Accelerating Data-Driven Transformation

Building a data-driven transformation is crucial for modern companies seeking to maximize returns from their data and gain a competitive edge. This process involves more than just hiring data scientists; it requires establishing an enterprise capability that can consistently generate innovative, analytics-based work practices and efficiently scale them across the organization. This article summarizes how companies, exemplified by BBVA, can accelerate their journey to becoming truly data-driven by focusing on four key activities. We'll break down these steps and provide insights from the BBVA case study, ideal for students exploring this topic.

Accelerating Data-Driven Transformation: An Overview

A data-driven company excels at creating, integrating, and liberating analytics knowledge to empower its people and optimize processes. It's about working smarter, continuously. Achieving this state is a multifaceted endeavor, demanding time and strategic execution. Companies must engage in four significant activities concurrently to build this powerful capability.

The Four Pillars of Data-Driven Capability

To become a leader in data utilization, organizations need to focus on these interconnected areas:

1. Cultivate Data Science Talent: Develop a strong community of data scientists capable of crafting experiments, generating insights from complex algorithms, and communicating findings through compelling visualizations.
2. Communicate Data's Value Proposition: Clearly articulate the benefits of data-driven behaviors, tools, and mindsets across the entire company, establishing a common language around data.
3. Co-create New Data-Driven Work Practices: Foster collaboration between data scientists and other employees, as well as external partners, to develop innovative new ways of working.
4. Scale Data-Driven Best Practices: Promote the widespread adoption and use of data by employees and processes to generate significant economic value.

BBVA's success, an award-winning MIT CISR case study, highlights how simultaneously executing and coordinating these four activities can dramatically accelerate this transformation.

BBVA's Journey: Building a Data-Driven Capability

In 2018, Banco Bilbao Vizcaya Argentaria, S.A. (BBVA) was a global financial powerhouse with €691 billion in assets, serving 72 million customers across thirty countries. Already recognized for its digital transformation efforts, including accolades for its mobile banking app and online service, BBVA embarked on an ambitious data-driven journey.

In February 2014, BBVA established BBVA Data & Analytics (D&A) as its data science center of excellence. Initially, D&A focused on generating revenue by developing and selling external products using BBVA data. However, leaders quickly realized D&A's potential for significant internal financial value through operational improvements and for creating valuable digital product features and customer experiences, which were vital to the bank's digital transformation.

Cultivating a Community of Data Science Talent

BBVA's strategy for building a top-tier data science team involved careful recruitment, dedicated development, and effective retooling of existing employees.

• Recruitment: D&A grew from six to fifty data scientists in three years. Each hire was a rigorous three to six-month process, involving deep assessment of both technical and soft skills by a panel of up to ten D&A staff and HR specialists.
• Development and Retention: A custom program offered non-financial benefits, known as an “emotional salary.” This included learning opportunities, flexible work environments, and challenging projects. Data scientists spent about half their time in D&A offices to foster community, and bi-weekly meetings allowed for sharing successes and lessons learned.
• Retooling: D&A partnered with BBVA's training organization to offer programs like the three hundred-hour “From Data Mining to Data Science” course. Two hundred fifty employees, previously data miners in various business units, graduated by the end of 2017. D&A data scientists taught these courses and served as mentors.

Communicating Data's Value Proposition

BBVA leadership understood that becoming data-driven required widespread engagement and change across its traditional, hierarchical organization. They needed to clearly communicate why data and a data-driven approach mattered.

• Economic Impact Framework: D&A developed a framework to credibly measure the returns from data. It classified projects by economic goals (e.g., increased revenue, reduced costs), with business units accountable for measuring and achieving project value. This framework helped D&A manage its own profitability and, ultimately, demonstrated to BBVA how data generated financial value.
• Company-Wide Education: BBVA committed to the then-radical idea of educating all employees about data-driven transformation, including concepts like artificial intelligence and big data.
• Brainstorm@BBVA: A combined live and virtual event in January 2017 attracted 18,000 attendees. Leaders showcased real BBVA data science applications and their impact on digital sales, process automation, and customer engagement.
• Deeper Dive Classes: For employees needing more in-depth understanding, D&A offered small-group classes like “Machine Learning for Designers,” “Machine Learning for Executives,” and “How Algorithms Shape our World.”

Co-creating New Data-Driven Work Practices

BBVA fostered innovation by challenging D&A data scientists to solve problems both internally and externally, leading to new ways of working.

• Inside the Bank: D&A collaborated with business units to improve operations. For instance, they worked with BBVA Customer Analytics to create a dashboard for branch managers, delivering individualized product recommendations. This replaced older, less effective