As the public sector faces increasing pressure to deliver efficient services, leveraging data from the frontline has become central to achieving meaningful transformation. Benjamin Taylor, Managing Partner at RedQuadrant, applies his systems thinking approach to help public sector organisations address the issues that affect us all.

In today’s conversation, Benjamin sat down with Joel Lister-Barker, Host of Talking Data, to discuss his professional journey so far, useful tools and techniques to capture data, and how Artificial Intelligence may impact business transformation consulting in the next five years.

Benjamin’s journey

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Joel: To kick things off, can you share a bit about your professional journey and what inspired you to focus on business transformation consulting?

I often talk about two foundational experiences early in my career. The first was working as a coordinator for a youth development charity that trained and funded young people to go on overseas expeditions. By focusing on building and maintaining a "ready stock" of participants rather than overloading the current intake, we were able to consistently meet our targets. The second experience was at a public advice centre, where we tackled long wait times and inefficient processes. We tried an experiment to keep the centre open for longer hours and send customers to the right person upfront. It resulted in a higher rate of issues being resolved, which reduced repeat visits and improved service quality. Both of these experiences ignited my passion for addressing systemic inefficiencies, leading me to roles at Hammersmith & Fulham Council, PwC, and eventually founding RedQuadrant, which is consultancy focused on public sector transformation.

Joel: How has your approach to working with data evolved throughout your career, particularly when working with clients in the public sector?

I’ve become less naive and more sceptical. For example, many organisations do not know how many people they employ nor how much they spend. This is because many internal systems and data sources can be unclear, which makes it difficult to get an understanding of baseline costs, build accurate target operating models, and perform options analysis. Therefore, my approach has shifted to focus on gathering data from the frontline, such as tracking interactions with customer service personnel, as these sources often provide richer insights than pre-existing datasets from internal systems. In the public sector, any gaps in analysis or misclassification of categories can have real-world implications. Meanwhile, the private sector often has the flexibility to define its own data categories and adapt its operations accordingly.

Joel: What are some of the data sources, tools, and techniques that you use when planning for a business transformation?
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We focus on demand analysis. This involves engaging frontline staff and customers to understand what people are genuinely asking for when they interact with the organisation. It requires capturing customer needs in their own words rather than through the organisation's existing service frameworks, as these can distort the real picture. We often use simple data collection techniques, such as tracking the types and frequency of customer inquiries to identify gaps in the current system. Tools like automated voice analysis can also help identify recurring issues, but the most valuable insights often come from direct engagement, such as listening to calls and observing interactions.

Joel: Can you describe a time when a surprising insight or trend was identified using data. How did it change the approach or outcome of the project?
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One interesting example is when I conducted a simple, yet revealing exercise for a Council - I called all 12 Council hotlines and transcribed each interaction. This mystery shopping exercise uncovered significant shortcomings in a hotline system termed "Smarter Borough," which was supposed to address typical community concerns. Instead, the service was confusing, bureaucratic, and ineffective. My feedback to Council leadership resulted in a lot of disruption, as they had invested heavily in this new system. A broader demand analysis across the whole Council revealed systemic inefficiencies. For instance, the neighbourhood office was being inundated with requests that they weren’t equipped to handle, effectively acting as intermediaries that slowed response times. These findings reinforced the value of grounding transformation efforts in real, frontline data to identify inefficiencies, improve services, and create tangible benefits for both the organisation and the people they serve.

Joel: With the rise of Artificial Intelligence (‘AI’), how has this impacted the way that you approach business transformation projects? What do you think the next five years looks like?
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At present, AI has had a limited impact on business transformation work in the public sector due to challenges with data security, quality, and reliability. While we use AI to review bids and adjust data in reports, we haven't yet applied it to large-scale data analysis, especially given the unreliability of large language models. However, I do see a lot of potential for AI over the next five years. If it can understand unstructured data with a high degree of accuracy, then that could be really powerful in the context of business transformation. For example, if AI could analyse signals from customer inquiries, then it could then direct customers to the right resources more efficiently.

Joel: Is there anything else that you would like to share about using data effectively in the world of business transformation consulting?
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Yes, there are seven deadly sins to be aware of when working with data, as follows:

1. Turning a measure into a target renders it ineffective, as people will focus on gaming the system rather than achieving the desired outcome (Goodhart's Law).
2. Proxy measures, like tracking inbound inquiries instead of actual sales, often become the de facto purpose, leading to misaligned goals (Deming's Law).
3. Many measurements are not repeatable or reproducible.
4. Averages or percentages mean nothing without understanding the impact of the range.
5. Binary comparison, such as year-to-year performance, is meaningless without context.
6. Failure to understand statistics leads to meddling rather than understanding the real signals.
7. Measures change their meaning from one part of an organisation to another (Transduction).

To avoid these pitfalls, I advocate for measures focused on real customer value that connect all parts of the organisation.

Joel: If you had a magic wand that could do anything with data, what is the one thing that you would make it do?
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I would change the concept of budgeting. I’m a big fan of Beyond Budgeting, which challenges the traditional model that tries to merge setting targets, forecasting, and resource allocation into a single, rigid number. This approach is inefficient, outdated, and often results in unethical behaviour, like pushing budget numbers around at the end of a quarter. Instead, I would separate these activities and create a more flexible, responsive approach to budgeting. Additionally, I believe in the importance of end-to-end cost-to-serve analysis for customer interactions. Current budgeting often fails to capture the true cost or profit associated with customer interactions because we only measure discrete steps in the process, not the whole picture. By integrating all these elements, businesses can make more informed, value-driven decisions.