Working out loud

Working out loud

This week we held a workshop where we checked in on the mission that we’d set ourselves for 2021.

Back in January we agreed that our goal for 2021 was to measure the emerging economy, overhauling the way that businesses and industrial sectors are categorised. This isn’t just an exercise in admin, or data management. Our aim is to create a new measure of the emerging economy. We want this to support better policy decisions, investment in innovative world-changing businesses and jobs.

We wrote a blog about the problems with SIC that we, and everyone else involved in industrial analysis, were experiencing every day until we built Data Explorer.

In January we also agreed that it would be useful to adopt a transparent approach to achieve this goal. After all, we couldn’t possibly create adequate classifications on our own. We needed to work with experts. People like Optimat, KTN, BEIS, DCMS, Harlin and others.

We also realised that we needed to put our early work out into the world to attract more experts. Often this would involve publishing early datasets so that an expert could tell us how they were wrong and how to improve them. This was our way of improving our datasets, our platform, and algorithms that sit behind it.

Back to this week. Our workshop focused on whether we were telling these stories as powerfully as we could. Whether we were working out loud as much as we had hoped we would four months ago.

The answer was ‘ish’. We’ve been doing an ok-ish job where it comes to working out loud. B- ‘could do better’ kind of report. So, we refocussed ourselves on our customers and created a series of user stories. We also committed to a new habit of working out loud. Using our website to start conversations with people interested in what we’re doing, rather than it being a sales brochure.

User stories

We created a set of user stories to make sure we all knew who we’re writing for and wanting to start conversations with.

We then used a Lightning Decision Jam to talk about how best to build a community around the problem that we’re fixing – that of SIC Codes, particularly for people working in the public sector, central and regional government, catapults and publicly funded research organisations.

Working out loud. The actions from our workshop

We agreed a series of actions and new behaviours as a team…

  1. As a start-up, we want to get feedback from the people we’re working with, and supplying data for, quickly. This will help us improve what we do and achieve our mission of mapping the emerging economy at speed.
  2. The best way of doing this is to publish our progress, in blogs like this
  3. We’re also going to publish early versions of our Real Time Industrial Classifications – sector definitions, financial datasets and company lists. These can then be reviewed and co-developed with experts in each sector
  4. To be confident enough to release in Beta and to be honest about imperfections in new datasets
  5. Fix imperfections quickly
  6. Release updates, ideas and data regularly

The new Data City website is going live in a matter of weeks, supported by a new brand identity. We’re excited by that. We’re even more motivated about providing a platform for people who want to solve the problems with industrial data.

We’d love to hear your feedback on our social channels or on email at hello@thedatacity.com

About the author

Tom Salmon

Tom coordinates our marketing and story-telling. He came to us from managing director of the award-winning digital agency Epiphany from 2015–2020