Data have traditionally been perceived as crucial to business operations, strategic decision making, and new business development. The terms under which data have been investigated have varied in recent decades, and range from business intelligence, business analytics, and big data to big data and analytics. But while the mission of maximizing value from data may remain steady, the particulars change.
Today, the biggest barrier to becoming more data-driven is company culture (see The Challenge of Becoming a Data-Driven Company). Building a data-driven culture requires deep interventions into the organization’s capabilities. Mindshift changes as well as greater knowledge and skills are required to become truly data driven. In a data-driven organization, everyone owns data. But there are challenges to overcome to reach that goal. In the following, we describe how companies can get there.
Blessing and Curse of Prevailing Structures
For established companies, the prevailing organizational structures are either a blessing or a curse. On the one hand, a great data and capability pool can be leveraged to put forward new data monetization initiatives. On the other hand, the same capability pool and inflexible structures might become a bottleneck, as the required innovation level is not met.
Unlike digital-native companies—such as Google, Netflix or Facebook—traditional companies find it challenging to maximize value from data. The former type of company has data value realization at its core, which paves the way for introducing data-driven business models (see What are Data-Driven Business Models?), including sales of advertising space (Facebook) or new insurance services (Amazon and Berkshire Hathaway). In traditional companies, introducing new data monetization initiatives implies organizational transformation and capability mobilization. Deep interventions into the entire organizational structures are required. Established companies face challenges in understanding existing data resources and mobilizing capabilities from different sources. Capability mobilization comprises internal company capability-building as well as the orchestration of external capabilities for value creation and capture. Developing strategies for data monetization when there is a scarcity of highly skilled resources, such as data scientists and full-stack developers, has become a key concern of executives (see Power to the Business Users with Data Mesh). In particular, enabling seamless collaboration between business and IT while fostering innovative working methods is a tremendous challenge. Establishing the required structural, cultural, and procedural conditions for data monetization initiatives to thrive in the context of prevailing organizational structures and scarce resources requires a gradual development of business model components and an understanding of the data from analytical and technical standpoints (see Architecture Management for Data-Driven Business Models).
Here are five practical recommendations to overcome the challenges of prevailing structures.
1. Treat Data as an Asset
Data monetization initiatives require an understanding of data as an asset. Data quality and reliability are crucial to the value proposition. Data privacy and ethical constraints might have long-term impacts on the company and it is vital to assess them. For data monetization initiatives, two perspectives on data should be considered. One, data processing within data-driven business models should be described via business-model frameworks and use cases with a high abstraction level. Detailed views on the data and their processing should be provided from an information system standpoint. This includes informational entities and how they are used, created, captured, transmitted, stored, retrieved, manipulated, updated, displayed, and/or deleted by processes and activities within the data monetization initiatives. The internal perspective answers the questions: What data is required? and How is it processed for the value proposition? Two, data is an asset used as an input resource for data monetization initiatives. Regarding data as an input resource for data monetization initiatives makes their harvesting crucial. Sourcing data for monetization internally necessitates the organizational building of a data foundation for data monetization initiatives. This includes data governance and data management procedures, regulations, and policies. A reliable data foundation is essential for data monetization initiatives. The effort required in laying this foundation should not be underestimated. Establishing standardized data management and governance procedures can be a transformation in and of itself.
2. Apply a Start-up Way of Working
In order to deal with the inflexibility of the existing structures and allow the data monetization initiatives to unfold, a “start-up” way of operating is recommended. If the established company takes such an approach in combination with recommendation 1, it can benefit from the wealth of data collected over decades and other resources while being able to move to new technology at the required pace. These synergies allow the established company to outperform the innovator. In terms of capability mobilization, the sourcing efforts must be broadly conducted beyond the internally available resources, especially when establishing a new business model. Role descriptions for analytical capabilities to realize data monetization initiatives should be defined independently of an internal view of prevailing resources. Matching the demand with the available capabilities should be a subsequent step. Additionally, one core team with end-to-end responsibility should be appointed.
3. Establish an Agile Mindset
Capability mobilization in data monetization initiatives should be conducted with an iterative/agile mindset. In this context, an agile mindset refers to project management techniques that are applied to endeavors with blurry requirements. This interpretation of agility contrasts with traditional project management in software development, which is characterized by clear outcomes, and time and budget constraints. The teams working on data monetization initiatives need to embrace failure and learn quickly while moving forward. As the requirements are unclear, an iterative requirement selection and prioritization approach is required. Without clear outcomes and no defined requirements, big data analytics must be seen as a cost of doing business. Data monetization endeavors are long-term initiatives; traditional management techniques may not be fruitful.
4. Foster a Central Decentralization
Establishing an agile and start-up way of working requires flat hierarchies and autonomous team setups. For data monetization initiatives, independent teams with end-to-end responsibility should mobilize capabilities along with the key enablers, with a central unit (value realization office) coordinating the decentralization-related work. The value realization office ensures senior management engagement with regular reports, progress tracking, and decision points. Additionally, it coordinates the allocation of financial resources for the planned endeavor. Effective financing is a crucial component of data monetization initiatives. In order to ensure sufficient funds, the procedure for data monetization initiatives must be continuously cost/effort driven. Ideally, the funding is structured in a staged approach, similar to start-up funding rounds.
5. Prioritize and Pipeline Business Models / Use Cases
Data monetization initiatives confer a high risk for established companies, as they require capability building in various domains with unclear returns. Implementation requirements are indistinct, and the business impact difficult to estimate. Analytical capabilities must be established to understand the data and technical capabilities for their processing. In order to prevent falling into the “hype trap” of data monetization initiatives, it is vital to keep a value-driven mindset through the endeavor. Within the ideation phase of data monetization initiatives, a multitude of business model ideas / use cases should be developed. Each option promises returns and demands capability mobilization. Sequencing the business model ideas / use case for testing and realization is crucial. An exploratory approach involving iterations of testing, prototyping, and, ultimately, implementing, is recommended.
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