Power Point presentation

Business Intelligence: making more informed decisions2 March 12:30 13:15

5 mins1

Shri Footring (Jisc)Janette Hillicks (Jisc)Gary Tindell (University of East London)Neil Barrett (Manchester Metropolitan University)Presenters

To introduce ourselves2

OrientationThe HESA and Jisc Business Intelligence (BI) initiative developing a shared service for UK education (overview / poster) Heidi PlusHeidi LabWho is involved and our agile approachEnvironmentExperiences via team GaryExperiences via team NeilNext steps / keeping in touchContent

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The HESA and Jisc BI initiative

10 mins or less, Jonathan

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A summarized overview of the various aspects of the Business Intelligence Project activity.6

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Heidi Plus The new business intelligence service for UK Higher EducationReplaces Heidi (which will be decommissioned in November 2016)Launched in November 2015 offering:Improved data content and functionalityDelivery of data sets through commercial data explorer toolNew visualisations and dashboards New training programme and support materialsAvailable to HE institutions with a full Higher Education Statistics Agency (HESA) subscriptionOver 65% of current Heidi subscribers have started the Heidi Plus application process

The new and improved Feedback has been very good across the sector.

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What is Heidi Lab?

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290 planners / interested staff130 universitiesCycle 1 and 2 teams50 planners44 universitiesAs an: Outreach officerWhen: Planning widening participation recruitmentI want to: Better understand potential student demographicsSo I can: Achieve my targets in the most efficient way

Our BI Experts group(comprises290 strategic planners from over 130 Universities) provide the community design input. Theyput forward analyst and head of planning staffto join short lived agile analysis teams addressing the needs of a wider range of staff roles than currently use BI through;Define user stories comprising As an (staff role) When I am (context) I want to (BI derived insight) so I can (action taken). Eg.'as an' outreach officer, 'when I am' planning my widening participation recruitment, 'I want to' better understand national student demographics, 'so that I can' achieve my targets inthe most efficient way.Map in the data sourceswhereinsights may layUndertake agile R&D data prep, load andanalysis teams (currently 50 planners from 44 institutions forming 9 teams)Provide dashboards as new service candidates for acceptance considerationSuccessful outputs migrate to Heidi-Plus or Jisc Beta service

Cycles: Cycle One November January, Cycle Two February May, Cycle late 2016 depending on findingsBased on 0.2 FTE.

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Heidi Lab Agile approach

Lee then Myles 5 minutes11

Heidi Lab Scrum in a slide

Sprints last 4 weeks, we have 3 of them1 day F2F Planning, weekly scrum virtually, Sprint review, retrospective and plan the next 4 week sprintRefining and creating user storiesIdentifying and acquiring dataAnalysing to make minimum viable product (dashboards etc) to meet the Sprint GoalWriting supporting narrative for safe onward useRegularly communicating with your Sector Advisor (product owner) for feedbackAdjusting scope, defining, re-developing, and making frequent early releases until signed off.

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Take a thin vertical sliceDont try to waterfall each stepWant quick release of product and steer

Dev Team1Dev Team 2Dev TeamsDev TeamsDev TeamsDev Teams

AnalysisData ordersUser stories

Dashboards

Process overview

Take a thin vertical slice to prove the concept. You don't want to spend too much time on each step.

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Scrum team activity

A first attempt at large scalecross institutional collaboration to create new BI dashboards andanalyses based on wide data collections for a national service to all UKeducation and research.A national project engaging with 70 experts from 60 HEPs to identify new business questions, likely data and undertake analysis for new service content14

Heidi lab overview

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Team Gary

Team Gary and Team Neil emerged because the fact that they did not know what their end product would be, and that they adopted agile methods to explore possibilities openly, is a fundamental part of the process16

University of East London (UEL) in collaboration with the local government association for London (London Councils) and London Borough of Newham have been purchasing higher education (HE) data from HESA for the last five yearsOutput includes published report The higher education journey of young London residents circulated to every councillor in London and mini-reports for each local authorityThe latest version report is launched annually with an audience from local/regional government and HE and the findings are placed within much broader policy contexts in terms of evolving 16-19 education and projected London labour market demands Expanding this project to cover the rest of the UK would make an ideal project for Heidi LabBackground and rationale

Tangible outcomes of this research include:Providing the evidence for the business case for 18 million investment in a specialist STEM focused sixth form centre in NewhamReport findings used to brief: About the investment in and returns from HE in London; Regional reports presented to 14-19 education leads across the capital; Borough level reports presented to Children and Young People Strategy Boards for the majority of London boroughs; Findings incorporated into the annual Young People in London: an evidence base which is used extensively across London by education professionals; Report used by the London Enterprise Panel to inform thinking about European Social Investment Fund programmes; Findings used to raise London specific skills challenges and potential lobbying points within London and central government.Model of city-university collaboration exported to Malmo project now in its second yearThe HE report forms part of the evidence base submitted as part of the social mobility section of the HE Green Paper17

As a: Strategic Planning ManagerWhen: Reviewing current course provisionI want to: Enable course/curriculum management planning to match national and local demand. So I can: Grow or at least maintain student recruitment

Data Sources: HESA student, Destination of Leavers from Higher Education (DLHE), Award data, key Information Sets (KIS), Complete University Guide (CUG)School/College performance data (A level results and numbers, School Age Populations Forecasts, etc.)Labour market data from Nomis (Employment rates, earnings, standard occupation classification (SOC), standard industrial classification (SIC), etc.)

User story

User story looks relatively straightforward but the data and analysis to undertake this task is more complicated.To achieve a long-term sustainable change in programme portfolio and student recruitment, really need to integrate HE data within a broader context and incorporate a horizon scanning and policy analysis capability. This is particularly the case now with the 16 area reviews of FE provisions and changes to A level provision.

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Mapping HESA Data to National Datasets

The approach has been to set the HESA HE data at the heart of the user story but to look for clear overlaps with data provided at a national level, primarily by ONS and central government (DfES, BIS, etc).The key is to look at the geospatial levels, in this case, local authority and regional levels. In addition, classify the external data sources in terms of pre-entry (population demographics and school/college performance) to HE and graduation (local/regional labour market and economic infrastructure). 19

Labour market application based on improving the data visualisation of NOMIS (ONS Labour Market Statistics) A Level/BTEC provision and resultsProject outputs video Demonstrations

Labour market

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A Level and BTEC provision

Over-ambitious with our fulfilling our user story given the time-frame (three months with Christmas break!!)Underestimated the time taken to clean, normalise and integrate different data sets (HEI institution names, spatial geographies, etc.)Fortunate to have a Tableau expert in our team who passed on his technical knowledge to other team membersThe close proximity of team members meant that they could meet face to face on a frequent basis to develop the dashboardsSprint planning process worked well in prioritising tasks

Lessons learned

Got to work with a fantastic group of BI developers and analysts!!Team worked cohesively and developed and acquired new skillsDeveloped some interesting dashboards by converting data from spreadsheets into meaningful data visualisationsGot to follow through on some of the original recommendations that emerged from the Jisc funded BI project that culminated in 2012

Benefits

Team Neil

Holding page for Neils input available after 24/2/1625

Making a differenceEXPLANATORYEXPLORATORYAnswering specific questionsFraming issues helpfully

A great team playing to its strengthsMaking a differenceInterpretationAnalysisSummaryData and facts

Proof of concepts quickly built up from bottom shelf scrapingsHelped by having a Tableau expert on the teamPragmatically adapting the visualisation to the actual dataTableau data files compiled specially?

Sprinting with nimble footed agility

Face to faceSketch out the productScrum 1DataScrum 2PrototypeScrum 3Snag and polish

Who are your benchmark institutions?

User storyAs a:When:I want to:So I can:Planning managerRefreshing our institutions benchmarking groupPick a group of providers and see which HEIs are comparablePropose alterations to the group with evidence

Analysis approachData gathering of various measures across league tables, staff, student and finance areasSimilarity is defined by numerical proximity based on a basket of measuresAll measure have an equal contribution due to standardisation but the basket of measures is adaptable via user selectionThe main page delivers the defined benchmark group but alternative visualisations of the metrics are available on other dashboard pagesIn addition to the selectable basket of measures there are quick filters to limit the institutions available to benchmark by geographical region, medical schools and mission groupDashboard design in orange/blue contrast for accessibility

Areas for developmentIs the basket of measures correct? e.g. substitute League table measures for directly calculated HESA equivalentsAre there missing measures? e.g. something about subject mixCurrently tableau doesnt allow the selected number of institutions in the competitor group to be applied to the measure chart visualisation Is institution level sufficient or would there be value in a subject level equivalent? (issues with mapping of subjects: Joint Academic Coding of Subjects (JACS), league table subjects, Cost Centres etc.)Are there any additional quick filters? (balance against over-modelling)Allowing a user to select a group of institutions and then get a visual of similarity

Planning for research

Dashboard aimsTo engage the academic and professional leadership of the institutionTo easily show patterns and trends from data rich sourcesTo encourage debate on Research Policy

Research income distribution by cost centreNeatly demonstrates the degree of specialisation of the InstitutionIllustrates year on year changeLeicester specialises in Clinical MedicineIncome to Clinical Medicine declined 10% between 12-13 Lancaster by comparison gets income from Physics and IT

Research income splitStaying with Lancaster Shows who supports spending in PhysicsBy geographical distributionBy funder typeIllustrates year on year changeLancaster gets all its research income from the UK (with the exception of the EC)

Source of research income by cost centreReverting to Leicester Research Excellence Framework (REF) main panel broadly groups cost centres by research areaA Life SciencesB Physical SciencesC HumanitiesD ArtsHeat map adapts to show which funder makes the greatest contribution

Higher Education Institution (HEI) income distributionShows where an institution is located in the research landscapeThose surrounding a body are in some senses peersAlso shows Income per Academic Full-time equivalent (FTE)To some extent reflects the STEM specialisationCan refine by moving to previous dashboard on income distribution by cost centre (normalised)

HEI average income and expenditureShows the surplus/ deficit in easy to grasp visual mannerAllows the user to drill into the dataSelecting institutions individuallySelecting Income sourceNarrowing the years for the calculation

Next steps

5 minsJonathan then Myles 50

We held a closed face to face Winter team showcase/ Spring team start up February 24-26 2016 Consideration for service productionWebinarSpring teams conclude May/ June 2016Consideration for service productionWebinarEvaluation of approach and recommendations for Heidi Lab beta post-July 16

HeidiLab - next steps

Minute on what happened at last weeks event plus follow-on activity potentials51

Vision:Making best use of HESA and other data to solve widely felt business problems, enabling customers to respond more effectively and with greater confidence to the volatile socio economic political environment we all operate in:

Possible service scenarios:Data for BI service - legal, licensed, cleaned, linked for your onward useDashboard delivery - locate and consume innovative visualisations to solve widely felt problems using wider than HESA data sourcesData/ visualisation continuing professional development (CPD) - send your staff to work in a Heidi Lab scenario to upskillBespoke problem solving - Heidi Lab based experts group has capacity to respond to widely felt problem areas - agileBuild your own dashboards - use our tools, data and include your own data to develop dashboards for youBI/ data consultancy brokerage - Jisc provides access to appropriate expertise at negotiated HE prices

Future service scenarios

Q & A

All - 10 mins53

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Final slide to encourage people to join the list, we have 300 people in the Heidi Lab contacts now54