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Data Strategy: what it is, how to apply it, and why
In a world increasingly driven by data but dominated by great uncertainty, following a well-defined data strategy is critical for any company, regardless of size or the industry in which it operates.
In times marked by major changes and significant volatility, data strategy serves as a compass that helps us navigate uncharted waters, enabling companies to find their bearings and seize important opportunities that might otherwise slip away.
In this article, we’ll begin by clarifying what a data strategy is, what defines it, and what the tangible benefits are of a robust data strategy—one that is likely to be decisive for business growth.
What is Data Strategy
According to Gartner’s definition, “data strategy” simply refers to a highly dynamic process used to support the acquisition, organization, analysis, and delivery of data to further business objectives.
More generally, we can say that adopting a data strategy reflects a company’s commitment to transforming data into a key driver of innovation, improved decision-making, and the achievement of business objectives.
All of this can only take place within a well-structured framework that encompasses data collection, analysis, data management, data governance, and, finally, data utilization. All of this is aimed at achieving clear and well-defined long-term strategic objectives, while fully respecting values such as privacy, transparency, security, and ethics.
There are at least three compelling reasons why, now more than ever, companies should adopt a robust data strategy.
First and foremost, increasingly stringent national and European regulations promote responsible and compliant data management. Without a structured and informed approach to data management, companies lacking a clear data strategy risk incurring financial penalties and legal repercussions.
Second, the exponential increase in the volume and variety of data poses new challenges. The enormous amount of data produced requires appropriate strategies for its storage, processing, and analysis; without these, companies would effectively be unable to fully leverage the informational potential at their disposal.
Finally, the importance of data-driven insights for informed decision-making cannot be underestimated. In a context where business decisions must be made quickly and effectively, having a data strategy that enables companies to understand and make the best use of their information assets becomes crucial for maintaining a competitive advantage.
What are the benefits of Data Strategy
As we have already mentioned, in the era of big data, a clear, company-wide data strategy is essential for collecting, analyzing, and leveraging data to make informed decisions. This strategic approach allows companies to identify operational gaps and take corrective action to improve the efficiency of their processes, enhance the customer experience, and find new drivers to support business growth.
Furthermore, a solid Data Strategy is essential for identifying potential risks and detecting any breaches in data management. It is crucial for a company to have a clear understanding of what data is collected, where it comes from, who can access it, how it is stored, how it is used, and for what purposes—all in accordance with current regulations.
Implementing a robust data strategy provides a decisive competitive advantage for companies, which can, for example, understand their customers’ behavioral patterns and preferences and thus gain the essential insights needed to personalize products or services and increase customer satisfaction.
To summarize, the benefits of implementing a Data Strategy are numerous and can be summarized as follows:
- Efficiency
- productivity
- cost reduction
- risk management
- business opportunities
Efficiency increases when companies, armed with the right data, identify and optimize slow or inefficient processes.
Productivity improves because employees, having access to relevant information on customer preferences, market trends, production data, and logistics flows, are better equipped to do their jobs.
Furthermore, an effective Data Strategy can lead to significant cost savings by identifying areas of waste or underutilization of resources and taking appropriate action.
Another key benefit relates to risk management and compliance. In this regard, a well-defined data strategy can help mitigate the risks associated with data breaches, regulatory non-compliance, and reputational damage.
Finally, access to real-time data insights enables companies to continually discover new ways to improve products or services, identify emerging market trends and customer preferences before competitors do, thereby ensuring innovation, scalability, and revenue growth.
The Stages of a Data Strategy
Before we delve into defining a roadmap to guide the creation of a data strategy, it’s important to keep in mind the key elements that make it up.
Generally, there are seven core elements that form the foundation of a data strategy:
- data acquisition
- governance
- data quality
- access
- use
- extraction and reporting
- analysis
Many of these elements can be reconfigured to suit the organizational context and the company’s level of maturity, but all must be present in some form.
By integrating these components into its strategy, the company will have access to data of sufficient quality to advance its strategic objectives.
In general, we can say that to develop a data strategy suited to the era of big data, companies must first establish their strategic objectives, assess the current landscape, and define their governance framework. Next comes the phase of designing the infrastructure and implementing the data platform, upon which analytical capabilities are then built, while strengthening security and privacy measures. All of this must be done without neglecting either employee training and the creation of a dedicated team, or the subsequent phases of assessment, monitoring, and verification.
But let’s take a closer look at how to develop a robust data strategy by defining a roadmap with the key steps
Vision
The vision outlines the future goal. It provides a clear and compelling picture of how data will be leveraged to drive innovation, improve decision-making, and achieve business objectives. The vision defines the direction of the data strategy, inspiring stakeholders and aligning their efforts toward a common goal. It is important to align data usage with organizational objectives, drawing on a 2- to 3-year vision to determine the role of data in the bigger picture. At this stage, it is essential to clearly articulate the “why” behind the strategy, which may be driven by the need to increase business visibility, monetize data, create new products, or improve efficiency in data usage.
Stakeholders
It’s not enough to have a clear vision. It’s also important to identify the key stakeholders involved in data investments. This includes not only internal management but also business functions and, of course, customers. Each stakeholder must be considered from their own unique perspective on the organizational data initiative. A successful roadmap requires alignment on this front as well.
Audit
Before choosing an infrastructure or data platform, it is important to assess the capabilities and effectiveness of the existing technology stack, including the data architecture and storage technology. Following the auditing phase, you can evaluate the adoption of the right architecture and tools, selecting those that align with your vision. All elements must be taken into account, from data warehouses to connectors, integration tools to reporting platforms. It is important to choose the right platform to ensure an uninterrupted flow of data between systems, while also evaluating aspects such as usability, security, compatibility, and scalability.
KPI
KPIs are essential for evaluating the effectiveness of data management initiatives and must align with business objectives. Organizations must monitor data quality, integration, storage, and delivery by establishing relevant KPIs.
Data strategy KPIs are key performance indicators that measure the impact and results of your data strategy. These KPIs must be SMART (specific, measurable, achievable, relevant, and time-bound) and aligned with business objectives. Examples of data strategy KPIs include business intelligence KPIs such as customer retention or churn; data governance KPIs such as data quality scores or data maturity levels; and data analytics KPIs such as insights generated or actions taken…
Governance
It is necessary to define a comprehensive data governance policy that covers key aspects such as data quality standards, verifiability, transparency, management, standardization, privacy regulations, and security protocols. Establish clear accountability for decisions and actions related to data.
Including a data governance framework is a key component of the roadmap, as it provides a high-level view of initial configurations, row-level security, dataset certification, and regulatory requirements for sensitive data.
Organization
Among the activities required to define the Data Strategy framework is the design of a team structure that facilitates cross-functional collaboration and establishes clear roles and responsibilities, improving collaboration and knowledge sharing, and creating specialized data teams capable of executing short cycles with achievable milestones.
This phase also includes analyzing the skills needed to execute the strategy based on the selected objectives and tools, assessing whether these roles already exist within the organization or whether they need to be created.
Implementation
Only at this point can an implementation plan be defined that includes design, data processing, use case development, testing/validation, deployment, monitoring, and updates, with realistic timelines and budget allocations for each phase of the transformation process.
Monitoring, Review and Update
Adopting a data strategy is an ongoing process that requires constant review and updating of the roadmap to ensure it remains effectively aligned with business requirements and objectives. This strategy is fully in line with the philosophy of “continuous improvement” demanded by today’s technological and market landscape. Only in this way can the strategy remain relevant, efficient, and flexible.
How to implement the Data Strategy
We have seen that the development of an effective data strategy must be based on a clear understanding and definition of objectives, aligning them with the company’s overall strategy.
Only by keeping the data strategy and strategic vision aligned can a tangible and measurable return on investment be guaranteed.
Accurately identifying data sources is the next crucial step. Data sources, which may include customer, sales, and financial data, must be cataloged and evaluated for quality and relevance. This process enables a holistic approach to data management, ensuring that the data is accurate, complete, consistent, and free of bias.
Equally crucial is the establishment of clear and detailed data governance policies that ensure data quality, security, and compliance. These policies should precisely outline the roles and responsibilities of stakeholders, establish quality standards, and define processes for data management and access.
Of course, you also need the right tools, as well as the right level of engagement from everyone involved—both in terms of training and in terms of understanding the current strategic direction.
Developing data analysis capabilities tailored to a company’s specific needs requires selecting appropriate and scalable analytical tools and technologies that enable the integration of data from multiple sources into a single repository and are built on a suitable and secure architecture.
