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Angel Serrano, head of data science, Santander UK

Angel Serrano, head of data science, Santander UK

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

 

Our data strategy has been built using the company’s vision and strategy as cornerstone. Therefore, data and analytics activities are fully aligned to our organisational goals and division objectives. Before any initiative is started, it is first linked to one divisional objective and then to an organisational goal, and if we cannot find that link, we ask ourselves why we should invest on it.

 

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

 

I believe 2020 has impacted data and analytics more than any other year. Leadership found in data and analytics the solution to problems their organisations never faced before.

 

At Santander, 2020 was the year where we moved from the data experimentation phase, where advanced analytics supported specific programmes, to a data-driven strategy, where data and analytics were fully embedded in business and operational processes.

 

Data is now one of the critical components to be assessed on all initiatives to the point that any funding request requires that the data necessary for the programme is in the right shape before getting approval.

 

There are several factors that contributed to this change in mindset, one of them being changes in leadership during the last couple of years which changed the culture and mindset. Another key factor was the coronavirus, which accelerated this shift.

 

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

 

In terms of data governance, I expect this year to transition from defensive data management to a proactive data development. Companies are realising that models are only as good as the data they receive, commonly known as “rubbish in, rubbish out”. Quality of the data is difficult to maintain unless proactive data curation activities are implemented in key data elements.

 

I also expect to increase the use of advanced analytics to reduce cost and increase efficiencies though process automation, get better engagement with customers through understanding their needs and trends, and increase oversight in risk management.

 

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

 

An important part of my role is data for good by looking after our customers. A couple of examples of those activities are models that predict when customers are likely to be in difficult or vulnerable situations and anticipate any customer needs before they are aware of them.

 

But this year data has been more important in society than any other year due to the fast deployment of the government loans, which have helped hundreds of thousands of SMEs to get through the year.

 

What has been your path to power?

 

I started my career in PwC in 2004, and stayed for 13 years, ultimately leading one of the data and analytics teams in the financial services consulting practice.

 

In 2017, I joined Santander UK as head of data science to build the advanced analytics capability. After two years, I was appointed as interim CDO, and a year later, I was appointed as co-head of the newly created data centre of excellence in Santander UK.

 

The Centre was created to align the data management strategy to the corporate vision and purpose. It includes all data capabilities (ie, governance, quality, engineering, metadata, analytics, and data science), but is also a concept that moves away from a federated model and into a centralised approach, with a focus on customer and product.

 

What is the proudest achievement of your career to date?

 

The creation of the data science capability and platform for Santander UK.

 

When I joined in 2017 there was no data science capability, infrastructure, or budget. During my first year I had to build a business case to get the funds required to build them and present it to the leadership team. Once approved, I built the team by training internal resources and recruiting external experienced professionals, and the platform by leveraging existing analytics platforms and tools.

 

Our data science team now has over 40 professionals and the platform is being actively used by over 150 employees.

 

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

 

One of Santander’s strategic objectives is to invest in our people. In data and analytics, this is becoming critical these days due to changes in technologies and new capabilities needed to support business and operational needs.

 

Particularly relevant are advanced analytics and cloud technologies, which are continuously developing. I believe a combination of external support and investing in current employees by providing the right training, is the best way to fulfill those demands.

 

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?

 

Alignment with business has been an issue in the financial services industry, because even if the data strategy is aligned, there is not a data-driven culture. This is something we have been changing during the last couple of years.

 

Some of the activities that have helped to get a better alignment have been bringing in business teams to help to develop the data strategy and periodical data forums or sessions, where business teams are invited. But the key for these programmes to succeed is the change in culture, starting from leadership.

 

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

 

As the consultant and writer Peter Drucke said, “culture eats strategy for breakfast,” and I could not agree more. Companies can create data-led strategies, but unless there is a data-driven culture starting from the top nothing is going to change.

 

Most data-driven programmes fail because leaders do not invest in data, starting from improving the quality. Data curation activities are often ignored because they do not produce immediate results.

 

Data-driven cultures require leadership sponsorship, and, to succeed, it needs investment in training existing employees, recruiting experienced professionals in key positions, and, crucially, patience.

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