bringing children’s - coram
TRANSCRIPT
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BRINGING CHILDREN’S DATA
BACK TO LIFE
Presented by
Matt Wagner – Data Analyst for Children’s Social Care, KCC
Dr James Geddes – Principal Data Scientist, The Alan Turing Institute
Sarah Hammond – Director, Integrated Children's Services, KCC
Penny Ademuyiwa – Assistant Director – Front Door, KCC
An interactive map of front door and assessment data
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Challenges/opportunities in Kent
Project set-up & problem definition
Creating the prototype
The prototype in action
Possible developments
Q&A
WHAT WE’LL BE COVERING
1
2
3
4
5
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STRENGTHS CHALLENGES Wealth of well-understood
data
“Performance management
systems provide detailed
data and helpful analysis to
monitor and develop
services effectively.” - Ofsted Report, March 2017 -
There are many factors
impacting children’s routes
and outcomes, which can
be hard to identify Variety of information
available to managers
and front-line workers How do we know that we
are asking the right
questions?
COULD LOOKING AT THE DATA DIFFERENTLY HELP US TO
UNDERSTAND HOW TO WORK WITH CHILDREN AND
FAMILIES MORE EFFECTIVELY?
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WORKSTREAM SETUP
CONCEPT GENERATION
Two primary applications of data visualisation in children’s social care:
Quickly testing hypotheses
Spotting unexpected trends or relationships
1
2
Application 2 was chosen with a focus on the entry into the social care system (including contact, referral and assessment).
AIM
CREATE A PROTOTYPE DATA VISUALISATION AS A PROOF-OF-CONCEPT TO DEMONSTRATE SOME OF THE
POTENTIAL BENEFITS WITHIN CHILDREN’S SOCIAL CARE.
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“BY VISUALIZING INFORMATION, WE TURN IT INTO A LANDSCAPE THAT YOU CAN EXPLORE WITH YOUR EYES. A SORT OF INFORMATION
MAP.
AND WHEN YOU’RE LOST IN INFORMATION,
AN INFORMATION MAP IS KIND OF USEFUL.”
- David McCandless -
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NFA/ IAG
EH Other
NFA/ cancel
Other NFA/ cancel
CIN CP NFA/ cancel
CIN Other
NFA/ cancel
EH CIN
Other
C&F Ax
Strategy Discussion
Section 47 ICPC
Initial Contact
Referral
Key Front Door Team District Teams Early Help Universal Support
OUR INFORMATION MAP
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CREATING THE VISUALISATION
EXTRACT DATA into 10 datasets
1
COMBINE & ANONYMISE 2
RESTRUCTURE & SYNTHETISE for developer
3
DEVELOP initial visualisation
4
ENHANCE visualisation
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PROTOTYPE VISUALISATION
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FUTURE POSSIBILITIES (1/2) DEVELOPMENT OF THIS
VISUALISATION • Different classification
options:
– Age;
– Gender;
– Asylum status;
– Referral source;
– # of missing episodes;
– etc.
• Expand the scope of the
map
• Display key statistics
– e.g. % of children taking
each route out of a node
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FUTURE POSSIBILITIES (2/2) DIFFERENT VISUALISATION
OPTIONS • Visualisation of aggregate information (e.g. average duration)
– E.g. Boston subway map example
• Comparison between teams within the LA
• Comparison to other local authorities
WIDER USE OF THESE TOOLS
• Standard data input tool for local authorities to use
– Similar to Annex A / CHAT tool
• Could there be a facility to compare your aggregate information to that of
other local authorities if you also agree to share yours?
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CLOSING REMARKS
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QUESTIONS & ANSWERS