Data and AI: what are the challenges for businesses in 2025?

In an age where information has become a strategic asset, organizations are facing new challenges. Indeed, the combination of big data and artificial intelligence is a major opportunity for them, provided that they know how to fully exploit their potential. Before measuring the opportunities, it is essential to go back to what Big Data is and the characteristics that make it both a source of wealth and a source of challenges for businesses.

What is big data?

We often tend to confuse Big Data and artificial intelligence. However, even if these two elements complement each other and pursue similar objectives, it is important to distinguish them well.

The term Big Data Designates such impressive volumes of data, and extracted at such speed that traditional analysis tools are not enough to process them effectively. This data comes from everywhere: our searches on the Internet, our activities on social networks, the information collected by our connected objects or even the digital traces left during our purchases.

In its raw state, this information or metadata is of little interest. To get real value from them, they need to be sorted, cleaned, organized, and then analyzed. In short, the value of Big Data only exists if it is combined with another technology that makes it possible to structure this mass of data and to derive useful lessons from it.

The 5 V of Big Data

Big Data refers to the immense volume of data generated every day from very diverse sources. To discuss its strengths, we often review five key characteristics that make it up: volume, value, variety, speed, and veracity of data. Here's how to address these five aspects.

• Volume : it corresponds to the considerable mass of information produced and accumulated continuously. To store and use them, you need infrastructures capable of supporting constant growth.
• Value : it is the most important aspect. Data is only valuable if it can be transformed into useful information. Valuable data makes it possible, for example, to improve the performance of an organization, to optimize customer relationships or to strengthen competitiveness. It is therefore valuable for any organizational Business Intelligence project.
• Variety : not all data is the same. Some are structured (databases, financial transactions), others are unstructured (texts, images, videos, sounds), and others are said to be semi-structured. This diversity of formats and sources makes their processing more complex, but also offers richer and more relevant analysis opportunities. The diversity of this information makes it possible to better understand customer behavior or to anticipate trends.
• Velocity : this is the speed at which data is produced and must be stored and processed by its recipients. Real-time social media posts or instant online searches are good examples. If they are not analyzed quickly, they quickly lose their usefulness. The speed or speed of data processing is therefore a critical factor in the data sector.
• Truthfulness : the quality of the data, i.e. its accuracy and reliability, is fundamental for successful analyses. Indeed, incomplete, false, or biased information is likely to lead to poor decisions. Ensuring the quality and accuracy of data through strict control processes is therefore essential for information to provide real added value.

What is the relationship between data and artificial intelligence?

Data feeds algorithms, and artificial intelligence makes it possible to extract real strategic value from it. Together, these two technologies act in synergy. Data and AI are part of a global data strategy.

AI, and more specifically generative AI, draws all its relevance from the richness and volume of information it receives: the more it is exposed to varied and abundant data, the more its models developed using the Machine learning gain in reliability and refinement. It can be compared to a person who progresses through the accumulation of experiences. Big data therefore plays a central role in providing this “raw material” essential for learning and perfecting algorithms.

But the relationship also works the other way around. The masses of data from big data would lose much of their usefulness if they remained raw and unexploited. This is precisely where artificial intelligence comes in. It processes data, interprets it, and transforms this quantity of information into predictive models. All that remains is to convert these into actionable decisions. In other words, it gives real operational value to data and thus directly to the refinement of organizations' strategies.

In the end, AI and big data reinforce each other. Without AI, data remains under-exploited; without data, AI loses all effectiveness. Their complementarity is therefore the key to their power.

What is the purpose of big data analysis?

Big data analysis, or Big Data Analytics, refers to the set of methods used to Exploiting Huge Volumes of Information and Drawing Useful Lessons. It makes it possible to reveal hidden trends, to highlight relationships between different phenomena and to provide concrete support for decision-making. More and more organizations are using this discipline, whether in finance, marketing, health or industry. Indeed, it encourages innovation and makes it possible to change the way of working.

Among its essential contributions, we can highlight:

• The Ability to Anticipate Trends : instead of reacting after the fact, businesses can predict how a market will evolve. In marketing, for example, the analysis of exchanges on social networks makes it possible to follow the evolution of consumer expectations and to adjust campaigns accordingly. This ability to predict also helps to estimate the potential of a product even before it is marketed.
• Improving internal processes : the study of data makes it possible to identify the weak points of an organization and to target corrective actions. In the industrial sector, sensors installed on machines collect information that is used to predict failures and reduce production stoppages, which translates into increased productivity.
• Reinforced personalization : by analyzing the behaviors and preferences of their customers, companies are in a position to propose offers adapted to each profile, to offer tailor-made services. Large e-commerce platforms, for example, use this data to suggest products that match the tastes of each user, improving the shopping experience and promoting loyalty.
• Safer, fact-based decisions : instead of relying solely on intuition, managers have numerical elements to guide their strategy. Thus, in the banking sector, the real-time analysis of millions of transactions makes it possible to quickly identify suspicious transactions and to strengthen customer security.

Big Data and AI: What are the challenges for businesses?

Most organizations have now recognized that data has become an essential tool for optimizing their performance. However, this opportunity involves meeting several challenges. What are the challenges faced by companies wishing to support their transformation with these technologies? Focus.

Analyzing growing volumes of data over the long term

For businesses, the continuous increase in data volumes thus represents a first major challenge. Indeed, without appropriate storage and processing tools and without a long-term vision, the management of this information can become complex. However, data that is not organized or not interpreted remains unusable. Therefore, they cannot be used to strengthen the company's strategy. That's where AI comes in. It allows the processing and valorization of these masses of information Cumbersome, but potentially so powerful.

Rationalize dispersed data, convert it into predictive models

To fully exploit this potential, businesses need to invest in powerful data management solutions and ensure that their teams are trained to use them. With these precautions, data then becomes a real strategic lever. Again, the intervention of AI is proving to be decisive. Indeed, thanks to machine learning algorithms, Generative AI brings improved predictive analytics capacity : it provides more accurate forecasts and generates more reliable models. It guides managers and directors, with ever greater relevance and foresight, on the decisions to be taken for greater performance.

Transforming these volumes of data into operational decisions for managers

The value of Big Data lies not only in collection or analysis, but in its ability to be converted into concrete decisions. The success of this stage is largely based on corporate culture. Recruiting or training experts in data science, statistics and computer science is an option to successfully take advantage of data.

However, there is another possibility: Outsource these skills by soliciting AI and Data consulting firms. What is the advantage of this choice? Take advantage of the excellent know-how of data scientists who make it possible to translate data analyses into useful insights, concrete actions and strategic decisions.

Implement stable, contextualized and secure solutions for teams

Today, a gap is widening between the concrete expectations of professions and the current discourse on artificial intelligence. The tools put forward in the media do not always reflect the operational needs of businesses. Above all, they are looking for secure solutions that are easy to implement, that really meet the needs of their teams and allow them to obtain a return on investment.

Industrialization of AI and Data: how to generate ROI?

Behind the spectacular announcements of the major players in the Tech sector, the process of industrializing AI and Data represents a challenge for many organizations. For the most part, a central question remains: How can investments in these technologies be transformed into measurable benefits?

To answer this question, it is essential to recall the steps that allow data to gain value. In fact, raw data has no value in itself: it is only an observation. It is only by organizing it that it becomes information. Then, knowledge, once understood by a human. Finally, this knowledge takes on its full value when it is shared and used collectively.

So, Obtaining a return on investment from data involves going through all these steps, which requires significant financial, technical and human investments: design of an AI or an AI program, recruitment of experts or outsourcing, acquisition of internal skills, implementation time...

A positive ROI, whether financial (turnover, cost reduction) or qualitative (customer experience, opening new markets, employee engagement) is therefore not built solely on technologies. It is based on a delicate balance to achieve between innovation, governance, skills and corporate culture.

An uncertain environment and fragmented regulation

But beyond the fear of not being able to meet all the conditions to obtain the fruits of its investments, other realities can also hinder development projects using AI and data:

• Tea Lack of regulatory uniformity Regarding the use of artificial intelligence due to a tense geopolitical context, where ethical and compliance requirements are becoming particularly complex. However, this regulatory mosaic creates legal and operational uncertainty for European businesses that is difficult to control.
• The Fear of Increasing Dependence with respect to technology providers and suppliers of models or tools. Such a situation may limit the autonomy of companies and increase their vulnerability.
• The Question of the protection of personal data. It is not only a question of complying with legislation, but also of putting in place implementing mechanisms for managing roles and access rights in order to avoid any deviation.
• One Legitimate Concern About Major Cybersecurity Risks. Hacking or manipulating data can compromise system reliability and directly enhance trust in AI solutions, making vigilance essential.

Bringing Out High-Value Use Cases

The success of an artificial intelligence project associated with Big Data is based on several essential pillars:

• Test and experience In a secure and controlled environment, in order to validate ideas without risk.
• Scaling up rigorously, by building solid and reliable processes.
• Develop a logic of continuous improvement, integrated into the daily life of the teams.
• Support the development of skills, by disseminating a real data culture among employees.
• Recruiting data experts, capable of ensuring the implementation, management and maintenance of solutions.
• Promote acculturation to data at all levels of the company, by strengthening the understanding and mastery of concrete uses. Showing how data directly improves everyone's work makes it possible to create convincing internal relays. These ambassadors facilitate a gradual and sustainable transformation of practices, in favor of the adoption of these technologies within various professions.

Do you need data advice for your business?

Faced with the increasing complexity of Data and AI projects, companies need partners capable of combining technological expertise, operational pragmatism and human support. Eulidia offers a results-oriented approach to help your company face the challenges of these technologies. Our consulting firm, expert in Data & AI transformation, thus makes your data a real driver of competitiveness.

With Eulidia, make your data a growth driver

Chez Eulidia, we support companies and their teams to transform their data into a growth accelerator thanks to AI. Our mission:

• Boost your competitiveness : by making full use of all your data, you convert it into an innovation engine. By putting our technological expertise and our know-how in performance management at your service, we help you anticipate trends and make informed decisions.
• Remove internal barriers : our flexible and multi-technological cloud offers you an ideal environment to centralize and harmonize your information sources. Your teams thus have access to a clear and shared vision of the data.
• Increase flexibility and efficiency : our approach simplifies the use of data, optimizes its use and guarantees sustainable cost control, for increased profitability.

With Eulidia, your data ceases to be a technical constraint and becomes a resource that generates performance and opportunities.