Public Sector Data Analytics - Nesta

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Public Sector Data AnalyticsA Nesta GuideEddie CopelandTom SymonsHilary SimpsonNevena Dragicevic1

This work is Nesta licensed under the Creative Commons Attribution-NonCommercialShareAlike 4.0 International Licence.To view a copy of the licence, visit

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8About this guideThis guide is for public sectors organisations who areinterested in using data analytics to make betterdecisions and improve public services.The methods and advice are primarily based on Nesta’sOffices of Data Analytics (ODA) programme. Duringpilots in London, the North East of England and Essex,we’ve explored how cities and regions can establishODAs to join up, analyse and act upon data sourcedfrom multiple public sector bodies to improve publicservices.For more information on our ODA programme, a-analytics3

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8Learning modulesThis guide contains the following modules:Module 1: How data analytics can help the public sectorModule 2: Barriers to using public sector dataModule 3: The ODA methodModule 4: The 8 phases of an ODA projectModule 5: Using data legally and ethicallyModule 6: Designing and running an ODA pilotModule 7: How to make it easier next time roundModule 8: Setting up an Office of Data Analytics4

Module 1:How data analytics canhelp the public sector5

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7-Module 8Data analytics: from insight to actionData analytics is the discovery, interpretation, andcommunication of meaningful patterns in data.It can be used by individual teams and organisations tobetter inform their own decisions and activities.It can also be used to help multiple teams ororganisations collaborate more effectively.At Nesta, we believe data analytics has the most valuewhen it leads to better actions. This guide thereforefocuses on achieving actionable insights from data.6

Old approach; new technologiesUsing data to deliver actionable insightsis nothing new. In 1854, John Snowfamously plotted the location of deathsin London’s Soho to show that a choleraoutbreak was caused by contaminationof a local water pump. The map hecreated led to the pump handle beingremoved, saving many lives.A key difference today is that computersenable us to analyse greater quantitiesof data in more sophisticated ways.7

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7-Module 8What can data analytics do?Public sector organisations are working towards manydifferent goals. So where does data analytics fit in?Data analytics can be particularly helpful for:* Identifying specific cases in a wider group Prioritising cases based on risk or need Creating early warning tools Making better, quicker decisions Optimising resource allocationLet’s look at a few examples *Categories derived from NOLAlytics:

Targeting HMO building inspections12 London boroughs and the GreaterLondon Authority analysed data on theknown risk factors associated withunlicensed Houses in MultipleOccupation (HMOs) to help buildinginspectors find other properties that werelikely to be unlicensed HMOs. Its aim wasto increase licence revenues andprotect vulnerable tenants.Read the full casestudyImage: AD Imgaes Pixabay CC0 Creative Commons9

Tackling modern slaveryEssex Police and Essex County Councilare exploring how data sharing andanalytics could help them develop abetter understanding of local businessinspections.The aim is to enable improvedcollaboration between the manydifferent public sector organisationsinvolved in assessing businesses’ safetyand compliance.Image: Brian A Jackson / Shutterstock.com10

Optimising ambulance standby locationsIn New Orleans, data on the nature,location and timing of past emergencieswas analysed in order to predict whereand when future emergencies couldhappen.This analysis helped identify the optimumplaces to park ambulances on standbyto reduce response times.Read the full casestudyImage: Emergency Medical Transport, Inc.11

Newcastle NEET analysisNewcastle City Council’s analysis of NEETindividuals helps the local authorityidentify children most at risk of not beingin employment, education or training.Read the full casestudy (page 26)Image: 5477687 Pixabay CC0 Creative Commons12

Understanding motoring accidentsThe Behavioural Insights Team analyseddata from East Sussex on KSIs – roadaccidents leading to catastrophic injuryor death. The analysis helped debunkwidely held assumptions about thecauses of accidents, helping the localauthority see where they could designinterventions with the most impact.Read the full casestudy (page 21)Image: SteelFish Pixabay CC0 Creative Commons13

In short, data analytics is useful because It enables many of thetried and tested ways ofworking betterBut it’s not always straightforward 14

Module 2:Barriers to using publicsector data15

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8Barriers to using public sector dataPublic sector organisations face a number of barriers tousing their data.The first is a basic issue around data quality. Commonquality issues include: Records are only recorded on paper Records are digitised, but in hard-to-analyse formatslike PDF Data is recorded inconsistently, such “Smith Street”and “Smith Str”. Records about the same person or thing lack acommon unique identifier Records are unknowingly duplicated16

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8Barriers to using public sector dataA second issue concerns discoverability. Public sectororganisations tend to hold thousands of records that haveaccumulated over time, and find them hard to search. As aresult, individuals may have little knowledge about what usefuldata is held by other teams.In other cases, the existence of the data is known, but isthought too hard to use as records are in the form of free-textfields, old emails, meeting minutes, etc. Where services or IT areoutsourced, a public sector body may even find that it cannotaccess the data relating to its own service, or must pay anadditional fee! (Our advice: you should explicitly prohibit thisbad practice in future contracts with external suppliers.)Nesta outlines some further potential solutions in our report:Can Government Stop Losing its Mind?17

The jigsaw problemAn additional challenge is that manydata analytics projects require sourcing,analysing and acting upon data sourcedfrom different teams and organisations.This is made hard by the jigsaw problem:every team has their own piece of thedata puzzle, but rarely can anyone putall the pieces together to see what thebig picture shows.18

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8Challenges caused by the jigsaw problemThe jigsaw problem hinders public sector organisationsfrom using data to enable some tried and tested waysof working more effectively. For example:1) Shared Services: it’s hard for organisations to seewhere they could share resources with their neighbours ifthey don’t have data on the scale and location of theproblems, demand and opportunities beyond theirboundaries.2) Target areas of greatest need: it’s hard to targetresources effectively if organisations don’t have accessto data that shows where the people and places ofgreatest need are located.19

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8Challenges caused by the jigsaw problem3) Intelligent coordination of teams: it’s hard for teams toefficiently coordinate their activities on complex areaslike adult social care (where up to 30 organisations mayneed to collaborate to support one individual or family)if they don’t have data on what each other is doing.4) Prediction and Prevention: it’s hard to intervene inproblems early if organisations cannot bring togetherand analyse the datasets that could collectively point tocases of highest future risk.20

Causes of the jigsaw problemThe jigsaw problem has several wellknown causes. These include:Technical: Different organisations andteams use different IT systems, some ofwhich don’t easily talk to each other ormake data accessible.Data: Records may be stored in differentformats and according to differentconventions, making the matching andanalysis of data about the same personor place hard.Legal: There are some things the lawdoes not allow. There are many morethings that can be done with data thatnever happen because most publicsector staff are not confident in whatdata protection laws permit and prohibit.Organisational: Every public sector bodywas set up to serve a certain communityin a certain way. It can take significantorganisational and cultural changes tostart systematically collaborating.21

Why data sharing is hard (and how to make it easier)Why it’s hardHow to make it easierTechnologyData Bespoke, siloed IT systems Legacy IT that makesdata hard to extract Outsourced IT providerscharging for data access Lack of common platformfor data sharing Data in hard-to-useformats like PDF Data inconsistentlyentered Use of different standards Lack of commonidentifiers Lack of open dataLegalOrganisation Risk averse leadership Staff unsure about dataprotection rules Most senior dataprofessional in org is DataProtection Officer Lack of template datasharing agreements Teams created to focuson their siloed remit Lack of dedicated timeand resources for datacollaboration Leaders lackunderstanding of role andmeans of using data TechnologyUse tech conforming tocommon standards forinteroperabilityInsist all IT has open APIsEnsure contracts give fullaccess to dataInvest in commonplatform for data sharingLegalTrain all staff in PrivacyImpact AssessmentsAppoint Chief DataOfficer tasked withresponsible data sharingMake use of templateInformation SharingProtocolsData Record all data inmachine-readable format Enforce consistent dataentry Use common standards Use unique IDs, e.g. UPRNs Release non-personaldata openly by defaultOrganisation Establish Offices of DataAnalytics Free up time of in-houseanalysts to work on datascience projects ratherthan KPI reporting Leaders insist on usingdata to inform decisions

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8The ODA methodTo help overcome some of these challenges, Nesta hasbeen experimenting with and refining a methodologyfor running public sector data analytics projects. We callthis the Office of Data Analytics (ODA) method.This work was originally inspired by the activities of NewYork City’s Mayor’s Office of Data Analytics (MODA),established during the tenure of Mayor MichaelBloomberg.We’ll now explore this method and show how it canhelp you determine whether a given challenge can betackled with data analytics.23

Module 3:The Office of DataAnalytics (ODA) method24

How to decide if a given challenge can be tackled with data analyticsPublic sector organisations face many different challenges. How can you tell which ofthem might be tackled with data analytics?We believe that successful data analytics projects consist of four core elements:SpecificProblemDefinedActionClear DataProductAccessibleDataLet’s explore each in turn.25

What’s your specific problem?KEY POINT: It’s vital to move from large, macrolevel problems to something narrow andactionable.Public sector organisations face many largechallenges, but some are too broadly defined toinvite any particular remedy. For example, theproblem statement: “Modern slavery is occurringin the city” is too vague.SpecificProblemWith further thought, this could be refined to anarrower problem statement, such as: “We don’tknow which regulated businesses are most likely tobe exploiting victims of modern slavery.”26

Five specific problem typesSpecificProblemWhen trying to identify a narrower, more actionable problem, it’s helpful toconsider the types of problem that data analytics is well suited to address.The analytics team in New Orleans' Office of Performance andAccountability have helpfully outlined these five specific problem types:5 Specific Problem Types*Targets are difficult to identify within a broader populationServices do not categorise high-priority cases earlyResources are overly focused on reactive servicesRepeated decisions made without access to all relevant informationAssets are scheduled or deployed without input of latest service data*Derived from NOLAlytics:

What’s your specific problem?Your specific problem statements should not be in the form of a question, but phrasedas follows:SpecificProblemOur problem is that [insert specificproblem statement].28

What action do you want to make possible?KEY QUESTION: What would you do differentlyif you had all the information you neededabout your specific problem?To be clear, the data analytics process is notthe intervention. It’s important to identifypractical actions and interventions that arewithin your control to change. For example,no single organisation can ‘solve’homelessness – but you might help address aspecific aspect of it in your area.DefinedActionDrill down to precisely who will act, andwhere and when they will do so.29

What action do you want to make possible?DefinedActionThere are five opportunity types associated with the five problem types weoutlined earlier.*Specific Problem TypeOpportunityTargets are difficult to identify within a broaderpopulationIdentifying specific cases in a wider groupServices do not categorise high-priority cases earlyPrioritising cases based on risk or needResources are overly focused on reactive servicesCreating early warning tools for proactive workingRepeated decisions made without access to allrelevant informationMaking better, quicker decisionsAssets are scheduled or deployed without input of latestservice dataOptimising resource allocation*Derived from NOLAlytics:

What action do you want to make possible?List all the actions or interventions that you would like to put in place to address yourspecific problem if you had better information:SpecificProblemOur problem is that [insert specificproblem statement].DefinedActionIn response to which we would like to [list the different actions you would like toimplement].31

What data product do you need?KEY QUESTION: What would a person need tosee on a screen in order to enable the actionsdefined in the previous step?It’s unlikely that whoever is doing the action(e.g. a frontline worker or service manager) willwant a spreadsheet or raw data. Instead theywill want the data conveyed in a moreintelligible way that provides a real insight –that’s what we mean by a ‘data product’.Clear DataProductA data product could be a map, a heatmap, aprioritised list, an alert, a dashboard, avisualisation, and so on.32

What data product do you need?Certain data products are suited to certain problem and opportunity types.ClearDataProductSpecific Problem TypeOpportunityExample Data ProductTargets are difficult to identifywithin a broader populationIdentifying specific cases in a wider groupA graph showing anomalies or outliersServices do not categorise highpriority cases earlyPrioritising cases based on risk or needA prioritised listResources are overly focused onreactive servicesCreating early warning tools for proactiveworkingAn alert to flag issues when a thresholdhas been reachedRepeated decisions made withoutaccess to all relevant informationMaking better, quicker decisionsA data visualisationAssets are scheduled or deployedwithout input of latest service dataOptimising resource allocationA map or heatmap showing where casesoccur33

Example data productsHere are four examples of data products usedby UK and US public sector organisations:A PRIORITISED LIST. In London, housing teams in manyboroughs rely on random inspections or tip-offs to locateunlicensed ‘Homes of Multiple Occupation’ (HMOs). TheLondon Office of Data Analytics pilot sought to changethis by developing prioritised inspection lists that wouldlead inspectors to properties most likely to be HMOs,based on the characteristics of known unlicensed HMOs.AN ALERT. To reduce the risk of excessive force by policeofficers, the city of Charlotte, North Carolina, combineddemographics, training, payroll, internal affairs and otherdata to develop an early warning system for when anofficer was likely to have a negative interaction with thepublic.A MAP. In partnership with the SumAllFoundation, New York City is fighting recordnumbers of homeless by analysing andvisualising the patterns of evictions that lead tofamily homelessness. The project is alsoimproving the targeting of outreach services bypredicting the neighbourhoods, buildings andspecific addresses where resources are mostneeded.A DASHBOARD. Louisville, Kentucky hasimproved ambulance turnaround times by usingdata to identify obstacles to speedier response,which have saved the city 1.4 million (USD).Dispatchers are now supported with regularreports from a Computer Aided Dispatchsystem, which spots hidden inefficiencies andmonitors real-time location of ambulances tospeed up response times.34

Example data productsSunderland City Council created Adult360, a project to bring togetherinformation about a person and their lifefrom across a number of source systemsincluding Social Care, CES, telecare,intermediate care, city hospitals and thepolice.It has helped deliver better and morecoordinated care, equipping over 350health and social care practitioners with amore complete view of all that individual’sinteractions - as shown in this mocked upversion.35

Example data productsThe Amsterdam fire brigade collated datafrom different sources (information onroads, rails, buildings, neighbourhoods,etc.) and matched them with historicalrecords of previous incidents in the area.The data was then visualised throughmaps that the Amsterdam fire brigade useto see where, when and how often firesoccur.36

Example data productsThe London Borough of Barking &Dagenham conducted analysis toidentify areas where individuals aremore likely to be at risk from gamblingrelated harm.The analysis provided context to thelocal Gambling Licensing Policyrevision and helped create a ‘localarea profile’ identifying two importantclusters, debunking the assumption ofvulnerable people and gamblingshops being dispersed across theborough.37

Example data productsKent Constabulary has used data onprevious offenses in their area to optimiseresource allocation and better coordinatepolice surveillance.Data includes five years of historicalrecords of crimes committed in the area.Officers receive daily updates on 180hotspots in the area and can use them toinform their decisions on what areas topatrol the most.38

Example data productsDurham Constabulary is using HART, adata tool to support consistency in thedecision-making of custody officers whenassessing the risk of future offending.This tool creates a risk score, from high tolow, drawing from data that relates to asuspect’s previous offending behaviourtogether with age, gender, residentialpostcode, and intelligence reports.39

What data product do you need?You can now see whether an insight from a particular data product could enable oneor more of the actions you previously outlined:SpecificProblemOur problem is that [insert specificproblem statement].ClearDataProductIf we could see / if we knew [insertwhat the data product shows]DefinedActionWe would [insert the action you wantto implement].

What data do you need?KEY QUESTION: What data do you need to createthe data product, does it exist, can you get it, andcan you use it?Data can come from many different sources, suchas: Open data (e.g. Public sector Businesses & Third Sector CitizensAccessibleDataYou can use a simple template like the one on thenext slide to brainstorm what datasets might beavailable from these different sources.41

Open DataCitizen DataPotentialDatasetsBusiness / Third Sector DataPublic Sector Data

Does the data you need exist?If the data you need to create your dataproduct does not exist, you may wish toconsider:1) Are there other datasets that mightcontribute a similar type of information, oract as a proxy measure?AccessibleData2) Could you start collecting this data so thatanalysis becomes more feasible in future?(This is still a useful outcome of the ODAprocess.)43

Does the data you need exist?In Module 5, we’ll explore how you can checkthat you can use and, if necessary, share thedata legally and ethically.For now, it’s enough to determine if the datayou need to create your data product is inprinciple available.AccessibleDataYou should now be left with a four-partstatement as follows:44

SpecificProblemOur problem is that [insert specificproblem statement].ClearDataProductIf we could see / if we knew [insertwhat the data product shows]AccessibledataDefinedActionusing these datasets [insert datasetsyou plan to use]we would [insert the action you wantto implement].

Module 4:The 8 phases of an ODAProject46

The eight phases of an ODA projectLet’s assume you’ve used the four-step ODA method to identify a challenge youthink could be tackled with data analytics. To turn this into a live project, there areeight core phases that should feature in your project plan:1. Discovery: assessing the project’s feasibility and refining its approach2. Securing the commitment of project partners: identifying who needs to beinvolved and their roles and responsibilities3. Information governance: putting in place agreements to share data4. Data acquisition: getting hold of the required data5. Data analysis and prototyping: analysing the data and building the first versionof the data product6. Testing and evaluating: trialing the data product in a real-world setting andmeasuring its results7. Refining: improving the data product based on feedback8. Scaling: putting the data product into permanent / wider use47

The ODA project lifecycleAn ODA project entails several stages that vary between thinking very broadlyabout possibilities before narrowing down to something more specific. The DesignCouncil uses the double diamond diagram shown below.48

Phase 1: DiscoveryA discovery phase is commonly used in digital development projects. It’s there toensure that the correct problem has been identified and to verify that theproposed solution is sound and viable.The UK’s Government Digital Service have their own guide to running a discoveryphase, and use the diagram below to show how it fits into a project lifecycle.49

Phase 1: Discovery - Going deeper on the four stepsDuring the discovery phase, you should thoroughly check and seek to improve yourthinking on each of the four steps.Some of this can be done through desk research, but it should also include interviewsand workshops with people whose work the data analytics project is intended tosupport, such as service managers and front line staff, and those who will be affected,such as specific end users and groups of citizens. Co-designing and testing potentialsolutions with these groups is a vital at every step of an ODA programme.You should aim to achieve a deep understanding of:1) The nature and complexities of the problem to be tackled2) The range of different interventions available to solve it3) What data product would serve the needs of those who would use it4) Whether the data you need is accessible, whether its quality is sufficient, andwhether it can legally and ethically be used50

Phase 1: Discovery - tools and techniquesThere are a number of toolsand techniques you canuse to dive deeper in yourchosen issue during thediscovery phase.For example, the Five Whystechnique, a FishboneDiagram and CausesDiagram (see image) canhelp you identify thecontributory factors of theproblem you are trying totackle.51

Phase 1: Discovery - tools and techniquesA stakeholder map can help youthink about the differentorganisations who come intocontact with the problem you aretrying to address.Those organisations couldpotentially be sources of expertadvice, additional datasets, oreven become partners in a dataanalytics project.52

Phase 1: Discovery - tools and techniquesCreating a user journey map can help you understand the touchpoints where thepublic sector comes into contact with a given issue. This can help you understandwhat data is collected and see where better interventions could potentially bedesigned. The diagram below is a hypothetical and simplified view of public sectortouchpoints with a victim of modern slavery.53

Module 5:Using data legally andethically54

Using and sharing data and shared legally and ethicallyDuring your discovery phase, it’s vital to check that the data you require can be used,and if necessary shared, legally and ethically.Most legislation governing the sharing of publicly held data relates to personal data.The UK Data Protection Act defines personal data as “data which relates to a livingindividual who can be identified from those data” or from those data combined withother information.The Act also defines sensitive personal data, consisting of personal information onrace, ethnicity, political affiliation, religious beliefs, membership in trade unions,physical or mental health, sexual life and criminal background. More exactingconditions must be met to share sensitive personal data.The ICO has a useful guide to determining what is personal data.55

Discovery phase: using and sharing data and shared legallyIn May 2018, the European Union introduced theGeneral Data Protection Regulation (GDPR),which places greater responsibilities on allorganisations who collect and use personal data.The UK’s Information Commissioner’s Officeprovides a useful Guide to the General DataProtection Regulation (GDPR), including a DataProtection Self Assessment Toolkit.AccessibleData56

Discovery phase: using and sharing data and shared legallyOnce you’ve identified some datasets that you’dlike to use, it’s best practice to carry out aPrivacy Impact Assessment (PIA). A PIA is astandard series of screening questions thatguides users through the potential risks andbenefits of sharing personal data.The PIA equally prompts users to developmitigation strategies to minimise potentialdownsides of information sharing.AccessibleDataThis editable PIA is provided by the InformationCommissioner's Office (ICO).57

Discovery phase: using and sharing data and shared legallyIf you must use personal data, an important stepis to identify the legal gateways that grant yourorganisation the permission or authority to pursuecertain objectives, which could be supported bythe sharing of personal data.For example, during a pilot for the London Officeof Data Analytics that sought to identifyunlicensed HMOs, two pieces of legislation - theHousing Act 2004 and the Crime and DisorderAct 1998 - were identified as placingresponsibility on local authorities to improvehousing standards and to prevent crime anddisorder.AccessibleData58

Discovery phase: using and sharing data and shared legallyKEY POINT: Sharing non-personal data comeswith far fewer conditions.Wherever possible, it’s best to use nonpersonally identifiable data. If the source data ispersonal, it may be possible to remove names(and other personally-identifiable attributes) andaggregate the data to large enough samplepopulations that it’s no longer personal.AccessibleDataGood guidance on data anonymisation andpseudonymisation is available in the ResearchEthics Guidebook.59

This diagram shows howthe data product mayneed to adapt based onwhether particulardatasets can be used.SpecificProblemIs there a legalgateway to sharethe data, or canthe data besufficientlyYesanonymised?ClearDataProductNoCan it be usedor shared?NoDefinedActionYesIs the datapersonal?What data do you need tocreate the data product?What data isavailable?Think of: Open data Public sector Businesses &Third Sector Citizens

Discovery phase: using data ethicallyRegardless of whether it’s legal to usecertain datasets, you must ensure yourproposed data analytics project is ethical,too. Ethical considerations apply not just towhat data is used, and how it’s analysed,but also the actions that the data enables.There are a number of excellent toolkits tohelp you think about these questions,including the Open Data Institute’s DataEthics Canvas and the Cabinet Office’sData Science Ethical Framework.61

Discovery phase: using data ethicallyMeanwhile, Nesta is developing a self assessment toolkit specifically for cases wheredata analytics is used to enable algorithmic decision making.View the draft toolkit.62

Module 6:Designing and running anODA pilot63

- Introduction- Module 1- Module 2- Module 3- Module 4- Module 5- Module 6- Module 7- Module 8Phases 2-8 of an ODA pilotHaving completed the discovery phase, let’s briefly remindourselves of the eight phases of a typical ODA project:1. Discovery: assessing the project’s feasibility and refining itsapproach2. Securing the commitment of project partners: identifying whoneeds to be involved and their roles and responsibilities3. Information governance: putting in place agreements to sharedata4. Data acquisition: getting hold of the required data5. Data analysis and prototyping: analysing the data and building thefirst version of the data product6. Testing and evaluating: trialing the data product in a real-worldsetting and measurin

- Introduction - Module 1 - Module 2 - Module 3 - Module 4 - Module 5 - Module 6 - Module 7-Module 8 Data analytics: from insight to action Data analytics is the discovery, interpretation, and

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