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Data> Big Data Updated: 04 Mar 2019

“Earning customers’ trust is a strategic issue”

BBVA created the Global Head of Data position in March 2017 as part of its strategy to make decisions based on data. David Puente took on this new role, leading one of the most disruptive and essential projects in the bank’s transformation.


Puente’s team focuses on the new platform enabling all business areas to work with data, professional training to learn about advanced analytical techniques and determining how these data are processed and used. Puente is aware that many challenges remain, but if he had to pick just one of his top goals, the answer is simple: “We have to earn the right to use customers’ data, earn their trust and consent,” he said in an interview where he reviews his first year and a half managing data at BBVA.

Question: What does it mean for BBVA to be a data-driven bank?

Answer: We want whatever decisions we make to be based on data. And we also want everything we build (products, services, processes, etc.) to have data, algorithms, and intelligence embedded in the design. In order to achieve this, we mainly need advanced analytical skills that extend across all of the bank’s locations and all functions. At the same time, we also need infrastructure to roll out all these capacities. Having governed raw materials - quality raw materials, meaning quality data - is equally important. These elements - capacities, infrastructure and data governance - underpin the BBVA Group’s strategy for data.

Q: Data analytics have been part of the banking business for years. What has changed? Why are data more important now?

A: For years now, the BBVA Group has been doing things that incorporate a great deal of intelligence - in the worlds of risk and cybersecurity, for example. It isn’t that data are now more important than before, rather what can be done with them. A huge technological breakthrough has taken place. The creation of big data infrastructure on distributed computing has, in recent years, made it possible to develop a capacity to store and process massive amounts of data, making a huge difference in efficiency and costs.

This has led to major advances, especially in fields that are typically associated with artificial intelligence, such as image recognition and the processing of natural language - in other words, everything related to unstructured data.

This leap has translated into products that decades ago were unfulfilled promises that created a lot of frustration. At the same time, it has also whet an appetite for data storage like never before in our society, and for developing more costly methodologies in terms of processing, but that can be operationalized efficiently with current technological structures. This is the case for “deep learning” or machine learning. And that has changed everything.

The big change, therefore, lies in the amount of data and level of detail that can be processed with particularly sophisticated methodologies that used to be too burdensome and expensive.

Q: BBVA was one of the first banks to create the figure of Global Head of Data. What challenges come with leading the bank’s data strategy?

A: The projects that underpin BBVA’s data strategy are projects related to building capacities around specific use cases, developed on the new platform being rolled out by the Engineering area. And raising awareness of data governance: ensuring that data are well defined, quality, traceable and available.

Where are we today? I think we are at a level with enormous traction, which reflects the extent to which the bank’s different locations and global functions clearly understand that this is an essential component of their current and future strategy.

With the Transcendence Project we intend to train 2,000 data scientists, experts and advanced data analysts in the BBVA Group. We have already identified more than 1,200 use cases, which have emerged from the business units, so our new experts can get to work. And this is just the beginning because the use cases will become infinite as the plan progresses.

In fact, we have already trained 220 advanced analysts and 252 more are being trained this quarter. We are also luring young talent through the “Young Professional Data” program, which has translated into 48 new hires.

In terms of infrastructure, the platform still needs to mature, but it has been launched we in all of the bank’s locations, with 37 sandboxes and data hubs in all locations.

In short, the traction is enormous at this time, but we also have challenges. On the one hand, we have the technological challenge of enhancing the platform as more and more data scientists work with it. The training challenge is huge, as our approach is highly focused on training the talent we have within the organization, training 2,000 advanced analysts - including data scientists and data experts. It’s very ambitious. Data governance is also an arduous task that entails establishing additional responsibility in an extensive network of people from the data institutions that are data owners. This means working hand in hand with Engineering in order to properly understand the data sources and the operationalization of quality.

With the Transcendence Project we intend to train 2,000 data scientists, experts and advanced data analysts in the BBVA Group

Q: BBVA is making a huge effort to train employees. Can any employee become a data scientist?

A: The data scientist profile we need is not someone who is completely specialized in highly sophisticated econometric techniques. Most of this type of talent is currently concentrated in the digital giants and we have resources available that mean we don’t have to completely start from scratch. What is really relevant is having a data scientist background, understanding the methodology, and being able to apply it to solving problems in our industry.

So we are looking for people with a strong statistical skills, but also an understanding of the business.

They typically have degrees from different technical areas, engineering, computer science, etc. We also have people coming from more of an economics background. Many others are self-taught in this field. Our approach entails looking for talent within the bank - people who also have a certain degree of business experience.


David Puente, Head of Data at BBVA.

Q: Is it hard to find these new profiles outside of the bank? What is BBVA doing to attract these professionals who are so in demand?

A: In general, when something becomes very popular - and right now there is a lot of hype around artificial intelligence and data analytics - talent in this area immediately gets scarce.

However, we will also have to hire external talent to carry out this project. We have quite a bit of native talent at BBVA, for example at our center of excellence, BBVA Data & Analytics, which to a large extent is the cradle of some of the most innovative services we currently offer our customers. But without a doubt, attracting external talent is a challenge.

And what makes BBVA attractive? For someone with a pure data scientist background, a key issue it the project, and a bank accumulates an enormous amount of data. Data on customers’ economic capacities and transactions, for example, are exclusive to banks. And that in itself is attractive.

If, in addition to being a bank you are BBVA - which to a great extent is currently leading the digital transformation of banking, in which data are a highly substantial element - you are well positioned in the race to attract talent.

Q: What would you point to as an example of using data to be a company that really makes life easier for its customers?

A: There are lots - in many different areas. For example, Mexico is already working with artificial intelligence to automate part of the customer complaint process, and to automate manual tasks in certain operational processes. In terms of customers, there is a wide spectrum, from very little things that make life easier, from simplifying authentication when the app automatically recognizes a recurrent transfer to a known recipient, to more sophisticated services like BBVA Valora or Bconomy in Spain. They are highly advanced, data-based services to help customers make the best decisions in the milestones of their lives. I think we’re just getting started.

Q: Will the competition to the big digital companies end up winning customer trust?

A: In the financial industry, as in other industries, the consumer is gaining more and more power which results in their demands being responded to. I think it's indisputable that customer trust will be needed in order to compete.

But beyond that, at BBVA we have a clear strategic mission: we want to bring the opportunities of the new age to businesses and individuals alike. The most tangible aspect of this mission is to help people make better decisions for their businesses and their private lives, in their financial and non-financial lives. How do you do this in a world where sensitivity about data is on the rise? We conceptualize it with the idea of a circle of trust. In order to use customer data, the customer's express consent is required, because it is the customer who owns his or her personal data.

To the extent we are able to give tangible value to the customer, the customer will understand that we are doing a good job using their data and will increasingly be incentivized to give us consent to use more data, which we will use to create even greater value. It’s a virtuous circle. It is an environment where data belongs to the customer, and the customer can choose to entrust it to any company he or she chooses.

Trust is at the heart of it. The financial industry, like any industry, is going to have to gain our customers trust in order to gain their consent. It is a strategic issue for BBVA. We want to build a business model based on helping individuals and businesses make better decisions; this requires having the customer's consent, which in turn requires their trust.

We want to build a business model based on helping individuals and businesses make better decisions; this requires having the customer's consent

Q: Can financial entities end up becoming “data banks” instead of banks for money?

A: This is an academic debate. Sensitivity about privacy issues is growing among the general public. The Cambridge Analytica scandal and the debate about Facebook put it in the spotlight. Regulation is being implemented – at least in Europe – with legislation like GDPR. But a lot more education is needed; average citizens don't have sufficient information to understand the implications of releasing their data, which by law is theirs.

Financial institutions and banks could be the guardians of data privacy for individuals. It seems quite natural. Ultimately, financial institutions are the custodians of something as intangible as money and wealth, which today is almost nothing more than accounting notes representing economic value in highly secure databases. We could have a debate about the general reputation of financial institutions, but in terms of reliability and security in safeguarding money, the financial world's reputation goes unparalleled. The natural extension of what is almost money – because data is increasingly a primary source of creating value – could logically fall under the financial sector's domain as custodians. It is certainly something to think about, but I think we have to start first with sensitizing the public about what it means to release personal data to be used in certain environments.

Q: What can BBVA do to help customers protect their privacy?

A: We need to help customers understand the extent to which their data can be exposed if they choose to release it. And if they do, they must do so consciously, fully understanding the implications and reach of their decision. At BBVA, we understand it clearly: data is customer property, and we have to earn the right to be able to use it. We have to earn their consent. We want to demonstrate that we are completely transparent and that we treat customer data with even more respect than required by law. We want to help clarify what data means and how it is used, privacy and its implications.

Q: What does more advanced artificial intelligence mean for the customer?

A: I prefer to speak about advanced analytics in general. The examples that we will see in the future will, for example, be related to processing natural language, improved predictions, micro-segmentation, the ultra-personalization of the digital experience, the automation of processes related to transactions at branches or in centralized services.

In general, we’ll see very different solutions, some to make what we're already doing more efficient and others to create brand new services for the customer.