Many companies face challenges in identifying and prioritizing initiatives within their digital transformation portfolio. The ‘data-driven’ index is presented as a useful tool for addressing these issues, as its use is strongly associated with success in digital transformation processes, placing data at the heart of business strategy.

Current Context

We live in an environment where the boundaries between the physical and the digital are blurring due to the exponential increase in digital density. This change is characterized by three phenomena:

1. Consumerization : Digital technology has gone from being an exclusive tool of corporations to being within reach of consumers, changing the relationship of companies with the market from unidirectional to bidirectional.
2. Democratization : The reduction in technological costs, driven by Moore’s Law, has lowered barriers to entry in various sectors.
3. Platformization : Linear value chains have evolved into information ecosystems where multiple actors collaborate in value creation, orchestrated by a platform, redefining the perimeter of organizations.

Digital Transformation

Digital transformation is not just a technological adoption, but a continuous process that involves constant adaptation and change. It impacts three key dimensions:

1. Technological infrastructure : To efficiently manage data.
2. Business model : To create and capture value.
3. Organizational model : Includes new organizational processes and capabilities.

In this context, data is gaining renewed value as a fundamental asset. Organizations must become data-driven entities to innovate in their business models and processes. While previously data was primarily used for control, in today’s environment it is also a raw material for innovation.

Change in data function:

• Defensive Function: Data is used to monitor and control business activity.
• Offensive Function: Data has become an essential resource for innovation and the development of new business models, driving digital transformation.

Traditional Organizational Model:

• In traditional organizations, the business model guides how the market approach and management are structured
• The technological infrastructure, which supports both the business and organizational model, must be synchronized with the business objectives.
• This approach has been effective in stable contexts, allowing the organizational model and technological capabilities to align with business objectives.

However, this view has limitations in today’s high-density digital environment of constant change. The rigidity that characterizes incumbent organizations, where adaptation to new business models is hampered by entrenched structures and competencies, is often compared to the difficulty of changing the function of a building once it has been constructed.

In response to this scenario, a new conceptual framework is proposed that places the data model at the center of the business strategy, influencing both the business and organizational models (see Table 2).

By placing data at the center, the data model catalyzes the development of new value propositions and business models, and facilitates a more agile and adaptable organizational structure, capable of proactively evolving in response to market dynamics.

Business Model

Data interactions can be used to achieve advances in  automation ,  prediction ,  coordination  , or  personalization , which contribute to both the creation and capture of value

1. Automation. It’s about doing more with less, through the digitization of processes to make them more efficient. This approach has concrete examples in industry, highlighting its relevance in improving the value proposition and internal operations, particularly within the framework of Industry 4.0.
2. Prediction. It allows forecasting the future state using AI models and improves the value proposition in areas such as health, anticipating preventive care needs.
3. Coordination. It focuses on the interaction of different actors through the exchange of data, mainly through platforms and APIs, reinforcing the value proposition by facilitating integrated service ecosystems.
4. Personalization. It seeks to better meet customer needs without increasing costs, and is reflected in the value proposition by offering products and services tailored to individual needs, with highly personalized customer experiences.

Organizational Model

While the business model focuses on the strategic use of data to generate value, personalizing offerings and anticipating market trends, the emphasis in the organizational model lies in how to use data to efficiently execute the business model, optimizing internal processes and fostering a culture of agility and collaboration

Six work practices, called meta-competencies, have been identified. These represent essential capabilities that organizations must develop and implement to successfully navigate digital transformation.

1. ‘Outside-in’ thinking. It focuses on identifying customer needs and the organization’s context, and draws on practices such as  design thinking ,  customer journey mapping , and  job-to-be-done , using data to accurately capture trends and behaviors.
2. Learning orientation. Aligns objectives with the development of new knowledge, using data in  lean startup techniques and  A/B testing  to iterate and constantly improve.
3. Agile execution. Through methodologies such as  scrum and  kanban , it leverages real-time data to iteratively adjust value propositions, fostering a culture of adaptability and continuous improvement.
4. Cross-silo collaboration refers to effective collaboration between different areas of an organization, which is essential to promote innovation and multidisciplinary teamwork.
5. Participation in ecosystems. In these, we collaborate with  partners through platform-based business models, with modular architectures and APIs for data exchange, creating joint value propositions.
6. Data proficiency.  Developing a data-driven culture is crucial for ensuring that decision-making in an organization is based on solid evidence. This involves not only incorporating specialists such as data scientists and data engineers, but also fostering an understanding and mastery of data analysis throughout the company, establishing data as the common language in all decisions and operations.

Data Model

The data model encompasses both the technology infrastructure critical to operations and innovation and the data management functions that ensure its security, characteristics, and governance

Technological infrastructure

The technological infrastructure constitutes the integrated IT architecture of the organization and is based on the coexistence of two fundamental functions within the IT architecture: the operational backbone and the programmability or digital platform, which facilitate both the efficient execution of existing business processes and the innovation and development of new digital business models.

1. Operational backbone. Ensures smooth and efficient day-to-day operations. Includes integrated enterprise systems (ERP, CRM, etc.), data repositories that act as the single source of truth (known as SSOT), and processes for data transformation and loading (ETL). Its goal is to automate repetitive processes, provide visibility into transactions, and support essential business operations.
2. Programmability. It is established as a central axis for innovation within organizations, offering the necessary flexibility to adapt quickly to market changes and explore new digital opportunities. Through elements such as reusable software, APIs for modular integration, and  data lakes for data experimentation, digital platforms facilitate the development of new value propositions and digital business models.

Data Management

For its part, data management in data-driven organizations is based on three fundamental pillars

1. Data security. Special emphasis is placed on this issue to protect information against cyberattacks through robust protection measures, early detection, rapid response, and continuity plans to ensure data integrity and availability.
2. Data characteristics. This aspect encompasses the accessibility, usability, quality, and reliability of the software used, highlighting the importance of optimizing the entire data lifecycle, from its capture to its effective application.
3. Data governance. This includes the use of tools such as data dictionaries, which facilitate understanding and access to data. It also involves establishing clear policies that define who can access the data and under what circumstances.

‘Data-driven’ index

Based on the dimensions that characterize the business, organizational, and data models, a maturity index is defined for each model on a scale of 1 to 5, where 5 represents maximum maturity and 1, minimum. Thus, the business model index is obtained from the weighted sum of the use of automation, prediction, coordination, and personalization. In turn, the organizational model index is the weighted sum of the development of the six meta-competencies:  outside-in thinking , learning orientation, agile execution,  cross-silo collaboration , ecosystem participation, and data proficiency. Finally, the data model index is obtained from the weighted sum of the development of the operational backbone, the digital platform or programmability, data security, data characteristics, and data governance.

Table 4 illustrates the distribution of the 161 organizations in the aforementioned study according to their maturity, based on the fact that the Idd _ranges  from 1 to 5. The average _Idd  of the study is 2.91 and most of the companies are below the middle of this scale, which indicates that there is considerable room to improve the use of data in various organizational areas.

To better understand Table 4, it is crucial to understand that each company’s Data Development Index (DDI ₎  is analyzed according to three main dimensions: business model, organizational structure, and data. These are fundamental to understanding each company’s position in the chart. Organizations with less maturity in these areas are located in the lower left. On the other hand, the eleven companies in the upper right corner, with scores above 4, stand out for their more advanced use of data across all these dimensions.

 Business Model

The average business model index for the 161 organizations is 2.70. As shown in Table 5, the use of data in automation processes to improve efficiency is prominent. In contrast, its use for predictive or prescriptive purposes through advanced analytics and AI scores considerably lower, suggesting that companies are still in the exploratory phase of AI use. Hence the need for a robust data model and specialized profiles, such as data scientists and engineers. Regarding coordination, it is evident that few organizations are integrated into ecosystems through external APIs to co-create value, reflecting limited programmability in their data models

Organizational Model

The average index in the organizational model is 2.78, broken down into the six meta-competencies shown in Table 6. A somewhat  outside-in approach still predominates , mainly promoting existing products and services

In contrast, cross-silo collaboration   is the most advanced meta-competency, demonstrating the prevalence of cross-functional teams in matrix structures. However, the adoption of artificial intelligence and  agile methodologies  is still in its early stages. This underscores a limited presence of specialized profiles and a scarce implementation of iterative development practices and  specific agile roles  . While common in IT, their use has not yet spread to other functional areas.

Data Model

The average data model index, at 3.24, is analyzed across five key areas, as shown in Table 7. The  operational backbone  underscores the importance of process digitization. The good average in data security demonstrates the organization’s commitment to protecting against and managing cyberattacks, although the allocation of specific roles can still be improved. Regarding the digital platform (programmability), efforts are being made toward a modular, API-supported IT infrastructure, but the development of  data lakes  and their potential for advanced analytics and AI is still limited. Data governance is a critical area for improvement, highlighting the need for more defined structures and roles to optimize its management, despite some progress in regulatory compliance and privacy due to regulations such as the GDPR

Diagnosis of digital transformation

Of the 161 companies analyzed in the  Data-Driven Index , 46 stand out with a high Data-Driven Index _(DDI)  and considered themselves successful in their digital transformation processes, compared to 45 that acknowledged not having been successful. This dichotomy highlights that the DDI _is  a clear indicator of success in digital transformation processes.

Successful companies are distinguished not only by adopting modular IT architectures that foster flexibility and innovation, but also by their intensive use of prediction and automation in their business strategies. Furthermore, these organizations adopt a highly customer-centric approach, applying methodologies such as  design thinking ,  job-to-be-done processes  , and  customer journey mapping .

On the other hand, the 45 least successful companies presented Idd _below  the average, showing deficiencies in the adoption of data technologies and the implementation of data-oriented organizational and business strategies.

A comprehensive strategy is needed that effectively harmonizes the technological, business, and organizational dimensions.

Table 8 clearly illustrates how the indices vary depending on the success of digital transformation processes, offering a visual perspective of the correlation between a high Idd _and  a company’s ability to navigate and thrive in the digital age. It is important to note that companies reporting success in their digital transformation exhibit higher indices across all parameters that comprise their business, organizational, and data management models.

The digital transformation portfolio

Recognizing the strategic value of data, many organizations still focus on using it to control the execution of their business model. To effectively embark on digital transformation, it is critical to view data not only as a control tool, but as a key resource for innovation, fully integrating it into the overall strategy.

A good score on the data-driven maturity index   (DDI ₎ presented in this article is a necessary condition for success in digital transformation processes, as shown by the results of the 161 companies analyzed. For this reason, the DDI _becomes  an extremely useful tool for companies that want to define, structure, prioritize, and manage their digital transformation portfolio.

Table 9 shows the matrix for classifying initiatives that make up an organization’s digital transformation portfolio ^11. The portfolio initiatives can be classified according to two dimensions: 1) their contribution to generating new revenue through new business models and 2) the need to integrate new capabilities into the organization.

In the lower left quadrant, we find initiatives that support the existing business model without requiring significantly different capabilities from those the organization already possesses. These initiatives focus on digitalization, seeking more efficient execution of the current business model, and tend to use data defensively to optimize existing processes.

In the upper left quadrant are the innovation pilot tests, which, although they do not require having new capabilities integrated, allow experimentation with new business models through data interactions that include not only automation, but also prediction, coordination or personalization.

In the lower right quadrant, we focus on initiatives that aim to introduce new capabilities. This may involve strengthening the operational backbone; expanding the digital platform; integrating specialized data management talent, such as creating new data science units; or adopting new work methodologies that reflect the meta-competencies necessary for cultural change.

The initiatives in the lower right quadrant are called options, as they are valued as strategic investments that give the organization various transformation options.

The upper right quadrant contains initiatives focused on the industrialization of new business models, which are implemented by scaling up pilot tests using newly acquired capabilities. This phase is crucial for transitioning from experimental ideas to solutions that directly impact the company’s strategy and profitability.

Sáez, from GB Foods, illustrates this process through the COG (Cost of Goods) project, where the objective was to deepen the understanding of the impact of raw materials on the profitability of various products and markets. This executive highlights the importance of effectively integrating data into the  company’s data lake  , emphasizing a strategic approach that prioritizes the quality and analytical relevance of the data over mere accumulation. The key was fully integrating the data into the  data lake .

In the context of managing a digital transformation portfolio, the Idd _provides  guidance on how to prioritize and strategically focus initiatives within each quadrant. This role of the _Idd  as a beacon in digital transformation is crucial for navigating the complex landscape of new technologies and their business applications.

Echavarri, from Banco Sabadell, reflects this dynamic when addressing the challenge of selecting and adapting to emerging technologies that truly generate value for the organization: “I think the big challenge will be all these technological capabilities that will emerge in the coming years, or that are emerging, and seeing how we are able to select them, to filter those that really add value, and to see how we are able to adapt.”

More than simply reflecting the current situation, the Idd _guides  the future evolution of organizations, highlighting critical areas for investment. Therefore, this tool is presented not only as a diagnostic tool, but also as a guide for the continuous strategic innovation and adaptation necessary in digital transformation processes.