What does a company need in order to be successful in this new digital environment?
The keys a company needs to be successful in the digital environment: raw material—data—and the other two are human factors—intelligence and innovation. Article written by Elena Alfaro, Head of Customer Solutions Analytics & Open Innovation, and Juan Murillo, Head of Analytics Dissemination and Data for Social Good at BBVA Data & Analytics. 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 caseAt 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:
- For individuals, we’ve helped provide our customers, not only an improved view of their past and present financial statements, but also a future prediction, and alert systems for situations that are about to occur.
- For companies, Commerce 360 provides context information to small and large-scale companies, which would be impossible to develop on their own CRM systems. In addition, a rules-based engine translates statistics into natural language in order to make it easier to interpret by users that are less familiar with statistics.
- For governments, after anonymizing and aggregating our data sources, we’ve opened up to the public information generated in the private sector, with the goal of helping them to better understand the economic effects of tourism, and the characteristics of different functional areas in big cities.
- For development agencies, data can also promote the common good. We’ve contributed to it through direct analyses and also by releasing data