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D2I news for December: 3 updated tools, 2 turtle doves, and apprenticeships in the new year...

Dear Data to Insight colleagues –

Please find below another bundle of news, within which I hope you’ll find something of interest. As always, if you want to talk about this stuff, or have ideas for how we can do useful things – or how you’d like to get involved – then reply to this email and let me know what you’re thinking about.

1. RIIA Quarterly Benchmarking Tool – national Q2 data now available

2. ChAT update – Annex A changes for January inspections

3. CLA Placement Demand Modelling Tool – minor fix

4. Apprenticeships – round 4 registrations extended

5. Quality Data project – new code-along learning opportunities

6. Upcoming events – next open house

7. Credits

1. RIIA Quarterly Benchmarking Tool – national Q2 data now available

RIIA Benchmarking Tool | Data to Insight


The newly standardised RIIA quarterly data collection is up and running for in-year national benchmarking, and this is the second of our quarterly data releases. it takes a little while (up to around 70 seconds) to recalculate when you first select your LA and region from the home page, but thereafter it should run smoothly.


Many thanks to everyone who provided feedback to support this release, for which I’ve pasted the change log below. New to this version is Q2 data for previously shared measures, Q1 data for workforce measures, and some fixes and improvements. The new chart showing quarterly variation is particularly nifty.


Note that use of this unvalidated in-year data is governed by a Memorandum of Understanding between LAs and regions across the country. Do not use this data, and especially do not share it further, until you have first understood the terms of that use within your LA. The short version is that you can use this data to inform local decision making but cannot publish it.


RIIA Benchmarking Tool 2023 Q2 v1.0:

• Loaded in Q2 data for all regions

• Quarterly trends table added

• Previous_vs_Latest charts added

• Charts tabs renamed and minor updates done

• % Denominator added for Measure 228

• Updated all hard-coded Q1 references to Q2

• Updated population data

• Tweaks to quarterly trends tab

• SE workforce measures adjusted to show they are 2023 Q1 not Q2

• Formatting quarterly trends tab


2. ChAT update – Annex A changes for January inspections


ChAT | Data to Insight


Ofsted have updated the Annex A specification with changes to list 9 (care leavers), which are now in effect, so we've shared an updated ChAT earlier than anticipated in order to accommodate these. We’ve got further changes planned to make more use of the revised specification in the ChAT report itself, but for now it’s a fairly modest new release – space for the new data on the list 9 tab, and some small changes and fixes suggested by colleagues from various LAs.


ChAT 7.03:

• Updated CLA chart series to show 21-22 data where they had not been updated

• Updated benchmarking dates (not data) for A15 as was showing incorrect dates

• Lookups added (various)

• List 9 amended to reflect new Annex A

• ICPC timeliness chart contains guidance note to indicate published comparator data from CiN stat release includes transfer-in conferences (calculated from referral date)

• Ofsted inspection data updated to 10/10/22 (with thanks to Sheffield City Council for compiling)

• Clarified dates used on population comparators (see note in November newsletter re: population data changes in CiN/903)

• Fixed ChAT monthly - series not showing 21-22



3. CLA Placement Demand Modelling Tool – minor fix


Demand Modelling Tool for CLA Placements | Data to Insight


A brief note for users of this tool – a colleague spotted that the “include UASC” toggle wasn’t changing anything in their copy of the tool, and we’ve been able to replicate this and fix it with their help. So if “Include UASC” doesn’t change anything for you, use the above link to download a fresh copy of the tool.


In the new year we’ll be sharing a web-based version of this tool which is much easier to navigate (and still doesn’t require any data sharing or software installation), and then continuing work through 2023 to make it more useful both as a single-LA and a regional forecasting tool. For the time being, if you’re looking at updating your local sufficiency statement or cohort predictions for next year, the Excel version linked above still works, and just needs the standard SSDA903 data outputs to run.



4. Apprenticeships – round 4 registrations extended


Apprenticeships | Data to Insight


We’re looking to kick off a new round of the D2I children’s services data apprenticeship in the new year, and we’ve extended registrations following feedback that December wasn’t a good time for LAs or individual colleagues to properly consider applying.


This is a tailored Level 4 Data Analyst apprenticeship, working with data and questions co-developed by the D2I community and delivered by a professional apprenticeship/training organisation called Corndel. We’ve done a lot of work since the scheme launched in 2020 to refine and adjust based on learner feedback with the intention that the course keeps getting better while continuing to deliver the same core learning goals and help LA colleagues develop industry-standard data analysis skills.


The course is funded in full by your organisation’s Apprenticeship Levy, meaning the only cost to your employer is the 20% of your working time which you’ll be spending learning and applying that learning to work-related challenges.


Applications are now open with a deadline of 23rd January for the initial application, enrolment until mid-February, and learning to commence mid-March. To stay in the loop, complete the “Join our mailing list” form on this page and we’ll make sure to email you when it’s time to apply. If you have questions in the meantime, let us know.



5. Quality Data project – new code-along learning opportunities


Analysts from all over the country, with the support of D2I, are just wrapping up the production of a Python tool than can be used year-round to validate that CIN census data meets the DfE submission guidelines. Most of the analysts on the project have gone from knowing no Python at all at the start of the project, to, by contributing just a few hours of their work week for the last few months:


• contributing to a large programming project,

• maintaining and bug fixing large projects,

• learning how professional collaborative software development is done on GitHub and following that model with the CIN validator,

• and creating and automating interesting and complex data visualisation and analysis beyond the scope of tools like Excel, locally.


D2I have fostered this learning by producing a range of materials, from videos through to worksheets, hosting twice weekly code-along lessons for those who had time to attend, running short stand-ups for analysts, facilitating analysts with previous Python experience to help those new to Python, and encouraging analysts to get involved in development and using Python as early as possible.


As we move into the new year, D2I is looking to help a new cohort of analysts who want to upskill, add some Python to their LA’s toolkit, and get examples of working on large Python projects on their CV, to join the current cohort and build a new tool collaboratively and later, maybe, be part of the cohort welcoming and helping new analysts to build other future tools. The tool we’re working on provides a pipeline to allow analysts who have coded analysis, visualisations, and dashboards in Python to share that work so that users from other LAs can safely use code on their local versions of the same data sets in browser with just a few clicks and no coding knowledge. Analysts who join us will mainly focus on building the code that does the analysis, visualisation, and dashboarding, to make the set of dashboards the tool launches with. One thing we hope to build as part of this project, for instance, is a browser-based version of ChAT-style visualisations, that only takes a couple of clicks and a few seconds to do what the Excel tool currently does. The goals of this project are to help interested analysts learn the Python needed to do complex visualisation, analysis, and dashboard making, and to help analysts who just want the output of those tools but have no desire to learn Python to have easy access to them.


If you’re interested in joining up, or just hearing about our plans, email us to get added to our new cohort mailing list.



6. Upcoming workshops and presentations – next open house


D2I open house – email for an invitation


We’ll be running another D2I “open house” team meeting on 18th January at 13:00 – this is a fairly informal meeting where we bring along a couple of interesting (to us) technical or strategic challenges we’ve been working on and share our working with the team. It’s open to all LA colleagues to come along and listen in, join the conversation, or suggest things they’d like to share from their own work. If you’d like to join us for this or future meetings, just email us.


If you have something interesting you’d like to share at the meeting, we’d really welcome that – just let us know how much time you’d want to talk through it.



7. Credits

Credit for recent improvements to our data tools is due to colleagues in:


Cheshire East

Hull

Slough

Newcastle

Kirklees

Hartlepool

Hounslow

East Sussex

Ofsted

Cheshire West and Chester

Stockton

Lancashire

Portsmouth

Hertfordshire

Wirral

Sheffield

Department for Education

DLUHC Local Digital Collaboration Unit

Social Finance

That’s it! If you have any comments, queries or ideas that you want to share, just let me know.

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Dear Data to Insight colleagues – Please find below another bundle of news, within which I hope you’ll find something of interest. As always, if you want to talk about this stuff, or have ideas for ho

Dear Data to Insight colleagues – Please find below another bundle of news, within which I hope you’ll find something of interest. As always, if you want to talk about this stuff, or have ideas for ho

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