Carolyn Geason-Beissel/MIT SMR | Getty Images
Up in the air, a modern plane generates 1 terabyte of data every 24 hours of flight. For airlines like the Lufthansa Group, this data can be used to create valuable business outcomes, from improved operational efficiency to higher customer satisfaction. On top of this rich data set, Lufthansa has invested substantially in deploying artificial intelligence technologies, improving data quality processes, and hiring data engineers and data scientists. However, in 2023, it recognized that it had to do more to become a truly data-driven company. The industry incumbent faced not a mechanical problem but a human one: Organizational resistance to change stood in the way of transformational efforts. Lufthansa’s data experts felt like they were operating as lone wolves, without the business support and use cases that would get the whole company behind its transformation goals.
In its quest to beat these challenges and speed transformation, Lufthansa recognized that a crucial ingredient had been overlooked: the leadership circle. Leaders can not only motivate and inspire but also coach their employees to drive exciting data and AI use cases and turn their own business units into front-runners of data-driven change. However, internal analysis confirmed that there was a huge shortage of data and AI literacy among Lufthansa’s leadership ranks, from the C-suite to team leaders. Lufthansa felt that turning its leaders into data leaders would be a key success factor for the whole organization.
So Lufthansa created a data leadership program, and along the way, its team learned valuable insights about the roles that people could play in data-driven change. The company’s approach has lessons for other organizations facing the same need.
Data Leadership Is Not Just for Tech Leaders
What is data leadership? At Lufthansa, it means accompanying and supporting one’s team to achieve data-driven transformation together. Lufthansa knew that its future data leaders would not just be tech leads, like chief data officers (CDOs), who drive the implementation of data and AI use cases and corresponding policies, standards, and programs. Especially in data-driven digital transformations, business leaders need to take an active role.1
To operationalize the concept, Lufthansa defined six different roles for effective data leaders (see “Data Leadership: Six Key Roles”) and shaped a corresponding data leadership development program to bring those roles to life.
Lufthansa recognized that not every leader would need to fulfill every role. Having specific roles for different leadership levels and situations turned out to be most effective, as noted during the presentation of this model at the Chief Data Officer & Information Quality European Symposium in September 2023.2
For instance, the organization’s top management, like the CEO, must support the CDO in playing the role of a data visionary — defining the overarching organizational vision of data and AI and its importance. Top managers are also important data decision makers and must act as role models in showing employees how data benefits decision-making. These leaders demonstrate how to tackle the unknowns in decision-making by requesting specific data to fill in those gaps in understanding. Top leaders democratize decision-making and motivate employees by making their own decisions based on data. They are also in the pilot’s seat for deciding which data and AI initiatives are implemented across the company.
The CDO, the most well-known data leader, must fulfill the roles of data transformer and data visionary. While playing the data visionary role, the CDO focuses on developing a target vision — a strategic end state — for the data-driven transformation. The CDO crafts this ideal picture in collaboration with other top management. While playing the transformer role, the CDO provides thought leadership and sets up organizational and technical transformation programs to fuel goals such as data and AI literacy. In these ways, the CDO ensures that data and AI drive the future business.
Technical team and department leads, such as principal data scientists, are much more engaged in playing the role of data driver. The data driver is responsible for understanding the business value of specific data assets, designing and implementing data and AI use cases, and nurturing the efficient use of data in daily work. These leaders know about the organization’s overall data and AI capabilities, including tools, standards, and guidelines, and make sure that they are properly implemented and utilized.
While the technical roles discussed above are crucial for achieving data-driven transformation, they bring just a portion of the required assets to the table. The rest must be contributed by an organization’s business leaders fulfilling other roles.
Business team leaders or department leaders, who are closer to use case creation and implementation, play the data collaborator and data coach roles. These leaders must tighten the loose ends in data-sharing efforts, help break down data silos, and connect the right people across boundaries within the business to support specific implementations. In particular, these leaders must encourage cross-functional knowledge exchange. Further, when acting as data coaches, these leaders must motivate individuals to practice continuous learning with data and AI, empower them to learn new skills, and accompany them on their individual development journeys. In doing so, these leaders also cross into the vital role of data transformer.
Finally, product owners (or similar business stakeholders) must be considered. These leaders frequently function as data owners and curators for specific data assets. A product owner’s main responsibilities are to be a data driver and data collaborator. This leader should be able to understand the business value of their data assets and how they can be systematically developed to further enhance their value. They must support the implementation of use cases and ensure that a data asset is fit for use, meaning that the data is available at the required quality level.3 In a similar vein, they need to ensure that other people in the organization can use the data.
Develop Your Leaders Into Data Heroes
Building from this set of roles and the core responsibilities, Lufthansa expanded its educational offerings to invest in leadership. To develop its data-driven leaders, the company took a tailor-made approach to setting up a training program.
The program includes three training modules: Spark, Inspire, and Activate. The target group of this program comprises all leadership levels, from team leads to top managers. The training is not mandatory; leaders decide whether to opt into the training and can choose among the three modules, depending on their needs.
1. Spark
The Spark module teaches that data drives business. It aims to train people to:
- Understand the power of data — and the limits of data and AI.
- Drive data use cases, and build business value faster and cheaper.
- Glance into the future by understanding data and AI megatrends.
Spark module coursework and activities educate leaders to be effective data drivers and data decision makers. Leaders learn the foundations of data and AI through a mix of instruction and hands-on workshops. They experience what it means to actively engage with data, machine learning, and AI, and even analyze data and train their own AI models using open-source tools.
Leaders learn the importance of creating cross-functional teams with expertise spanning crucial areas like data science, data engineering, business applications, and operational know-how. They also explore how to frame business problems and pain points while developing real-world data and AI use cases. Finally, the leaders participate in a “pitch to win” exercise for a hypothetical data-driven project: They bring together everything they have learned to create and structure their own use cases to drive business value and persuade decision makers.
2. Inspire
The Inspire module teaches that stories drive use cases. It trains leaders to:
- Show the North Star (desired outcome) when building a data strategy and vision.
- Tell a data story that’s inspiring, high impact, and memorable.
- Build an internal network to rally people around the data mission.
The module shows leaders how to act as persuasive data visionaries and data collaborators. Participants learn how to create compelling data-driven narratives to motivate their teams, stakeholders, and leadership to work in data-driven ways and transform the business using data and AI. The foundations of storytelling are a focus, along with how to use data imaginatively to win people’s hearts and minds.
These leaders engage in launching a data-driven transformation process by developing a data and AI strategy for their own units. For this exercise, leaders identify any skills gaps for implementing the strategy and build an internal network of people to support the data mission and the improved process.
3. Activate
The Activate module teaches leaders that they drive transformation. It trains them to:
- Learn the characteristics of an effective data leader.
- Build a data culture by investing in people and avoiding toxicity.
- Handle trials and triumphs as part of an experimental culture.
The Activate module shapes leaders who can support and develop their teammates during data-driven transformation work. Leaders are asked to step into the data coach and data transformer roles and evaluate their personal and team data and AI maturity levels. To increase resilience, leaders also learn to spot biases and protect their teams or units from threats to success. From here, they create action plans, customizing a road map for implementing their own data and AI strategies. The leaders also craft plans to develop their data leadership skills and their unit’s data citizenship.
Leaders explore data culture through an intensive hands-on workshop, learning how to create a team environment that allows data and AI use cases to flourish. Finally, data and AI experts and leaders from inside and outside the Lufthansa Group participate in a data culture panel, where they exchange lessons and develop concrete strategies for driving their own data culture.
Four Strategies for Success
While executing on this program with its leaders, Lufthansa found keys to success that other organizations can emulate. Use these four strategies to get leaders ready for take-off in doing data-driven transformation work:
1. Define tailor-made data leadership roles. If you’d like to build a similar program, you’ll face a big decision: how to pinpoint all of the essential data leadership roles that are required in your organization. Lufthansa’s approach can be a starting point.
But to be fully effective, you must clearly outline the strategic importance of data and AI in the organization and identify the gaps between your ambition and reality. Against that backdrop, you can shape the roles to meet the expectations of your leaders and develop the skills required to turn those ambitions into results.
This insight was critical for Lufthansa because data and AI were considered technical domains rather than business-related ones. Therefore, business and tech leaders had to be able to find themselves within Lufthansa’s framework of six data leadership roles, given their varying levels of data and AI literacy.
2. Cocreate the leadership program with your team. Involving Lufthansa leaders from various groups to understand their needs and demands was crucial to developing a data leadership program that aligned with both strategic goals and operational data and AI goals. The company sponsored a series of exchanges, such as interviews, workshops, and roundtables, which proved important for another reason. These interactions helped Lufthansa develop a value proposition and narrative explaining why its leaders should participate: to become front-runners and role models of data-driven transformation and actively shape the future of their business units.
Consider the perspective of your target group of learners, and don’t assume that the value of data and AI skills is self-explanatory. Gaining familiarity with these topics won’t feel like a natural consequence for all leaders, and they won’t all be waiting eagerly for data- and AI-related change.
3. Create greater organizational awareness on data leadership. To build wide interest and engagement, connect with your HR organization’s leadership development team, which should integrate the data leadership program into its standard offerings. Spread the word and showcase the training’s benefits to leaders using internal channels, like podcasts or internal news apps, especially when kick-starting your program.
Do not underestimate the value of personal information exchanges early on: Once you persuade the first group of leaders to participate, news about their experiences will spread quickly through word of mouth. View the initial training as a learning opportunity for gaining participant feedback. By listening and iterating, you can improve the program as needed and activate the first participants as multipliers and ambassadors.
4. Highlight tangible examples that inspire your leaders. To help all leaders embark on their personal data and AI journeys, it is mission critical to show them the business value of data and AI. You must get leaders fascinated before you can inspire and motivate them to bring a can-do attitude into their business units. Lufthansa recognized that integrating easily understood use cases that demonstrated direct, positive impacts had the biggest effect.
For instance, Lufthansa developed a computer vision technology use case related to carry-on baggage. Using a camera and computer vision software, this solution would be able to detect, assess, and count distinct types of carry-on luggage during the boarding process.4 Because Lufthansa’s leaders travel frequently, they could personally envision the scenario and easily comprehend its compelling potential to streamline boarding process efficiency.
To energize your leaders, draw upon use cases from the core business, or seek out proven examples in your industry, such as initiatives implemented by competitors.
Effective data leaders bridge a crucial gap that still exists in too many organizations. These leaders play a key role in transforming organizations that are leveraging data and AI to increase business value.
As Thomas Rückert, CIO of the Lufthansa Group put it, “The right awareness of data-specific skills among all leadership levels creates the necessary tension to successfully drive the transformation toward a data-driven airline group.”
Training widely to create data leaders should be a key component in any data-driven transformation.
