SlideShare a Scribd company logo
1 of 12
UEL Approach to
Information Strategy
Development & Implementation
Information Strategy workshop @UEL in
association with JISC RSC London
31st July 2014
Andy Cook – Chief Information Officer
Gurdish Sandhu – Associate Director Information Strategy
Context
Understanding the external and internal Information Environment
External
• Rapidly evolving HE sector
• Changing data collection requirements of HESA, HEFCE etc.
• Performance measurement & Bench marking culture
Internal
• Increasing demand for access to quality information
• Utilising Information as a strategic asset to drive competitive
differentiation
UEL Information Strategy – SWOT analysis
STRENGTHS WEAKNESSES
OPPORTUNITIES THREATS
• Expanding suite of Business Intelligence reports
• Very competent and loyal staff in certain areas (e.g.
IT systems & data)
• Good practice of evidence-based approach to some
aspects of planning and corporate strategy
• Pockets of excellence in information management
• External visibility & reputation in information
management
• Positive relationship with external stakeholders e.g.
JISC, HESA, UCISA etc.
• Too few staff with effective information
management skills
• Single point of failure across many business
processes
• Patchy understanding of our business processes
• Fragmented information culture
• Lack of understanding to exploit strategic use of
data
• Only few schools /services have strategies
• Too much operational focus and short-termism
• Willingness to make strategic use of data for
competitive differentiation & advantage
• To develop & implement innovative Information
Strategy from scratch
• Horizon scanning for all
• Development of talent, information
management skills
• Digital information archiving & digital
preservation
• Embedding evidence-based planning across the
university
• Exploit innovative technology
• Streamlining of information process
• Loss of some key staff
• Poor succession planning & knowledge
management
• Uncertainties related to organisational
change
• Funding constraints
UEL IS Vision
‘The aim of the strategy is to move the university to a position
where it understands how to use data as a strategic asset.’
To move toward a culture of informed, evidence based decision
making, to use the potential of modern technologies to derive business
intelligence to improve the student experience, better inform course
design, develop the curriculum offer, understand and develop the most
effective pedagogies, better informed research and streamline business
processes. The Information strategy seek to enable a more agile
organisation that is responsive and adaptable to its environment.
High quality, accurate,
reliable information
Integrated across
systems
Facilitate data sharing
& re-use
Single version of the truth
Easily accessible
Fit for purpose
Secure, reliable, resilient
Unambiguous
Ownership
Comply with changing
requirements
Guiding principles
Continuous Information
Management Skills
development
Aims & Objectives
 To ensure that the university’s processes comply with legislative requirements
and national & international standards.
 To protect information security & appropriate use of data
 To improve data quality, streamline business processes, improve data security
and more effectively derive business intelligence
 To improve the availability and exploitation of management information to
support effective decision making processes
 To utilise external data in the delivery of business intelligence to the university
 To equip and support staff with data analysis & data interpretation skills
 To use information to advance university’s research agenda
Annual review process to ensure continued alignment with External
agencies and UEL business requirements and strategic directions
Our Approach to IS development
Living, evolving Information Strategy rather than static document
Adoption of JISC Framework
Taking best practice into consideration
Risk based approach to Information Strategy
Learning from Industry - Gartner
Jisc : The Sequel - Strategy Process
Framework
http://www.jisc.ac.uk/uploaded_documents/
practiti.pdf
1. Getting Started
II Information Needs
III Planning the Implementation
IV Roles and Responsibilities
V Monitoring and Review
Information Governance
Information
Strategy Board
Strategic Use
of Data
(Competency
Centre Report
Designer, BI
developers, Dbase
Administrator,
adding value,
Information asset
administrators’
coaching &
training)
IS implementation
& monitoring
Information
Architecture
Systems, tools
and data flow
IAO = Information Asset Owner
IAM – Information Asset Manager
DS = Data Steward
IAO
IAM
DS
VCG Services Schools Research
IAO
IAM
DS
IAO
IAM
DS
IAO
IAM
DS
InformationGovernanceInformationDefinition
Information Quality Information Architecture Capability & Culture
Embedded
Information
Culture
Fragmented
Information
Culture
2014 2015 2016
Delivering Information Strategy (IS) @UEL
Information
Policy
Established
IS Board
Staffing
structure
The case
for IS
Developed
IS
Develop &
implement data
validation & data
cleansing plan
Defined
Information
Quality
standards
Monitoring
& review
Design &
develop
report
repository
Established
robust
Technical
Infrastructure
Annual
review
Information
Skills &
Training
Information Strategy 2014-2016
Thank You
A.Cook@uel.ac.uk
G.Sandhu@uel.ac.uk

More Related Content

Similar to Uel information strategy development implementation v2-ac

Knowledge Management Practices in Large Companies
Knowledge Management Practices in Large CompaniesKnowledge Management Practices in Large Companies
Knowledge Management Practices in Large CompaniesNovi Research Center
 
OneIS CANHEIT V03 NN
OneIS CANHEIT V03 NNOneIS CANHEIT V03 NN
OneIS CANHEIT V03 NNMark Roman
 
KPI presentation - Northumbria conference 2013
KPI presentation - Northumbria conference 2013KPI presentation - Northumbria conference 2013
KPI presentation - Northumbria conference 2013jamiesoh
 
KPI: Keeping Purposeful Intelligence. CSE Event Cardiff Nov 2013.
KPI: Keeping Purposeful Intelligence.  CSE Event Cardiff Nov 2013.KPI: Keeping Purposeful Intelligence.  CSE Event Cardiff Nov 2013.
KPI: Keeping Purposeful Intelligence. CSE Event Cardiff Nov 2013.jamiesoh
 
Augury Introduction V2 1
Augury Introduction V2 1Augury Introduction V2 1
Augury Introduction V2 1Paul LaRiviere
 
AIIA_DataAnalytics_Project_External_20160721
AIIA_DataAnalytics_Project_External_20160721AIIA_DataAnalytics_Project_External_20160721
AIIA_DataAnalytics_Project_External_20160721Graeme Wood
 
14.05.08 connecting the it dots
14.05.08 connecting the it dots14.05.08 connecting the it dots
14.05.08 connecting the it dotskevin_donovan
 
Jisc learning analytics service overview Aug 2018
Jisc learning analytics service overview Aug 2018Jisc learning analytics service overview Aug 2018
Jisc learning analytics service overview Aug 2018Paul Bailey
 
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalSoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalChris Ballard
 
Research in to Practice: Building and implementing learning analytics at Tribal
Research in to Practice: Building and implementing learning analytics at TribalResearch in to Practice: Building and implementing learning analytics at Tribal
Research in to Practice: Building and implementing learning analytics at TribalLACE Project
 
Aegon hiek van der scheer
Aegon hiek van der scheerAegon hiek van der scheer
Aegon hiek van der scheerBigDataExpo
 
Cybersecurity strategy-brief-to-itc final-17_apr2015
Cybersecurity strategy-brief-to-itc final-17_apr2015Cybersecurity strategy-brief-to-itc final-17_apr2015
Cybersecurity strategy-brief-to-itc final-17_apr2015IT Strategy Group
 
Building agency capacity
Building agency capacityBuilding agency capacity
Building agency capacitySCSUTRIO
 
RDAP14: University-wide Research Data Management Policy
RDAP14: University-wide Research Data Management PolicyRDAP14: University-wide Research Data Management Policy
RDAP14: University-wide Research Data Management PolicyASIS&T
 
CURRICULUM VITEA-Teddy
CURRICULUM VITEA-TeddyCURRICULUM VITEA-Teddy
CURRICULUM VITEA-TeddyTeddy Angida
 
Knowledge Management Overview
Knowledge Management OverviewKnowledge Management Overview
Knowledge Management OverviewRahul Sudame
 
Information resources, mis, csvtu
Information resources, mis, csvtuInformation resources, mis, csvtu
Information resources, mis, csvtuNarender Chintada
 

Similar to Uel information strategy development implementation v2-ac (20)

Knowledge Management Practices in Large Companies
Knowledge Management Practices in Large CompaniesKnowledge Management Practices in Large Companies
Knowledge Management Practices in Large Companies
 
OneIS CANHEIT V03 NN
OneIS CANHEIT V03 NNOneIS CANHEIT V03 NN
OneIS CANHEIT V03 NN
 
KPI presentation - Northumbria conference 2013
KPI presentation - Northumbria conference 2013KPI presentation - Northumbria conference 2013
KPI presentation - Northumbria conference 2013
 
KPI: Keeping Purposeful Intelligence. CSE Event Cardiff Nov 2013.
KPI: Keeping Purposeful Intelligence.  CSE Event Cardiff Nov 2013.KPI: Keeping Purposeful Intelligence.  CSE Event Cardiff Nov 2013.
KPI: Keeping Purposeful Intelligence. CSE Event Cardiff Nov 2013.
 
David Reeve - UKAD 2016 forum
David Reeve - UKAD 2016 forumDavid Reeve - UKAD 2016 forum
David Reeve - UKAD 2016 forum
 
Augury Introduction V2 1
Augury Introduction V2 1Augury Introduction V2 1
Augury Introduction V2 1
 
AIIA_DataAnalytics_Project_External_20160721
AIIA_DataAnalytics_Project_External_20160721AIIA_DataAnalytics_Project_External_20160721
AIIA_DataAnalytics_Project_External_20160721
 
14.05.08 connecting the it dots
14.05.08 connecting the it dots14.05.08 connecting the it dots
14.05.08 connecting the it dots
 
Jisc learning analytics service overview Aug 2018
Jisc learning analytics service overview Aug 2018Jisc learning analytics service overview Aug 2018
Jisc learning analytics service overview Aug 2018
 
HSCIC: Developing Informatics Skills
HSCIC: Developing Informatics SkillsHSCIC: Developing Informatics Skills
HSCIC: Developing Informatics Skills
 
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalSoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at Tribal
 
Research in to Practice: Building and implementing learning analytics at Tribal
Research in to Practice: Building and implementing learning analytics at TribalResearch in to Practice: Building and implementing learning analytics at Tribal
Research in to Practice: Building and implementing learning analytics at Tribal
 
Aegon hiek van der scheer
Aegon hiek van der scheerAegon hiek van der scheer
Aegon hiek van der scheer
 
Cybersecurity strategy-brief-to-itc final-17_apr2015
Cybersecurity strategy-brief-to-itc final-17_apr2015Cybersecurity strategy-brief-to-itc final-17_apr2015
Cybersecurity strategy-brief-to-itc final-17_apr2015
 
Building agency capacity
Building agency capacityBuilding agency capacity
Building agency capacity
 
RDAP14: University-wide Research Data Management Policy
RDAP14: University-wide Research Data Management PolicyRDAP14: University-wide Research Data Management Policy
RDAP14: University-wide Research Data Management Policy
 
CURRICULUM VITEA-Teddy
CURRICULUM VITEA-TeddyCURRICULUM VITEA-Teddy
CURRICULUM VITEA-Teddy
 
Itsm knowledge roadmap ar updates
Itsm knowledge roadmap ar updatesItsm knowledge roadmap ar updates
Itsm knowledge roadmap ar updates
 
Knowledge Management Overview
Knowledge Management OverviewKnowledge Management Overview
Knowledge Management Overview
 
Information resources, mis, csvtu
Information resources, mis, csvtuInformation resources, mis, csvtu
Information resources, mis, csvtu
 

Recently uploaded

Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectBoston Institute of Analytics
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfblazblazml
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Cathrine Wilhelmsen
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Thomas Poetter
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfSubhamKumar3239
 
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Boston Institute of Analytics
 
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...KarteekMane1
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBoston Institute of Analytics
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...Amil Baba Dawood bangali
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 
Networking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxNetworking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxHimangsuNath
 
Digital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksDigital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksdeepakthakur548787
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
Principles and Practices of Data Visualization
Principles and Practices of Data VisualizationPrinciples and Practices of Data Visualization
Principles and Practices of Data VisualizationKianJazayeri1
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Seán Kennedy
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonTimothy Spann
 
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024Susanna-Assunta Sansone
 

Recently uploaded (20)

Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis Project
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
 
Insurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis ProjectInsurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis Project
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdf
 
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
 
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
wepik-insightful-infographics-a-data-visualization-overview-20240401133220kwr...
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 
Networking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxNetworking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptx
 
Digital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing worksDigital Marketing Plan, how digital marketing works
Digital Marketing Plan, how digital marketing works
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
Principles and Practices of Data Visualization
Principles and Practices of Data VisualizationPrinciples and Practices of Data Visualization
Principles and Practices of Data Visualization
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max Princeton
 
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
 

Uel information strategy development implementation v2-ac

  • 1. UEL Approach to Information Strategy Development & Implementation Information Strategy workshop @UEL in association with JISC RSC London 31st July 2014 Andy Cook – Chief Information Officer Gurdish Sandhu – Associate Director Information Strategy
  • 2. Context Understanding the external and internal Information Environment External • Rapidly evolving HE sector • Changing data collection requirements of HESA, HEFCE etc. • Performance measurement & Bench marking culture Internal • Increasing demand for access to quality information • Utilising Information as a strategic asset to drive competitive differentiation
  • 3. UEL Information Strategy – SWOT analysis STRENGTHS WEAKNESSES OPPORTUNITIES THREATS • Expanding suite of Business Intelligence reports • Very competent and loyal staff in certain areas (e.g. IT systems & data) • Good practice of evidence-based approach to some aspects of planning and corporate strategy • Pockets of excellence in information management • External visibility & reputation in information management • Positive relationship with external stakeholders e.g. JISC, HESA, UCISA etc. • Too few staff with effective information management skills • Single point of failure across many business processes • Patchy understanding of our business processes • Fragmented information culture • Lack of understanding to exploit strategic use of data • Only few schools /services have strategies • Too much operational focus and short-termism • Willingness to make strategic use of data for competitive differentiation & advantage • To develop & implement innovative Information Strategy from scratch • Horizon scanning for all • Development of talent, information management skills • Digital information archiving & digital preservation • Embedding evidence-based planning across the university • Exploit innovative technology • Streamlining of information process • Loss of some key staff • Poor succession planning & knowledge management • Uncertainties related to organisational change • Funding constraints
  • 4. UEL IS Vision ‘The aim of the strategy is to move the university to a position where it understands how to use data as a strategic asset.’ To move toward a culture of informed, evidence based decision making, to use the potential of modern technologies to derive business intelligence to improve the student experience, better inform course design, develop the curriculum offer, understand and develop the most effective pedagogies, better informed research and streamline business processes. The Information strategy seek to enable a more agile organisation that is responsive and adaptable to its environment.
  • 5. High quality, accurate, reliable information Integrated across systems Facilitate data sharing & re-use Single version of the truth Easily accessible Fit for purpose Secure, reliable, resilient Unambiguous Ownership Comply with changing requirements Guiding principles Continuous Information Management Skills development
  • 6. Aims & Objectives  To ensure that the university’s processes comply with legislative requirements and national & international standards.  To protect information security & appropriate use of data  To improve data quality, streamline business processes, improve data security and more effectively derive business intelligence  To improve the availability and exploitation of management information to support effective decision making processes  To utilise external data in the delivery of business intelligence to the university  To equip and support staff with data analysis & data interpretation skills  To use information to advance university’s research agenda
  • 7. Annual review process to ensure continued alignment with External agencies and UEL business requirements and strategic directions Our Approach to IS development Living, evolving Information Strategy rather than static document Adoption of JISC Framework Taking best practice into consideration Risk based approach to Information Strategy Learning from Industry - Gartner
  • 8. Jisc : The Sequel - Strategy Process Framework http://www.jisc.ac.uk/uploaded_documents/ practiti.pdf 1. Getting Started II Information Needs III Planning the Implementation IV Roles and Responsibilities V Monitoring and Review
  • 10. Information Strategy Board Strategic Use of Data (Competency Centre Report Designer, BI developers, Dbase Administrator, adding value, Information asset administrators’ coaching & training) IS implementation & monitoring Information Architecture Systems, tools and data flow IAO = Information Asset Owner IAM – Information Asset Manager DS = Data Steward IAO IAM DS VCG Services Schools Research IAO IAM DS IAO IAM DS IAO IAM DS
  • 11. InformationGovernanceInformationDefinition Information Quality Information Architecture Capability & Culture Embedded Information Culture Fragmented Information Culture 2014 2015 2016 Delivering Information Strategy (IS) @UEL Information Policy Established IS Board Staffing structure The case for IS Developed IS Develop & implement data validation & data cleansing plan Defined Information Quality standards Monitoring & review Design & develop report repository Established robust Technical Infrastructure Annual review Information Skills & Training Information Strategy 2014-2016