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Big Data 31 Jan 2018

What does a company need in order to be successful in this new digital environment?

We inferred as much in the title to the first article of this series: to key to realizing all the opportunities of this new technological era, lies in these three factors: raw material—data—and the other two are human factors—intelligence and innovation.

We need people who can extract value from data by asking the right questions and articulating their responses. Data scientists with a deep knowledge of statistics and broad programming skills (R, Python, Scala…) are currently in high demand. They must also acquire knowledge of the business, as it is being analyzed by colleagues in other areas (commercial intelligence, risk analysis, design, legal framework). These are people capable of understanding a problem, looking for solutions in a large dataset, and programming an algorithm that solves the problem automatically, allowing it to be carried out on a massive scale, through iterative processes that are capable of learning from new results.

Four years ago, when we founded BBVA Data & Analytics, the challenge was to create an attractive environment for this rare type of talent. These individuals tended to be more entrepreneurial, inclined to developing their own business ideas, joining digital native companies, or pursuing academic research, rather than entering large corporations. For training and growth, and for retaining talent, it’s essential to create an attractive business culture for these professionals; the pillars on which we’ve built our culture are as follows:

  • Encouraging applied research and providing space for ideation: In addition to responding to the demands received—generally, for incremental innovation—time must be devoted to answering the questions that we ask ourselves, and which can lead to innovative, disruptive proposals. We also collaborate with the academic world, by carrying out joint research and supervising doctoral candidates, which is an incentive for the members of our team with teaching experience. The fruits are reaped in the form of research articles, and we also increase our knowledge of the possibilities suggested by the data we work with.
  • Shared learning and online work. Internally, tools must be created to allow knowledge to flow and for the analytical models and computer code to be reused. The agile methodology is employed in multi-disciplinary teams and training programs are given to traditional analysts by data scientists. Besides promoting efficiency, all of this keeps us from working in isolation, without any influence on the company’s core business. On the external side, we are in contact with other distinguished centers and actively participate in forums and conferences, where the new developments in our area are presented, along with their business applications.
  • Responsible flexibility and evaluation by measurable objectives: elements such as telecommuting and flexible scheduling help to create a work-life balance and allow each team to best organize their time. The goal is to strike a balance between execution and research, by being aware that at the end of each year, achievements will be evaluated; and, that their impact on the business is one of the main metrics of success.

What results can be expected? The BBVA case

At BBVA Data & Analytics, the appropriate combination of data, talent and innovation is helping to make BBVA one of the most successful financial companies in terms of its digital transformation, through a portfolio of new solutions aimed at a diverse group of users:

But we’re convinced that the most promising results are yet to come. Specifically, during the year that has just begun, the central place that data has occupied in the bank´s global strategy will bear new fruits.

In the next article, we’ll address an essential aspect of data applications in business, one that finalizes our overview of the new possibilities and risks inherent to the digital transformation processes: the responsible use of data and algorithms.

*This is the second article in a 3-part series exploring the challenges and opportunities of Data in the digital world. Read the first one here and the third one here

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