Technology Strategy and Innovation

5 Myths and Realities of the Data-Driven Enterprise

Article written by Martin Bourgeois
Director, Tech Strategy

In today’s world, being “data-driven” implies that an organization’s strategic decisions, business objectives, and tactical plans are based on the information generated by its own data. Gartner recently hosted a webinar exploring the potential of this approach within corporate strategies. Two major findings emerged: the transformation of data into an exponential strategic asset and a positive correlation with business outcomes. Despite these advantages, few companies actually declare themselves truly data-driven in 2023. This article debunks five persistent misconceptions among business leaders regarding the data-driven approach.

Myth #1: Technological debt must be considered

Myth: The technological barrier, an insurmountable wall? In client interviews, the concept of technological deficit is often cited as the main obstacle to transitioning to a data-driven business. After over two decades of evolution in data valuation, the observation is clear: the main barrier lies at a different level.

Reality: A recent study of 1,000 companies and organizations reveals that only 24% declared themselves data-driven despite their efforts, and 79% identified cultural obstacles as the main hindrance to transformation. Among the cited cultural elements are stakeholder management, colleague receptiveness, organizational change management, roles, and responsibilities. Therefore, consideration for human capital must be at the core of any such transformation. The evolution of organizational culture requires a more substantial investment in time and effort than technological investment.

Myth #2: It’s a complex and lengthy process

Myth: A long and arduous journey? Some leaders perceive the data valuation approach as a laborious path. They claim not to have the time to dedicate to such a long-term initiative. However, the reality is that it is more of an evolution in thinking and operating than a complex process.

Reality: Our experience reveals that the most significant successes in valuation have started with small pilot projects. Such initiatives generate positive momentum, facilitating adoption and interest, gradually developing a larger and cross-functional approach. We recommend embracing agility and piloting to create enthusiasm and progressive interest within the organization.

Myth #3: It’s a project for IT

Myth: For some business leaders, data valuation is an approach that must be led by IT. This is a widespread but incorrect idea.

Reality: Developing a data-driven organization is an approach that must involve all stakeholders and resources of the company, necessitating many changes. It is primarily a shift in the mindset of leaders and the culture of the company, placing data and the information it provides at the center of strategy, processes, and tactics.

Myth #4: A large amount of data is required

Myth: The more data, the better the approach? Some believe that to develop a data valuation practice, massive amounts of data are required. This perception worries many leaders, especially in medium-sized enterprises. The reality needs to be nuanced.

Reality: Our experience shows that it is more important to gather a small set of high-quality data ready for exploitation than to create a larger set of often unstructured data. Smaller data sets are often easier to handle and manage, allowing users to interpret them more quickly and take action.

Myth #5: An army of specialists is required

Myth: To advance the practice of data valuation in business, one needs programmers and data architects. While these roles are indeed valuable for any mature company, a data-driven company must first emphasize operational roles. Emerging technologies now address many technical deficiencies, allowing small and medium-sized enterprises to grow rapidly.

Reality: Data valuation must primarily pass through business processes and data organization. To succeed, it is essential to ensure the quality of the produced data. Internal experts who understand processes and interpret what the data means are required to determine data quality. Increasingly, technological solutions exist that enable data orchestration and transformation without requiring advanced data processing expertise. This does not necessarily mean that the organization does not need scientists and architects eventually for greater complexity and sophistication in data valuation but emphasizes the need for business experts to create, analyze, and interpret fundamental data before delving into more complex analyses requiring advanced data valuation skills.

An organizational stance Recent technological advancements make the business development landscape both exciting and daunting. On one hand, technological costs to implement a data strategy have never been more attractive for companies of all sizes, from startups to multinationals. Additionally, innovations in data transformation and support, especially with generative AI, now accelerate the analysis and interpretation of results. On the other hand, the level of confusion regarding the implementation of a data valuation approach remains relatively high.

Analyzing the five myths discussed earlier allows us to conclude that becoming a data-driven company is primarily a cultural transformation project, and its success requires the involvement of all stakeholders, centered around people. Being data-driven is, in fact, the development of a posture in decision-making processes leading to delivering its strategy. Therefore, be prepared to build this approach with a collaborative and continuously innovative mindset.

Talsom is your ideal partner to implement a data-driven organizational orientation. Our multidisciplinary and customized approach will help you establish a relevant and innovative strategy aligned with your vision while ensuring that human capital remains at the core of your transformation.

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Published on 08.01.2024