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Dan Taffler, Group Director of Data and Analytics, Reach PLC

Describe your career to date

 

I started my career as a Financial Analyst, but as I used more and more data and analytical tools, I realised that I wanted to specialise in this space. I have always felt that data is a powerful engine for business transformation. However, without being informed by (and informing) a business strategy, data can be prevented from reaching its full potential. Doing an MBA at Manchester Business School gave me the tools needed to link these pieces and advocate for data at executive level. Throughout roles with larger and larger remits I have found that data teams often work best with integrated product management and software development capability. This has enabled us to partner more deeply with stakeholders, iterate rapidly, and deliver lasting business value and cultural change. Our successful artificial intelligence (AI) products are great examples of how this product and user focus moved us from being clever techies to key strategic change-makers and an engine for business growth. Over 20+ years in this space, I have been Chief Data and Analytics Officer or held the top data roles in four organisations and have really enjoyed creating strategy, building teams, and delivering positive outcomes for these businesses. This includes approximately £70m of directly attributable net present value, several business innovations, and several enterprise-grade functions mostly built from scratch. Probably the most important element of this success has been finding good people, treating them as human beings, and creating a friendly environment to do interesting work. Data people are too often put in a small box, but we are often very creative and passionate individuals. Fighting for the right to create things ourselves as much as partnering with other functions has had brilliant results. 

How are you developing the data literacy of your organisation, including the skills of your data teams and of your business stakeholders?

 

For me the key thing for developing data fluency and a data culture in organisations has been to partner with the business on delivering business outcomes and solving problems (often without mentioning data at all). It might only be afterwards that they realise that they have learned several data concepts and are able to use them when thinking about future projects and ideas. Learning through doing has been key to the business, actively pulling the data team into collaborating on more projects. This works at an executive level all the way down through the business, but benefits from selling a business transformation vision (powered by data) as well, of course. Getting the level just right can be tricky but linking it to business outcomes and showing what the day in the life at all levels would be has been useful in making it tangible for people. In terms of internal team skills development, it is also driven from a data strategy that supports and extends our business strategy. This suggests what capabilities are needed and a gap analysis can be done for planning purposes. However, by focusing each step on business problems, opportunities, and solutions it was possible to bootstrap the learning and functional development processes with delivering business value on the way. Lastly there is also regular horizon scanning to see if there are new tools or techniques that might give a substantial advantage that we should pivot to – this makes us nimble and adaptable. Media is very fast-paced and changeable. 

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organisation or just the data function?     

 

We are already at a fairly advanced stage of AI maturity as we have multiple AI products live and working at massive scale. Some of them are user-focused, such as our AI Editorial Toolset, Guten, and others are backend products, such as our Next Best Action recommendation and decision engines. We also have guidelines on the use of AI and a governance structure that supports the safe use of it, with working and steering groups, champions, and SMEs within functions, and a very strong Data Science and Product team to support technical elements and solution design. The successful adoption of AI products has been really helped by involving business stakeholders in designing these products and focusing deeply on business opportunities and problems to be solved. Stripping out the fluff and hype of AI and not pushing out technically exciting features that are hunting for a use case has also been critical. The hype curve for AI has been extreme and being able to stay grounded and focus on the “so what?” of AI solutions design has paid dividends. There is still further to go, of course, so one of the change management pieces coming now is how we create an AI culture as much as a data culture. I like the analogy that AI is a general technology like electricity, because its enormous potential is open-ended, we cannot predict what some of those future use cases will be. However, creating AI products together with the business opens deeper grounded and value-driven discussions at executive level. This helps us plan for the future with the art of the possible and the art of the deliverable hand in hand. 

Dan Taffler
has been included in:
  • 100 Brands 2020 (EMEA)
  • 100 Brands 2022 (EMEA)
  • 100 Brands 2023 (EMEA)
  • 100 Brands 2024 (EMEA)

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