Article

Transformation towards a Data-Driven Business

Data-fueled value generation as core accelerator for successful transformation

Enterprises are striving for growth, profit optimization and a value-driven portfolio mix - to sum it up: future competitiveness. In today’s world, the success of achieving these goals is highly influenced by a vastly dynamic environment, including changing paradigms and rising megatrends like connectivity, globalization, individualization, knowledge sharing, and security. The opportunities these megatrends provide for an enterprise’s processes, organization, and technological enablers can be fueled and proactively exploited by utilizing its data. This leads to the generation of insights, optimized efficiency, an extended value chain or new business and monetarization models as well as sustainable planning. 

Consequently, more and more enterprises declare data as one of their primary assets, triggering an evolution of data from a supporting to a value-adding role. In particular, organizations of various industry sectors and sizes highlight data-driven decisions as foundations to ensure their competitive advantages in the future, and potentially evolve as pioneers in data-driven ecosystems.

And yet, there are many pitfalls that hinder enterprises to transform their business successfully to generate value from data. Major changes usually are accompanied by an enormous complexity in combination with vast costs and uncertainty. Many of our client experiences prove that managing the dominant complexity and extensive amounts of data and information is a tremendous challenge for enterprises and, therefore, frequently data initiatives are failing or do not leverage the expected value.

But why do most data initiatives fail?

  • Pitfall 1: Technology First rather than Business Value First

When landscapes are historically grown or new trends are adapted regardless of specific business needs, isolated technology-centric decisions can hinder the transformation towards a data-driven business. Rather, the enterprise should focus on value-driven enterprise-wide initiatives with a distinct value centricity, focusing on the business value of various potential initiatives within corporate-strategic boundaries. This fuels value and customer centricity combined with an integrated change and use case management process within the enterprise.

  • Pitfall 2: Inflated Data Initiatives rather than Manageable Use Cases

Enterprises are generally structured into logical clusters, business areas and processes, each having their own, partially contradicting, interests. As each of these organizational groups have dependencies amongst each other, it is crucial to avoid isolated initiatives, which optimize encapsulated silos rather than breaking them down. The guided operationalization and management of use cases following one cross-domain vision is key for a transformation towards a shared data ecosystem. The rationale behind this is to slice the massive complexity into smaller and manageable use cases, which are prioritized and implemented based on their value-add and feasibility.

  • Pitfall 3: People Delegation rather than People Integration

Practice proves that many enterprises are developing and communicating their data initiatives top down only, without considering employees’ needs and expertise. This can lead to confusion, demotivation, denial and refusal throughout the enterprise and transformation journey. A crucial factor for success evolves around the capabilities and skills of the enterprise and its workforce. Becoming data-driven enhances the need for updated roles, skills, capabilities, and a shift in the mind-set which need to be shaped and fostered throughout the enterprise via an early embedding of change management and holistic communication.

  • Pitfall 4: Legacy Applications rather than Scalable Technologies

Due to the historic growth of many companies, enterprise, and solution architects often operate in the brownfield and are facing diverse challenges, including technological feasibility, long-term license contracts, redundant applications, legacy data storage systems, and many more. Hence, technological enablers are rather chosen by criteria of price, legacy and manageability than by a focus on the business’ immediate and future needs. 

This bouquet of challenges highlights the need of a guiding framework to enable organizations to map their journey towards what they, individually, want to achieve. Although the goal appears to be clear, the industry sector, ecosystem, current and planned data initiatives, the maturity and – most certainly – the culture of each enterprise differs. Consequently, the approach to transform towards a data-driven business needs to be tailored so that enterprises can exploit the opportunities hidden in their treasure troves of data to take the lead in their field.

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The Deloitte Data Transformation Map

Deloitte developed Data Transformation Map framework, which supports enterprises to become truly data-driven, manage complexity of the transformation, and generate value from data.

Fig. 1 – Deloitte Data Transformation Map

As the transformation towards a data-driven business cannot be done with a finger clicking but rather resembles a transformation journey, the Deloitte Data Transformation Map visualizes a closely interwoven solution space of business, organization, and technology as a tube map. With seven tube lines, the 34 tube stops and the enterprise repository of a data-driven business, the framework enables enterprises to gain an overview over the ecosphere of a transformation. Further, the framework applies insights and boundaries from client projects, scientific research as well as commonly known norms and standards, such as DAMA DMBOK, ISO 9001, ISO 27001, TOGAF, and UNECE.

The seven tube lines in the Data Transformation Map represent the main pillars of a data-driven business. Their interconnectivity and interaction are essential for the success of the transformation. Each tube line of a data-driven business can be detailed into specific topics, which are illustrated as tube stops. These subsume a broad and deep asset pool, including methodological toolboxes, procedural models, and modular solution patterns. When tailored to the individual needs of an enterprise, the usage of the assets has successfully realized numerous Deloitte clients’ projects. The outcomes of applying the asset pool are aggregated, refined, and stored in the enterprise repository, which documents technologies, target architectures, roles, and processes.

Enterprises can decide, depending on their business needs and maturity level, which stops they want and should logically combine to achieve the desired output. Companies that are currently at the beginning of their transformation journey can, for example, use the framework to develop a data vision and data strategy. For enterprises that are more advanced in their transformation towards becoming data-driven, Deloitte uses the framework to realize the emerging paradigm of data mesh.

The data mesh is a domain-driven socio-technological approach for creating decentralized data architectures and governance structures as foundation for generating value with and through data products by merging data and people. Coherent roles and responsibilities enable the sharing of trusted and high-quality data via an enterprise-wide marketplace, resulting in a rise of re-usability and collaboration across departments. A possible rationale to strive for data mesh is that data is currently siloed and integrated point-to-point, which inevitably leads to high maintenance and implementation costs as well as operational complexity. Implementing a data mesh concept can therefore turn the challenges which arise from the existing megatrends and paradigms into the following tangible value drivers:

  • Business value first driven by overarching data products in the data mesh, directly emerging out of use cases with a dedicated and proven business need
  • Manageable use cases through dedicated use case management and data product lifecycle process which are steered by federated governance structures to break down data silos and establishing cross-domain transparency and accessibility of data products
  • People integration by living the new roles of the data mesh concept (e. g. data domain managers, data stewards and data custodians) that require and encourage all employees to develop new capabilities and actively participate in data-related processes and activities
  • Scalable technologies as an enabler for managing data along its lifecycle, as well as providing accessible and trusted data products to consumers via data catalogs and marketplaces

After setting the scene – what’s next?

Our previous experiences with various clients from different industry sectors have proven that the right strategy and a corresponding transformation paradigm (such as the data mesh) are crucial for a successful transformation towards a data-driven business and realizing the actual goal – to generate sustainable business value. 

When it comes to operationalizing data initiatives to achieve the objective of becoming data-driven three main pillars backed by a comprehensive change management need to be considered: processes, organization, and technological enablement. As such, a transformation changes the way an enterprise operates, this directly impacts the company's processes. Existing processes have to be refined in parts to achieve the goal of generating business value using data while other processes may become obsolete. Further, additional processes for e.g., use case management or data product lifecycles are created. In addition, if an enterprise changes the way it operates, its organizational structure needs to be adapted accordingly to ensure smooth execution of business processes. New roles and responsibilities emerge, a mindset shift towards a safe and compliant shared data ecosystem as well as new collaboration models need to be defined and implemented in an organization. To enable the interlink between both– processes and organization– technological solutions need to be evaluated, tailored and implemented. These solutions, e. g. data catalogs, data marketplaces, and data platforms are central vehicles to provide data-driven services, which unveil the business value of use cases for end users in their daily work.

For more insights and best practices regarding processes, organization, and technologies of a data-driven business as well as accompanying change processes, download here our Whitepaper "Transformation towards a Data-Driven Business".

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