The Data Lab, Scotland’s innovation centre for data and AI delivers an £8m skills portfolio offering a range of training and professional development programmes aimed at a range of levels from data citizen, data worker, data professional through to data leader.
This includes The Data Lab Academy which provides scholarships to students studying on data and AI masters programmes across Scotland, where along with their fees being paid offers an annual programme of additional employability and skills training and the opportunity to undertake a 3-month paid industry placement.
Since the Academy launched in 2015 over 1000 students have benefitted from the programme with approximately 500 participating in a placement with a range of public, private and third sector organisations including Natwest and TV Squared (part of Innovid).
Matching students to placement projects with industry partners is a resource intensive exercise, which in previous years had been carried out by either a recruitment partner or dedicated in-house team.
Every year the team at The Data Lab would ask – wouldn’t it be great if this process could be automated? But it seemed like such an enormous challenge to address. At the start of academic year 2021-22 just as they were about to kick off the programme for the year, Gigged.AI stepped in.
Gigged.AI provided the opportunity for The Data Lab to join their beta programme, providing access to a white label version of their newly launched talent platform to enable them to pilot automated matching for 155 Master’s students and also a smaller PhD placement programme of 15 students.
This also enabled the team at Gigged.AI to undertake further testing before launching the product to the market. During the 2021-22 term, 61 masters students and 15 PhD students applied and were matched to placement projects using the Gigged.AI platform, with 16% offered longer term employment with their host organisation.
1. Rapid Skill Matching
The implementation of the Internal Talent Marketplace proved to be a game-changer in skill matching. It facilitated a much quicker matching process compared to traditional methods. By bringing all matching in-house, The Data Lab was able to expedite the process of connecting students with placements, ensuring that the right skills met the right opportunities with greater efficiency.
2. Effective Resource Allocation
By automating and streamlining this process, The Data Lab was able to allocate resources more effectively, thus enhancing the overall efficiency of their workforce and talent utilisation.
3. Cost Savings
The implementation of this platform resulted in significant cost savings for The Data Lab. By streamlining the matching process and making it a recurring annual practice, the organisation achieved not only immediate cost reductions but also the promise of ongoing savings in both time and financial resources. This approach led to a substantial, cumulative cost saving strategy for The Data Lab.
“We were thrilled to be involved in the early testing of Gigged.AI’s talent matching platform. Access to the platform enabled us to test out automated AI matching for our placement programmes, which we had been considering for a while, but didn’t quite know where to start.
Instead of having to build a tool from scratch which would have taken some time, or search the market for a suitable product and commit to investing immediately, participating in the beta programme provided the opportunity to pilot an off-the-shelf product that could be customised for our needs.
Whilst there are a number of off-the-shelf Internal Marketplace solutions, many of these have restricted or no capability to customise. The placement programme had a number of bespoke requirements and participating in the beta programme enabled The Data Lab to work closely with the Gigged.AI team to tailor the product to meet their needs in extremely short timescales to meet critical deadlines.
Whilst there were a number of challenges along the way The Data Lab found the team at Gigged.AI to be very responsive and adaptable to change requests to support the requirements of the programme”.
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