Maximizing the value from data by renovating existing business models or introducing new data-driven business models has become a key concern of business leaders (see What are Data-Driven Business Models?). Latest technological advancements such as cloud, internet of things, big data, and machine learning, have contributed to the rise of data-driven business models as an emerging phenomenon in the data sphere. Data-driven business models are characterized by data as a key resource, data processing as a key activity, or both. Novel opportunities appear for organizations to monetize their data.
Established companies, who are expected to be resting on tremendous amounts of data, are particularly exposed to increasing pressure in this regard. These companies have been collecting data for decades, which could give them an advantage over start-ups with limited data resources. However, prevailing organizational structures (and especially the architecture) might become a bottleneck. Data is collected and stored in silos or hoarded in monolithic data lakes. With the rise of data-driven business models, many organizations have started to invest heavily in new technologies. Business units are introducing their own data and analytics solutions, creating more silos and, along with them, increasing complexity. The fragmented data and application landscape leaves confused business users behind, neither able to treat data as an asset nor to leverage it for value generation.
The Potential of Enterprise Architecture
Introducing new data-driven business models requires deep intervention in the entire organizational structure. The current (as-is) architecture must be well understood and the desired target (to-be) architecture, which would apply the data-driven business model, must be carefully planned. This is where enterprise architecture comes in. Enterprise architecture has proven its potential in many IT-related projects and is deeply rooted in the information system discipline. It is a critical component for strategic planning, top management decision making, and project management. By providing tools such as metamodels, frameworks, and management methods, enterprise architecture supports transparency, building on an organization’s key components – from business, data, and application to the technology level. Furthermore, enterprise architecture helps businesses organize their architecture in a manner directed toward a common vision. The enterprise architecture management function supports the transition from the as-is to the to-be state through several intermediate architecture stages.
Enterprise architecture has been successfully applied in the context of big data analytics and business model design, as it helps to achieve transparency across relevant social and technical elements and their interdependencies. Enterprise architecture can provide answers to stakeholder concerns based on models and tool support for the design and implementation of such socio-technical systems, where companies struggle to understand the existing data resources and capabilities. Similar to the support for business model design and big data analytics, enterprise architecture can be beneficial for data-driven business model innovation. Beyond that, enterprise architecture offers blueprints, reference models, frameworks, and assessment tools that are beneficial in making the as-is state transparent and developing the to-be state of data-driven business models. In order to justify huge investments in organizational transformation, as required for such models, enterprise architecture first and foremost supports the data-driven business model design phase. A good understanding of data assets and system components (and their interrelation), as well as their link to the ecosystem, are key for successful data-driven business model realization.
Data Architecture for Data Assets
As highlighted above, data architecture is an essential component of an enterprise architecture. With the dawn of data fabric and data mesh concepts, new opportunities are arising to manage data as an asset by applying these emerging data architecture design patterns. For example, domain-oriented and decentralized data ownership and architecture enable federated data management and data sponsoring along business domains. Additionally, the increasing amount of produced metadata, while the actual data flows between the system, reveals automation opportunities for classifying and contextualizing data.
The bottom line is that the enterprise architecture practice offers vast potentials in the context of data-driven business model design and realization. Many organizations face architecture concerns on their journey to data-driven business model realization. With the rise of data fabric and data mesh as emerging data architecture patterns, new opportunities are revealed for established companies to maximize value from their data (see Power to the Business Users with Data Mesh).
Assefy applies emerging data architecture concepts and federates data management and sponsoring with related domains, providing an interface between the complex and fragmented data and application landscape to business users. Coming from the right side of the information supply chain, Assefy focuses on connecting business value to data assets.
Contact us and find out how you can leverage your enterprise architecture to Assefy your data.