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Ben Dias, data science and analytics director, EasyJet

Ben Dias, data science and analytics director, EasyJet

How is your organisation using data and analytics to support the corporate vision and purpose?

 

EasyJet has the ambition to become the most data-driven airline in the world. Prior to the pandemic, we were investing in, and giving greater focus to our use of data to provide our customers a better experience, reduce cost, improve punctuality and operate more sustainably. Our need for data and analytics has significantly increased because of the pandemic, and we are using data-driven innovations, not only to accelerate our post-pandemic recovery, but also to make our business more resilient to future disruptive events.

 

2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?

 

While there certainly have been some new additional short-term priorities that we have had to address, I’m really pleased that our overall data strategy still remains relevant and so has not significantly changed.

 

What has changed in some cases is the priority order and speed that we are doing what we had already planned to do. For example, it has been exciting to build more complex data science and AI models much quicker than we initially expected to, to deal with the impact of the pandemic. The pandemic has also accelerated the culture change required for us to become more data-driven, as the culture change is the hardest part of the data strategy to achieve.

 

As a result, we found that we didn’t have to spend as much time and effort trying to convince business stakeholders of the need for data-driven innovations as we had planned to. Instead, the data team has been busy supporting departments with making all aspects of our business more data driven. Therefore, our focus then shifted instead to introducing a better prioritisation framework to ensure we optimised our resource allocation to maximise our impact.

 

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

 

This year, we are looking to nurture the pandemic-driven culture change we experienced last year, towards becoming more data-driven across the business, and continue to promote data and analytics by showcasing successful case studies. We plan to invest in significantly improving our data literacy across the organisation, further democratising data and analytics. We will also accelerate our new cross-functional collaborative lean-agile ways of working with our business stakeholders to develop and deploy data-driven innovations quickly and at scale - or fail fast trying.

 

Data-driven innovations are only as good as the quality of the data that feeds them. Therefore, we are also expecting to continue to focus on improving our data quality. All of this requires the right people, which is why we are continuing to recruit for many data-related roles.

 

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

 

Data for good has always been an important part of my personal agenda and will certainly continue this year. I volunteer my time and expertise to support various initiatives, such as mentoring underrepresented people working towards a career in data and analytics and advising charities on how to maximise the use of their data.

 

I am also actively engaged with the UK mathematics community, to inspire and support the next generation of mathematicians. My team regularly join data-for-good hackathons and other events, in support of charities.

 

As a business, I’m particularly proud of our continued commitment to sustainability and decarbonising the airline industry. We are still the only major airline that is offsetting all of the carbon emissions from all of our flights, even post-pandemic, and data and analytics will play an increasingly important role in progressing our sustainability ambitions this year.

 

What has been your path to power?

 

Straight after my PhD in the area of human facial expression recognition, I started my career as a hands-on data scientist working first at Unilever and then at Tesco. Four years ago, I then made a conscious career change, giving up the hands-on technical work to take on the new and emerging world of data science management when I moved to take on the head of data science role at Royal Mail.

 

Being a scientist at heart, I continued to apply the scientific approach to management, experimenting with everything from team structures to ways of working to recruitment, and I regularly shared my learnings from these experiments with the wider data community via blog articles and conferences.

 

I was then enticed to join EasyJet when I heard about their ambition of becoming the most data-driven airline in the world. I initially joined to transform the data science function. But in just over a year and a half, my role has now grown significantly to the point where I am now responsible for delivering our data ambition.

 

What is the proudest achievement of your career to date?

 

The proudest achievement of my career to date has to be the amazing transformation of the data science and analytics team at EasyJet. Even though most of the people remain the same, my team has transformed in almost every aspect into a completely new team, using new tools, following new ways of working and significantly increasing their impact on the business.

 

Despite going through this significant transformation in the midst of a global pandemic, the amazing team culture prevailed and has even continued to grow stronger. I am so proud of what my amazing team has already achieved and of how they looked after each other during 2020, which was an extraordinary year.

 

Tell us about a career goal or a purpose for your organisation that you are pursuing?

 

Our immediate priority is to use data and analytics to accelerate our post-pandemic recovery, while setting us up to grow and thrive as soon as the demand for flying picks up again. However, we haven’t lost sight of our ambition of becoming the most data-driven airline in the world. Therefore, one of my key responsibilities is to ensure that as many of our tactical investments and initiatives in data and analytics as possible move us closer to realising our longer-term ambition.

 

How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?

 

Innovating with data is a fundamental part of our corporate strategy at EasyJet. Therefore, everything we do in data and analytics is fully aligned to our business. We are fully ROI-driven, using a backlog-driven approach, which ensures that the business requirements inform the prioritisation of the data-driven innovations we develop and deploy.

 

This results in a close collaboration with business stakeholders, and, in general, a smoother path to deployment and adoption of the new data-driven capabilities we develop.

 

This close collaboration leads to significantly higher ROI. The aviation industry in general seems to be quite good at utilising data and analytics to achieve better business outcomes. I think the fact we will always have to deal with disruptions due to external factors on a regular basis, is a primary factor that brings the two closer together in our industry. As the disruptions cannot be avoided, data and analytics are essential in minimising their impact both on our customers and our business.

 

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mind-set?

 

From my experience I see this as a four-step process: Prepare, Inspire, Empower and Unleash.

 

  • Step 1 – Prepare: Set up the foundations – you need data management/data governance to create one version of the truth and make data accessible;

  • Step 2 – Inspire: Set up a centre of excellence – in data science and analytics – that starts delivering solutions from the centre to showcase the art of the possibl;e

  • Step 3 – Empower: Set up a federated community – starting with a federated analyst within each business function and, when the time is right, also deploying federated data scientists into the business functions. This starts to create the spokes in a hub-and-spoke structure, teaching and empowering the federated analysts and scientists in the business functions to do more;

  • Step 4 – Unleash: Provide tools and training for everyone in the business to get direct self-serve access to data and analytics so that everyone is able to do more and more self-serve analytics.

 

Where you start (ie, whether at step 1, 2, 3 or 4), really depends on where the organisation is on the data and analytics maturity curve. This must be done iteratively, and you have to take the organisation on a journey with you.

 

One very practical approach to building out data literacy, especially for colleagues who are very keen to learn, is to use the apprenticeship scheme. There are a variety of apprenticeship standards available from data analytics to data science that provide an opportunity for people to learn while continuing to work.

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