Dear Data to Insight colleagues –
This is the first Data to Insight newsletter addressed to www.datatoinsight.org site members. If you’re receiving it by email, that means you’ll continue to receive news emails and updates from me into the future. I’m now retiring the old mailing list, so if you have any colleagues who haven’t yet signed up at www.datatoinsight.org and want to continue receiving emails from me, please advise them to take appropriate action! I’ll be sending this one message to both lists so all recipients on the old mailing list should be receiving the same advance warning.
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 by all means contact me and let me know what you’re thinking about. In the meantime, this is the news:
ChAT and BMt updates
Python data cleaning / Quality LAC Data
1. Mailing lists
We’re still working out the best way to keep people informed but not deluged with emails. We set up a “notification preferences” page on the new website, but this is poorly populated thus far, and feels like an extra burden for people to maintain, which is the opposite of the intention. For the time being, we’ll keep sending these summary newsletters, and supplement them with immediate brief notes when a new data tool update is available for download.
If you’d like to keep tabs more frequently on what’s going on with our data tools and projects, consider joining our Slack workspace. You can access by downloading the Slack app or go directly through your web browser if you can’t install the app locally. There are separate channels for each of our key work areas, and plenty of them are quiet, but we’re getting some activity starting to happen in the apprentices channel, and the demand modelling channel. I’m also thinking out loud in some of the channels for data tools I maintain, and happy to use that as a way to draw in user feedback on my plans.
If any of that sounds interesting, join the Slack, and then pick the individual channels that you want to follow. You can also set your user preferences to hide notifications if you just want to log in every now and again to see what’s going on.
3. ChAT and BMt updates
The day after I shared the latest Benchmarking Tool, the DfE published new statistical neighbour lists (actually, they published a calculator tool, but I’ve converted this into static lists for the ChAT and BMt). As such I’m afraid I now have yet another release of the BMt, plus a ChAT which has been updated to match.
It’s worth also checking that any local tools you have are updated to match your new statistical neighbour lists.
(13.4) Updated statistical neighbours in line with DfE publication of 01/04/2021
(13.3) Added CIN/CLA Outcomes 2019-20 LA-level data
(13.3) Added 2019-20 Workforce data
(13.3) Added Adoption Scorecards 2016-19 LA-level data
(13.3) Added "Latest local" input on the "Charts" tab – you can now more easily include (and name) a local data point in the trend charts
(13.3) Added "Charts (2)" tab containing single-year ranking chart:
(6.5) Updated statistical neighbours in line with DfE publication of 01/04/2021
(6.4) Fixed RIA dataset calculation for NFA assessments - was previously counting 6 months, not 3
(6.4) Fixed date formatting in child level lists - should all now be set to force UK format dates
(6.4) Reduced dataset on ChAT trend charts to show last 5 years (previously 6)
(6.4) Reduced dataset on ChAT MONTHLY trend charts to show last 4 years (previously 6)
(6.4) Charts in ChAT_MONTHLY, and monthly items in Benchmarking tab, now refer to "Report date" on HOME tab (cell E14) to identify previous 6 month period (was previously "today"); to report on full months, run your Annex A to the end of the previous full month, and set the report date to either the last day of that month or the first day of the following month; report dates in first half of month will ignore current month whereas report dates in second half of month will include it
4. Demand modelling
Last month we shared the Demand Modelling Tool 2021, trying to help prepare for an imminent end to 2021’s lockdown. You can download the tool HERE; it may help with preparing for the new few weeks of possible front door activity.
We’re now gearing up for a longer project through this year to build on what we learned to date, and draw in more voices from LAs interested in demand modelling. The goal is to produce an operational tool similar to the ChAT which will continue to be useful into the future, not just around COVID issues, but the specific approach is currently open for discussion. I’ll be writing today to people who’ve already expressed interest in this work to do some initial user research, and within the next month or two looking to hold a workshop or similar to kick ideas around and try and arrive at something achievable and useful to LAs which we can deliver this year. If you’ve not previously expressed interest, and want to be involved in any of that, let me know.
Our first cohort of apprentices have started and we’re busily ironing out the kinks in the programme – so far, mostly local issues with installing relevant software! Given how much interest there was in the first cohort, I’m looking at setting up a second cohort to start around September time this year. Given how long it can take to get over the various procurement hurdles, we’ll be looking to get started enrolling people sooner rather than later. If you’ve not already, please visit the apprenticeships mini-site and register your interest here for the second cohort.
6. Quality LAC Data
This is the “data cleaning in Python” project which I’ve been talking about for a while. The project is being funded by MHCLG, hosted by Wigan Council, with support from Data to Insight and Social Finance (they’re supplying the expertise for us to learn from). We’re currently agreeing timescales but hoping to do some prep work in the coming months, and line up LA colleagues to get to work learning some code and writing some validation rules after the statutory return windows close for this year. The goal will then be to get something useful into the hands of LAs in the autumn so that it’s useful in preparing for next year’s 903 return.
If you’ve not already expressed interest, but would be interested in working on a collaborative project to build a data cleaning tool which we can maintain as a community, initially checking the validation rules as per the SSDA903 specification, then let me know.
Credit for the Benchmarking Tool’s fancy new visualisation is due to colleagues in the following organisation:
Data to Insight
East Sussex County Council
London Borough of Newham
Wandsworth Borough Council
Credit for the Demand Modelling Tool 2021 is due to colleagues in the following organisations:
Brighton & Hove Council
Data to Insight
East Sussex County Council
Greater Manchester Combined Authority
Isle of Wight Council
Kent County Council
Ministry of Housing, Communities, and Local Government
Slough Borough Council
Wandsworth Borough Council
York City Council
Credit for the apprenticeships course tailoring is due to:
Too many LAs to count, but especially Walsall Council who are working at creating some “dummy” datasets for learners to use
Special thanks also to the following LAs who have chipped in with advice, bug fixes, testing support, and other such help over the last few weeks:
Bournemouth, Christchurch and Poole
East Riding of Yorkshire Council
Newcastle City Council
Manchester City Council
That’s it! If you have any comments, queries or ideas that you want to share, just let me know.