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Big Data and AI: From Accumulated Data to Strategic Decisions
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Investment in data infrastructure has been steady over the past decade. Companies have hired talent, deployed sophisticated platforms, and amassed vast amounts of data. However, the sheer volume of investment does not guarantee success. Today, the key issue is not to keep accumulating more and more data, but to transform that data capital into real business value.
There is a clear gap between the available data and measurable impact. According to a global report based on a survey of 3,200 business and technology leaders from 24 countries, the challenge is no longer to prove that AI works, but to scale it up: to turn pilot projects into actual processes integrated into daily operations.
From Exploration to IndustrializationMost organizations have already completed the exploration phase. They have launched pilot projects, tested tools, and gained insights. But the next step—scaling these initiatives into production—remains the bottleneck: only one in four organizations has managed to bring at least 40% of its AI initiatives into production, although more than half are confident they will achieve this in the short term.
In Spain, this transition is particularly significant. Eighty-five percent of Spanish companies plan to increase their investment in AI over the next year, and nearly a third expect increases of more than 20%. The investment ambition is clear. Now, that investment needs to translate into faster, more accurate, and better-informed decisions. To achieve this, organizations need providers and platforms that can support them in making the leap from pilot projects to actual production, with solutions that scale seamlessly.
Data as a strategic asset, not a technical oneFurthermore, data management is no longer solely a technical responsibility. It is a business decision. The organizations that have made the most progress in integrating big data and AI are not necessarily those with the most data; rather, they are those with an architecture that allows them to access, interpret, and use that data quickly and efficiently.
A global report based on surveys of more than 2 , 500 C-suite executives across 34 markets confirms this: organizations that are leaders in AI are 2.5 times more likely to achieve revenue growth of over 10% and 3.6 times more likely to operate with margins exceeding 15%. The difference lies not in the technology used, but in how well the data strategy and business strategy are aligned.
This requires a review not only of the tools, but also of internal processes, governance models, and the organizational culture surrounding data.
The challenges hindering the use of dataTypically, there are three obstacles that limit organizations’ ability to derive value from their data:
- Latency and performance issues in legacy systems. Fragmented data access and lengthy queries prevent real-time responses. This could lead to decisions based on incomplete information. Modernizing that infrastructure—or integrating it with more agile analytics layers—is a decision that cannot be put off.
- Talent. Building and maintaining a robust data architecture requires specialized professionals who are in short supply and in high demand. Organizations that manage to shorten the cycle between data capture and its use in production are better positioned to respond to market changes. Here, augmented analytics platforms and low-code environments are reducing that reliance on scarce technical talent.
- Governance. Seventy percent of organizations in Spain express high or very high concern regarding the use of their proprietary data. Responsible data management, data traceability, and regulatory compliance are not mere bureaucratic hurdles: they are prerequisites for AI to function reliably and at scale. Companies that view governance as a competitive advantage—rather than a cost—will be better positioned to earn the trust of customers and regulators.
Most of the analytics systems used by organizations today were designed to look back: what we sold, what went wrong, what happened… Integrating big data with artificial intelligence models enables the leap toward predictive and prescriptive analytics.
This makes it possible to anticipate future events and choose the best course of action. Departments such as operations, finance, marketing, and supply chain that have adopted this approach are not only more efficient but can also plan and compete with a competitive edge. In practice, this translates into more accurate demand forecasts, optimized inventory management, personalized customer experiences, and early detection of operational risks.
Data accessibility as a competitive advantageHowever, data analysis has little impact if it reaches only technical teams. According to data from a leading global consulting firm, organizations that enable their employees to access and use data on a regular basis are nearly twice as likely to achieve their data and analytics goals and are 1.5 times more likely to report revenue growth of over 10%. Furthermore, data-driven organizations are 23 times more likely to acquire customers, 6 times more likely to retain them, and 19 times more likely to be profitable.
Augmented analytics tools and self-service environments are making it easier for non-technical users to work with data independently. This reduces reliance on specialized teams and speeds up decision-making cycles. For organizations, the question is no longer whether to democratize access to data, but rather which solutions to use to do so in a secure and scalable way.
Big data and AI: at the heart of the industry’s agendaScaling AI, modernizing data infrastructures, ensuring governance, and democratizing analytics are decisions that organizations must make now. Providers of data platforms, analytics solutions, intelligent automation, and data management are key players in this process. Big Data & AI World, part of Tech Show Madrid 2026, taking place November 4–5 at IFEMA, is the venue where these providers and those seeking solutions come together to do business and accelerate transformation.
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