Data, innovation and

transformation of public sector

services One Public Sector Scotland

19 June 2014

Atkins Lectures

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The importance of data Should it be open?

Things to consider when collecting data

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● What type of data do you need? – numbers? timetables? location based? is it qualitative or quantitative?

● Source – where can you get this data? is it from an open database? the public? data that already exists in your organisation?

● Granularity – how specific and to what degree of accuracy do you need the data to be?

● Use – what will you use the data for? what would members of the public use it for? how would this benefit the organisation and the public?

● Innovation potential – can the data help us do something new or improve the way we already do things? how can we make it commercially viable?

Should all data be open?

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The public sector is already

making more data available

to everyone, for example

data.gov.uk:

While it’s scary to give all

that data away, there’s one

big benefit - it makes the

public your innovators:

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What is innovation? Should we care?

What is innovation?

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"An innovation is something original, new, and important in whatever field that breaks in to a market or society". Based on Frankelius, P. (2009), Questioning two myths in innovation literature, Journal of High Technology Management Research, Vol. 20, No. 1, pp. 40–51.

It is not invention!

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It is different from improvement

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It is different from improvement

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Should we care about innovation?

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Technology Needs

Expectations Environment

Need to innovate

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Data innovation in action

Broadband infrastructure upgrade Campbeltown (>3,000 lines)

Capital funding investment to upgrade broadband infrastructure

Quickly extend the availability of superfast broadband services

Need to understand the geography and group settlements into types

Ordnance Survey data, Royal Mail data, background maps

Image courtesy of LLDC

Case study: wireless city capabilities

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Context

Towns/cities procuring WiFi coverage in town/city centres and/or public transport

Need

Local authorities currently collect footfall data but collection may be limited to key streets or lack granularity

Understanding how citizens and tourists use the town/city (streets visited, direction of travel dwell time, use of public transport routes, get on/off public transport)

Solution

Depending on suppliers offerings and procurement routes, Local Authorities may be able to negotiate access to anonymised footfall data for the area covered to review:

- City scape investment/regeneration

- Tourism attractions

- Optimise public transport, etc.

Marine Scotland National Marine Plan Interactive

Data visualisation and interrogation

Stakeholder engagement

Communicating decision-making through raw data distribution

Scottish Water Building age assessment for lead pipe replacement

Feature extraction from historic raster mapping using algorithms

Comparison of extracted data with current large-scale features

Linked to customer database

Desktop assessment of relative risk

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Thank you colin.henderson@atkinsglobal.com

Twitter brainstorming

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To innovate and transform public sector services what data will be required?

@RodneyMountain: Big

data offers enormous

opportunities for innovation

@christophmccann: The

people that analyse the data

have to be close to the data

collection

@christophmccann: We

have to make sure we have

a reasonable non-draconian

approach to the security of

data

@CBS_Shaun: Should all

data be open and free

@lwardrop: Good

discussion on balance

between complete v's

incomplete data

Twitter brainstorming

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To innovate and transform public sector services what data will be required?

@colgis: WW2 aerial photos

used to identify potential

unexploded bombs

@colgis: Poor data in will

lead to poor decision

making

@colgis: Is it right for

commercial organisations to

make money from public

data?

@colgis: Public bodies need

to fund data capture - a

freemium model could be a

good fit

@colgis: Sharing of data

between partner

organisations is often

difficult

@ colgis: Should data

innovators be afraid to fail?

Failure can be good for

future innovation

@colgis: Challenges of data

sharing - licensing,

compatibility, data protection