Data-Driven Work Cultures: Richard Pugh of Ascent On How To Effectively Leverage Data To Take Your Company To The Next Level

An Interview With Pierre Brunelle

It’s important for organisations to understand their impact on the environment and be able to reduce their carbon footprint. It’s even more crucial to be able to quantify the improvements they make and show their progress on the path to net zero objectively — numbers speak louder than words! Here, again, data and analytics provide the necessary tools to achieve this goal, and to enable organisations to differentiate themselves from those merely paying lip-service to improving sustainability.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Richard Pugh of Ascent.

Richard Pugh is Chief Data Scientist at Ascent with over 20 years’ experience in helping customers drive value from data. A founding member of the Data Science Section of the Royal Statistical Society and the R Consortium, Richard has led a wide range of ground-breaking data science projects for some of the most forward-thinking companies in the world.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to ‘get to know you’ a bit better. Can you tell us a bit about your backstory and how you got started?

I studied as a mathematician and statistician, so I was a practitioner in the industry working with data back in the late 90s. I always felt frustrated that the data role was perceived to be quite reactive and more of a back office function — essentially an audit team without a chance to add value. I would often automate the analysis I was doing — so I would put myself out of a job over and over again!

A number of years later I created a data science & analytics company called Mango Solutions. I ran this for about 19 years before it was acquired by Ascent in 2020. The acquisition helped me to work more closely with leadership teams in major organisations, helping them understand the role of data in their future success with ideation or data strategy. So I’ve moved from being a data practitioner to an advisor for major corporations and governments.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?

At university you’re literally taught how to fit a range of mathematical models — like being taught how to use a set of tools. For example, you’re taught how to use a hammer, a drill, or a screwdriver, but no one really teaches you how to build a cupboard, how to talk to a customer or how to help them understand where the cupboard’s going to go in their house. So, early on, a lot of the mistakes I made were around facing a challenge and using the best model I’d been taught at university to solve that challenge — but later finding out that I actually added no value whatsoever because I hadn’t thought of all the other elements around the edges.

That was a big learning curve for me that shaped the rest of my career, and funnily enough I still think there is a gap there. When I interview data scientists coming out of universities, I can see that there is not enough emphasis in academia on creating value beyond which model or which tool to use in real-life situations.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

“Moneyball” is a classic — it’s the perfect example of data being used in a situation where subjectivity had reigned for years. It showed the potential of data and analytics in areas that weren’t really open to that kind of thinking before.

Historically applying analytics to drive business outcomes only really happened in certain ‘analytically-mature’ industries, such as pharmaceutics or insurance — we know drugs need to go through thorough testing processes to prove they work, and we know, if we apply for the same insurance company today, we will get different prices based on modelling, etc.

“Moneyball” was interesting because it opened people’s eyes to areas where data analytics can be very useful but that are non-traditional. Therefore it was a very influential book and film that impacted my industry and my career. I spend a lot of time these days advising companies that have never used data proactively to understand what their opportunity is.

Are you working on any new, exciting projects now? How do you think that might help people?

Ascent is fundamentally an engineering company — we know how to build things really well. With a great team of data engineers, software engineers, data scientists, business intelligence, and cloud engineers we can build digital capabilities that span the data and software world. But we know that the biggest pitfall in the world of data and digital is you ending up (brilliantly) solving the wrong problem.

So, the projects I am working on right now are about creating clarity around a customer’s opportunity and journey with data — helping them understand the exact right thing to deliver. Our work is varied as customers have a range of questions around data to answer, but it can be thought of as three core services: education (helping leaders understand the language and potential of data analytics, such as ‘what does AI mean?’), ideation (what is the right project or the right initiative to go after that will create value from data) and data strategy (helping a company become more data-driven by understanding their current state, their target state, and creating a roadmap that encompasses technology, people process management, etc). So, we are really focusing on creating clarity around the role of data in organisations’ future success.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organisations to be more “data-driven.” My work centres on the value of data visualisation and data collaboration at all levels of an organisation, so I’m particularly passionate about this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

Clearly the role of data in organisations has changed dramatically. Not too long ago we thought of data as a byproduct of what we did. Now people are seeing that data can actually be used as a competitive advantage to drive value and better experience, with over 83% of organisations currently undergoing a digital transformation. However, becoming data-driven means something different to every organisation I work with.

Fundamentally, if you think about data, it can help you become a smarter, leaner, more engaging organisation — making better decisions, automating processes to reduce costs and time, and driving more relevant interaction with customers. It can also help you to deliver on your sustainable ambitions, creating less impact on the environment.

We help organisations understand what it really means to be data-driven by asking a key question — what does a future version of your organisation, where data is the way you do business, look like, and what benefits does it bring? It might be all about efficiency or all about intelligence, but it’s shaped per organisation.

My favourite example of a company created in the past 10 years is Gousto, the food delivery service provider. If you look under the covers, they really are a data company that happens to love food and provide this service. And there are a lot of start-ups now that follow the same model. They are ‘born in the data’ companies that happen to provide a specific service, making them leaner and smarter from day one — I think the challenge for a lot of traditional organisations is going to be how fast they can transform and use their scale to be able to compete with these companies in an increasingly digitally-enabled and data-driven future.

Which companies can most benefit from tools that empower data collaboration?

You can split the world into analytically mature and immature industries. Mature industries are the ones that have always been using data analytics, for example, insurance, pharmaceutics and banking, and companies in those industries have massive opportunities in two ways.

One is to modernise — since they’ve been having the same processes for many years, there is always a reluctance to innovate. Pharmaceutical companies have almost anti-innovation regulations, and most insurance companies are using the same models that were used in the 70s to price cars and houses. So, there is a huge innovation opportunity there.

But the other problem that you get in analytically mature industries is how the role of data is set in stone. Insurance analytics are used in pricing and underwriting, but then it’s much harder to change this culture within Marketing, HR and other departments, so inertia is definitely a big challenge for these companies.

On the flip side you have these immature industries, such as leisure or media or sports. Here the role of data is not yet set in stone, so the remit can be quite broad, but one of the biggest challenges I’ve faced in those companies is trust. I built a really powerful model for a sports organisation, but it took almost two years for it to be widely accepted because it went against the traditional kind of subjectiveness. I come across many companies with ‘invisible reward mechanisms’, with promotion or success based on gut feel — here data is almost the antithesis of the way they run the company so change can be difficult.

For me, big companies that are carrying big costs are really at risk. Look at Monzo as a great example of a company that has disrupted a traditional industry — Monzo now has around six million customers, which is pretty impressive for a company founded only seven years ago. If start-ups can become really data-driven, which means really intelligent, smart, lean and engaging from day one, I think they’ve got an opportunity to scale and take on some big competitors.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

When I think of the decision projects we’ve delivered over the years, there are many that stand out. I suppose we’ve done a lot of the classic decision projects, such as which price to charge, what to offer a customer, which product to build, who to hire, etc. But I always like the more unexpected stories as it helps us to think outside the box about what is possible with data.

For that reason, perhaps my favourite informed decision project was for a company that made a very popular coffee product that you can buy in the supermarket. It turns out a challenge in their world was around creating a product with a consistent taste, which can be difficult given the variations in the coffee-making process, from raw ingredients to the way you blend the beans. This makes sense — after all, if your coffee tastes different every time you buy it you would probably stop buying it. We created an algorithm to help them optimise the process in a way that helped them achieve the same taste every time, ensuring they were making the right decisions throughout the process. A very cool project, and a nice use of data and analytics.

I also really enjoy some of our ‘engaging’ projects — imagine you had an infinite number of interns, and you asked them to follow your customers around every day, learning everything about their actions and interactions, so when you next had a call with the customer, you would already have all the information about them. Data analytics allows you to get to that point where you can really understand your customers, so, instead of them having a really broad and bland experience, you can have an in-depth and relevant conversation, offering a unique and tailored service.

So, for example, I’m working with a customer right now who wants to form their call centre. They just want to know what that call is going to be about before they pick up the phone; for example, if it’s a complaint, they want to ensure they are prepared for it, have the right answers or transfer the call to the right person. So, the more you know your customer, the more tailored your approach can be, helping you deliver the best service!

Has the shift towards becoming more data-driven been challenging for some teams or organisations from your vantage point? What are the challenges? How can organisations solve these challenges?

A lot of people think that achieving value with data just involves getting the right technology in place, but actually it’s more about people, culture and change. Large organisations can struggle to quickly change and innovate — to use an analogy, imagine how long it takes for an ocean liner to turn around compared to a small boat.

When it comes to having a conversation about how best to respond to disruption and threat, I have always advised large organisations to try and understand at the top level what a future version of themselves would look like. If they were trying to incubate a brand-new version of their organisation, what would the opportunity be and what can they learn from that?

Companies need to look at the competitive landscape and work out what changes their competitors have implemented successfully to become more data-driven. They can then start to use some of those levers themselves, such as intelligence, efficiency and engagement. For example, if you could automate a big part of your organisation, what would that do in terms of cost-saving and time-saving? So, there are some big conversations that need to take place, because from my experience of working with large organisations, if there was a really focused start-up that went completely up against them, a lot of their market share would disappear quite quickly!

I am currently working with a traditional bricks-and-mortar retailer, and as part of my research I went into a few of their stores, which were completely empty with a few salespeople sitting around not doing very much. At moments like this you know that an organisation won’t last long unless they change right now — if someone sets up a digital disruptor that does exactly what they do, they’ll be in serious trouble very quickly!

So, I think this is where a lot of organisations need to take time out to imagine how they might reinvent themselves and think about where they want to be in the future. Funnily enough, one advantage that large, established companies have over start-ups is data — they have their customer base, digital footprint, etc., so they can leverage that. Data can be both a threat and an opportunity, so make sure to have these ‘how can we become more data-driven?’ conversations!

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

So, there are five ways data can help your organisation:

1. Information. This is about ensuring everyone has access to the right information at the right time, so that they have the insight they need to achieve their goals and objectives. Crucially, it’s not about generating information for information’s sake. It’s better for someone to have the one piece of data they need to achieve their monthly target, than it is for them to have access to multiple reports and dashboards that don’t tell them anything they need to know — so, quality, not quantity, is the key. The easiest way to achieve this is to always use business goals and objectives as the driver for defining what information people need and when. That way, you’ll always be sure that each piece of information you generate is ‘earning its keep’ — actively being used by someone and giving them value.
2. Intelligence. This focuses on using data to enable more informed decision-making across an organisation to create a more intelligent company. If you take Harry Potter books as an example, there is a bit where Harry takes a potion called Liquid Luck, which allows him to make the right decisions all day. Now imagine if you could do that as a company, being able to know which prices to charge, which clients to talk to, which people to hire? Data and analytics can provide the intelligence and insight that people need to make decisions like these quicker and quicker, and also to have more confidence that the decision they’ve made is the right one.
3. Efficiency. This is about using data and analytics to reduce costs and time to execute, to create a more streamlined organisation — for example, by automating and optimising processes to achieve outputs quicker and at less cost. If you currently pay for 50 people to do one thing, how can you optimise and automate the process and make it leaner so that in the end you only pay 5 people to do the same task, but with the same or even better results? Where are the inefficiencies in your supply chain, and how can you best fix them to save time and reduce costs? What is the right maintenance schedule to balance maintenance costs against downtime costs? Data and analytics can answer these questions and more!
4. Engagement. As I mentioned earlier, this is all about using data and analytics to understand your customers, to be able to have more relevant, intelligent conversations with them, and to tailor your services to better meet their needs. Also, customers nowadays expect organisations to have this in-depth knowledge about them too — for example, it’s no longer really acceptable to send marketing to a customer on a product they already have. The payback for using data in this way is obvious — better customer acquisition and retention, but perhaps less obvious is that you should also apply data and analytics in the same way to better understand your employees too. Taking a more data-driven approach to employee acquisition and retention becomes more important every day, given the current scarcity of talent. Successful organisations will be those that use data to identify the right people to hire, and to then improve the experiences of their employees (e.g., streamlined, data-informed processes and tools, and personalised training programmes) to ensure they retain them.
5. And finally, sustainability. It’s important for organisations to understand their impact on the environment and be able to reduce their carbon footprint. It’s even more crucial to be able to quantify the improvements they make and show their progress on the path to net zero objectively — numbers speak louder than words! Here, again, data and analytics provide the necessary tools to achieve this goal, and to enable organisations to differentiate themselves from those merely paying lip-service to improving sustainability.

When people see us, they think we are going to have a conversation about AI, machine learning and data science, but that’s usually not where we start. You can very easily talk about data projects and analytics projects without actually talking about what the business needs or what those projects are trying to achieve, and this is absolutely not what we advocate. Where do you want to be as an organisation? Do you want to be more intelligent, more efficient, do you want to reduce your costs? Focusing on the five areas above is what helps us have the right conversations with our clients.

The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

For starters, you need to want to change, and this is part of the problem. If you have a leadership team or a business at large where the reward structure perhaps is set up wrong, or where you’ve always done things in a certain way, a lot of people will be against change, and this is exactly why a company like that will struggle to become more data-driven.

The right way to go about changing culture is to fully engage the business on why you’re going on the journey to becoming more data-driven. Why does your business need this change, what threats or opportunities are out there if you do (or don’t) change? Employees need to be educated on what data and analytics mean for them specifically and how their lives will improve as a result of the change.

The most important tip is to seek enthusiasm. A lot of the time I work with organisations where there might be 20 managers in the room, and half of them are close to retirement, so they just don’t care about change. But then you have the other 10 people who are very enthusiastic about it. So, you need to find the parts of your organisation that are most open to change, and then make those people data heroes. As those departments become more data-driven, the people in them will have stories to tell about how the changes have improved their lives. They will start acting as advocates and champions for the transformation and having a positive influence on the more reluctant parts of the business! This creates a momentum where other people are looking at those heroes doing amazing things and smashing their targets, and they want to get involved too. So, this is how to achieve a culture shift — seek enthusiasm, make sure everyone understands why you are doing this, and then drive that change through bottom-up advocacy as well as top-down leadership. Otherwise, it’s just never going to happen!

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

The pandemic has created new ways of working — we see organisations operating with more remote and hybrid approach, communicating through Teams and Zoom calls, and so on. This is driving patterns of work based on trust, flexibility and nimbleness. Over the next five years, we are going to see this trend continue, resulting in increased competition and increased disruption.

Take Revolut as an example — it’s a seven-year-old company that is now worth £33 billion! The myth that a start-up can’t become relevant so quickly and compete with traditional companies surely should be dispelled.

So, over the next five to 10 years we are going to see a lot of nimble, data-driven organisations created that will compete with larger, more traditional organisations and start winning. And those companies that are not able to respond or change quickly enough are going to lose out. I see a lot of investment in data due to a lot of new start-ups and disruptors being born, so this is really where the opportunity is right now.

Does your organisation have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you to achieve these goals?

As an organisation, we definitely eat our own dog food! We are actively using data to drive our own ambitions and objectives. For example, we are currently focusing on our own data strategy, going through a maturity assessment to understand where we are and where we need to go next with data.

Our goals are to really create an organisation that spans data, software and digital to a point where we’ve got enough skills to help any organisation out there with any data challenge, which is why we are acquiring some of the best businesses in the world right now. To us it’s all about being an organisation that can drive innovative thinking and digital transformation across any industry.

How can our readers further follow your work?

You can follow our work on the Ascent website, and you always reach out to me directly on my LinkedIn.

Thank you so much for sharing these important insights. We wish you continued success and good health!