According to McKinsey, data-driven organizations are 23 times more likely to acquire new customers, 6 times more likely to retain them, and 19 times more likely to be profitable than organizations that do not adopt a data-driven approach. Yet, as a previous analysis highlights, truly data-driven companies remain rare. Sometimes this stems from the complexity of the underlying technical ecosystem, but more often, the root cause is cultural and/or organizational.
Every transformation journey begins with a strategy, which, in this case, is a data strategy. According to various sources, about one in two (large) companies has already formalized a data strategy. However, research from MIT Technology Review and Databricks reveals that only 13% of these companies have successfully translated their data strategies into significant and lasting business results.
But what exactly is a data strategy? It is a strategic plan or long-term vision, that defines how an organization intends to collect, manage, analyze, and utilize its data to achieve business goals. The key element is the connection between data and business: the data strategy outlines how the organization plans to leverage its data to implement business strategies and achieve its objectives. Alignment with the business strategy is the starting point for any successful initiative.
Developing and implementing a data strategy offers significant advantages by establishing centralized oversight and setting clear objectives for the entire data-driven transformation process. This approach reduces the project fragmentation that still characterizes many companies.
A data strategy defines processes and equips users with the tools and knowledge needed to make data-driven decisions, enhancing the organization’s ability to respond quickly to market dynamics and anticipate major trends.
A well-planned data strategy helps optimize business processes, reducing operational costs and boosting efficiency through automation and improved decision-making.
By establishing clear policies for data management, companies ensure regulatory compliance, prevent penalties and mitigate risks related to data breaches.
A well-defined data strategy enables organizations to adopt advanced technologies, such as artificial intelligence (AI), which requires high-quality data, as well as modern architectures and infrastructures.
Every organization should define its unique data strategy depending on its industry, market context, available technologies, and objectives. While specific needs may vary, the building blocks of a solid data strategy are universally shared.
As mentioned earlier, a data strategy must align with business objectives. The first critical step involves identifying the correct strategic direction, which requires a clear and thorough understanding of organizational goals. Without this awareness, there is a risk of focusing on and investing in non-essential or low-value projects.
Engaging stakeholders and business line leaders is essential to identify the specific needs of each department and understand how data can add value not only to the overarching vision but also to individual organizational units. Following the analysis phase, the actual strategy must be designed, detailing how data will be used to achieve tangible results and support organizational growth.
To define an effective data strategy, it is crucial to understand the current state of the organization’s data ecosystem. This requires a comprehensive analysis to identify strengths and areas for improvement. Common issues include silos, which reflect organizational divisions and hinder data democratization. Key topics to address include:
This analysis helps assess current analytical capabilities, both in terms of available tools and adoption levels, as well as the organization’s data literacy—i.e., the ability to use data to meet the specific needs of professionals and departments.
Managing and analyzing data requires skilled professionals in various areas of data science, such as data analysts, data scientists, and data engineers. Having specific and complementary expertise in-house ensures the organization can handle data complexities, guaranteeing both the integrity and security of information and the ability to transform it into tangible value.
Establishing a data governance framework is essential to ensure data is managed effectively, responsibly, securely, and in compliance with regulations. This involves defining policies and procedures for data collection, storage, usage, analysis, and sharing. These policies must then be implemented across all tools supporting the data strategy.
Roles and responsibilities, such as data stewards and data governance committees, are vital to ensuring data quality, security, and compliance. Effective governance helps maintain high data quality and protects sensitive information.
Data architecture refers to the structure and organization of an organization’s data. It encompasses storage platform selection, integration of data from different sources, and the design of systems enabling easy access and analysis. A good data architecture must be scalable and flexible to adapt to future needs and increased data volumes, while remaining accessible to all users, from employees to applications.
The choice or development of tools to support the data strategy depends on specific objectives, data complexity, existing competencies, and budget. Organizations must prioritize scalable, modern, and innovative solutions that ensure robust data management while delivering concrete value.
Fostering a data culture is crucial for the long-term success of a data strategy. This involves educating staff on the importance of using data for informed decision-making, promoting trust in data, and encouraging a data-driven approach across all organizational levels. Addressing the challenge of data democratization is key in this context.
To generate value, the strategy must be implemented. This requires a detailed action plan starting with well-defined objectives, operational steps, and measurement criteria (including KPIs). The roadmap should organize key milestones and prioritize actions while accounting for time constraints and resource needs. Progress must be consistently monitored to allow for optimizations as necessary.