Making and ActionDecision Making and Action
Business Intelligence
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Business Intelligence ToolsBusiness Intelligence Tools
Software that enables business users to see and Software that enables business users to see and use (analyze) large amounts of complex data.use (analyze) large amounts of complex data.
Database RelatedDatabase RelatedData Storage SoftwareData Storage SoftwareQuery and Reporting ToolsQuery and Reporting Tools
Data Warehousing RelatedData Warehousing RelatedData Warehouse/ Data Mart Creation ToolsData Warehouse/ Data Mart Creation Tools
Data Mining Related Data Mining Related Data Mining ToolsData Mining Tools
Other Special Purpose ToolsOther Special Purpose Tools
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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)
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Data WarehousingData Warehousing
Physical Physical separationseparation of operational and decision support of operational and decision support environmentsenvironments
PurposePurpose: : to to establishestablish a a data repositorydata repository making operational making operational data accessibledata accessible
TransformsTransforms operational data to relational form operational data to relational form Only data needed for decision support come from the TPSOnly data needed for decision support come from the TPS Data are Data are transformedtransformed and and integratedintegrated into a consistent into a consistent
structurestructure Data warehousing Data warehousing ((informationinformation warehousing warehousing): ): solves the data solves the data
access problem access problem End users perform ad hoc query, reporting analysis and End users perform ad hoc query, reporting analysis and
visualizationvisualization
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Data Warehousing BenefitsData Warehousing Benefits
Increase in knowledge worker productivity Increase in knowledge worker productivity Supports all decision makers’ data Supports all decision makers’ data
requirementsrequirements Provide ready access to critical dataProvide ready access to critical data Insulates operation databases from ad hoc Insulates operation databases from ad hoc
processingprocessing Provides highProvides high--level summary information level summary information Provides drill down capabilitiesProvides drill down capabilities
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YieldsYields
• Improved business knowledgeImproved business knowledge
• Competitive advantageCompetitive advantage
• Enhances customer service and Enhances customer service and satisfactionsatisfaction
• Facilitates decision makingFacilitates decision making
• Help streamline business processesHelp streamline business processes
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Data Warehouse Data Warehouse ComponentsComponents
Large physical databaseLarge physical databaseLogical data warehouseLogical data warehouseData martData martDecision support systems Decision support systems ((DSSDSS) ) and and
executive information system executive information system ((EISEIS))
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Characteristics of Data Characteristics of Data WarehousingWarehousing
11 . .Data organized by detailed subject with Data organized by detailed subject with information relevant for decision supportinformation relevant for decision support
22 . .Integrated dataIntegrated data
33 . .TimeTime--variant datavariant data
44 . .NonNon--volatile datavolatile data
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DW SuitabilityDW Suitability
For organizations whereFor organizations where Data are in different systemsData are in different systems InformationInformation--based approach to management in based approach to management in
useuse Large, diverse customer baseLarge, diverse customer base Same data have different representations in Same data have different representations in
different systemsdifferent systems Highly technical, messy data formatsHighly technical, messy data formats
