Data Stream - GOVERNMENT ICT 2 · Data Stream . Don’t forget to tweet @GovernmentICT ......
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Transcript of Data Stream - GOVERNMENT ICT 2 · Data Stream . Don’t forget to tweet @GovernmentICT ......
Data Stream
Don’t forget to tweet
@GovernmentICT#GOVICT
Keynote SessionMaking Better Use of Data as an Enabler for Public Services
Heather SavoryDeputy National Statistician and Director General for Data CapabilityOffice for National Statistics
Making Better use ofData as an Enabler for Public ServicesHeather Savory
[email protected]@SaturnSA4
Data for ONS, Government and Researchers
Societal insight Economic insight
Savings and efficiencies
Better Informed debate
Innovative economy
Better Informed research
Targeted Services
HS 6
Targeted Service Delivery
Better Informed Public Debate:
Migration
• Confused and mixed messaging from
different sources
• Collaboration between ONS and OGDs
• Clearly present and explain all
information
Service integration: DfE/CLG Land
Availability Tool
• Identify possible sites for new free
schools
Clustering
• Better policy
decisions based
demographics
and geography
Reduce reoffending rates
• Assess interventions
Better policy decision : Flow
of funds
• Closer monitoring of financial
flows
• Reduce risk of another
financial crisis
• Asset and liability position by
sector
• One sectors liabilities are
spread across economies
EFFICIENCY
HS 7
z
Data Infrastructure: delivering a critical resource
Data Catalogue
Metadata
Security / Access rights
Analysis
Open Data
Government Information Infrastructure
Registers/Core Reference Data
Local Government
Central Government
Census
Citizens
Business
Business-facing Services
Policy Design /Efficiency
Statistics/Economic Analysis
Academic Research
Innovation
Citizen-facing Services
HS 8
Statistical production:
Secure, in-house access
ONS staff
Statistical research:Working in partnerships, project-based access
Statistical services:Match, link, annonymise data:
internal and 3rd party use
3rd party disclosure: Accredited research and
statistics
The challenge: multiple user lenses across mixed data estate
INFO
RM
AN
ALY
SEP
REP
AR
EA
CQ
UIR
E
Data use and access
SRSA (inc ISOs)DEASTA (business)RSA (registration)VAT/finance actsST
ATU
TOR
Y
NO
N-S
TATU
TOR
Y
Voluntary SurveysNon-controlled admin data
Commercial partnershipsOpen data
METADATA
STATISTICAL METHODS
DEVELOPMENT
STATISTICAL RELEASESAD HOC OUTPUTS
INFORM POLICY ACCREDITED
RESEARCH OUTPUT
DEVOLVED STATISTICS
IDENTIFIED
SAFE
UNRESTRICTED ACCESS
CONTROLLED ACCESS
STATISTICAL
METHODS ADVICE
STATISTICAL
PRODUCTION
STATISTICAL
RESEARCH
3RD PARTY SERVICE 3RD PARTY
DISCLOSURE
National Statistician’s Data Ethics Advisory Committee (NSDEC)
The data subject’s identity (whether person or organisation) is protected, information is kept confidential and secure, and the issue of consent is considered appropriately.
Confidentiality, data security, consent
The use of data has clear benefits for users and serves the public good.
Public Good
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The risks and limits of new technologies are considered and there is sufficient human oversight so that methods employed are consistent with recognised standards of integrity and quality.
Methods and Quality
The access, use and sharing of data is transparent, and is communicated clearly and accessibly to the public.
Transparency
The views of the public are considered in light of the data used and the perceived benefits of the research.
Public views & engagement
Data used and methods employed are consistent with legal requirements such as the DPA, the Human Rights Act, the SRSA and the common law duty of confidence.
Legal Compliance
ETH
ICA
LP
RIN
CIP
LES
Data Science CampusActive Learning Experimentation
Apprenticeship in Data Analytics
2 year programme Level 4 Diploma-Data Analytics
MSc Data Analytics: Government
September 2017 Multiple academic partners Framework reviewed by GSS
MSc Data Analytics: Government
Open to Public sector staff Run with GDS & GO-Science Network “hub” @ DSC
Research Teams
Launched September 201624 FTE60 FTE projected March 2018
MoUs agreed including Universities (Cardiff), research institutes (Alan Turing),
international statistical institutes (Stats Netherlands)
Collaboration with national & devolved government, e.g. DEFRA, DCMS, DFID,
Welsh Government
Partnerships
Data Science Campus
Tourism and Migration- geo-located information
• Alternative data sources for QA of the IPS
• Domestic travel trends, small area statistics, crowd size estimation
• Address nationality based under-representation on other sources
• Google analytics web journey
• Draw insights: where do tourists go? Why?•Fishing industry supports economic activity in coastal
communities.
•Collaboration with DEFRA,-better understand importance of fishing industry, particularly in deprived coastal communities.
•Consolidate of data sets regarding measures (employment, Gross Value Added (GVA) and fleet landings) with IDBR data and
spatially visualise on various scales.
•Better understand how benefit of strong fishing activity propagates through the industry from production to services
Economic & Social Impact of fishing related industries
Visualisation of local and tourist photo spots(Geo-located Flickr data)
UK Sea Fisheries Statistics
z
ONS and National Addressing
MATCH
VALIDATE
BATCHVALIDATED
ADDRESS
ADDRESS
Local Authorities
Royal Mail
Ordnance Survey
Common secure services available to public sector
(and potentially wider subject to licensing)common service > consistency & efficiency
HS 14
UPRN
ONS Address Matching Service
HS 15
ONS
Citizen Service
FEEDBACK TO SOURCE
(IMPROVING QUALITY)
SINGLE
ADDRESS
UPRN
ADDRESS
UPRN
MATCH
BATCH
AddressBase
ONS DATA
MANAGEMENT
PLATFORMBUSINESS INDEX
ADDRESS INDEX
Matching for the public sector
GeoPlace
Business Index
Companies House
VAT
PAYE
Current Data
Future Data
Self assessment
Corporation Tax
Charities and Commissions
Business Index
Matching & Linking algorithm: Combines records into linked data (legal units)
Data Science
Business IndexLegal unit spine
Input Function Output
Companies House
VAT
PAYE
Company detailsCompany details
Putting Data at the Centre of the Next Wave of Digital Transformation
Charlie BoundyHead of Data ScienceDepartment for Work & Pensions (DWP)
Putting Data at the Centre of the Next Wave of Digital TransformationPutting Data at the Centre of the Next Wave of Digital Transformation
Charlie Boundy, Head of Data Science
DWP Digital
Support those who can work;
whilst protecting those that
cannot
Increase saving for, and security
in, later life
Run an effective welfare system
DWP has deep expertise in working with data
…now add Digital Services
•
And remember the challenges being faced by our customers
• Slow changing data, after the event
• Sampling, trials & evidence
• Policy rules in waterfall delivery
Digital ModelClassic Model
• Contact events, session behaviour, decisions & outcomes… feedback
• Data science, APIs, data visualisation… capability
• Embedded analytics within agile products
Digital Transformation and Data
Understanding the user and the decisions they need to make
Operations Manager
Strategist/ Policy Maker
Service Designer
Citizen facing staff
Citizen
As a ….I need to know ….So I can ….
A
O
E E E
E
DD
D
DB C
BB
BC
C
X
Y
Z
Authoritative Data trusted data that’s accessible, secure and
low latency
Augmenting contactdesign patterns to enable differentiated
services
Data Visualisation what’s happening, what’s normal,
what next?
Web Analytics Journey analysis
to improve service design
Data Platforms close working between digital, security
and architecture teams
Data Science & Analyticsdiscover, innovate and change
What data capabilities do we need to develop
Bring concepts to life with real data
Data isn’t what you expect it to be
The “product bit”The “science bit”
It takes more than just a data scientist
Valu
e
Capabilities
DISCOVERY(SANDBOX)
PILOT ALPHA(PROOF OF VALUE)
SCALING BETAMajor initiatives
EMBEDDINGAcross the organisation
OPTIMISINGAs BAU
Sustainable value from data is not anovernight sensation
Accountability & Explain-ability
Ethics
Privacy
Just because you can, doesn’t mean you should
