Data usage 9 important data trends

Related Vendors

One Data GmbH

FAULHABER Drive Systems

Wuxi InfiMotion Technology Co., Ltd.

Current trends in the data world go far beyond technical innovations. Below, we take a look at the key developments that will be relevant in the near future—
and provide tips on how companies can directly benefit from these trends.

Dr. Andreas Böhm is the founder and managing director of One Data GmbH.

More and more, companies need to leverage real-time analytics, AI, and effective data governance and monetization to stay competitive. Real-time analytics promote quick action and increase both revenue and profitability. Additionally, artificial intelligence enables personalized offers and enhances economic value. A simplified data landscape and the use of low-code tools strengthen data competency within the company. However, data protection and responsible AI development are crucial for customer trust. Therefore, we will subsequently consider nine important factors that will shape the data strategy in companies.

1. Real-time analytics become standard

Real-time analytics offer the advantage of responding quickly and agilely to market changes and making decisions without delay. According to a study by MIT CISR, companies operating in real-time achieved 62% higher revenue growth and 97% higher profit margins than their slower competitors. This development is particularly relevant in dynamic areas such as finance, trade, and logistics, where quick adjustments can be crucial for success. According to McKinsey, companies employing real-time analytics experience an increase in profitability of 5-6%.

Tip: In industries with frequent, hard-to-predict market changes and dynamics, companies should focus on rapid data streams and consider implementing event-driven architectures to process data continuously.

2. AI and machine learning as driving forces

Artificial Intelligence and Machine Learning (ML) have the potential to sustainably change the entire data world. According to a study by PWC, AI could create an economic value of $15.7 trillion by 2030. Especially exciting: AI can improve decisions, automatically detect anomalies, or even enhance customer retention through personalized offers. A recent study has shown that 89% of executives believe that personalization is crucial for their company's success in the next three years. AI-driven personalization could therefore contribute to significant revenue increases by 2025.

Tip: AI must be part of the data strategy for 2025—for example, for personalized customer interactions or predictive maintenance. Companies should pay particular attention to explainability and fairness when using AI to build trust (see point 8).

3. Data governance and privacy by design

Data protection is not only a legal but also a business requirement. Gartner predicts that by 2025, 75% of the world's population will have their personal data covered by modern privacy regulations. The importance of data protection is highlighted by fines for violations of regulations such as the General Data Protection Regulation (GDPR), which according to Statista, will reach 2.1 billion euros (2.27 billion US dollars) in 2023. Companies that fail to comply with privacy regulations risk fines of up to 4% of their worldwide annual revenue according to GDPR guidelines. Companies that adopt Privacy by Design early avoid penalties and strengthen their customers' trust.

Tip: It is worthwhile to invest in automated governance frameworks now, which monitor and enforce data protection regulations throughout the data flow. Transparent handling of data protection also strengthens customer loyalty.

4. Data monetization becomes a revenue source

Offering data as a service or product opens up new revenue streams—this is particularly evident in the rapidly growing market for data monetization, which, according to current studies, is expected to grow annually by over 20% to approximately 15.5 billion US dollars by 2030. It involves not only the sale of raw data but also value-adding data products such as subscription-based services.

Tip: Companies should now identify the most valuable data within their organization and consider whether products can be developed from it that offer added value to external partners. Data marketplaces offer an exciting opportunity for monetization in the future.

5. Data and data products as core business competence

Data and data products could already become the foundation for business decisions in all departments of a company this year. Especially through low-code and no-code platforms, data becomes accessible to non-technical teams, leading to faster decisions and less dependence on IT departments.

Tip: It will be a central task for CEOs and CIOs to enable the use of low-code tools so that departments can independently develop data solutions. This broadens the anchoring of data competence and makes departments more agile.

6. MLOps and automation

The complexity of data-driven products, especially those with machine learning elements, is increasing. MLOps (Machine Learning Operations) facilitates the management of ML projects and reduces manual effort. Companies that adopt MLOps can deploy models more quickly and continuously monitor their performance.

Tip: To ensure relevance and performance in the long term, companies should automate the lifecycle of their AI models.

7. Multi-cloud and interoperability

According to IDC studies, more than 80% of companies are already pursuing a multi-cloud strategy. The reason: flexibility, cost optimization, and the avoidance of dependencies on individual providers. Interoperability between different cloud providers is increasingly becoming a must.

Tip: A data architecture should be designed to function independently of specific cloud platforms. This minimizes dependencies and enables a more flexible and resilient data strategy.

8. Ethical AI and responsible development

The importance of ethical principles in AI development is increasing. Studies show that 62% of consumers trust companies more when their AI systems are transparent and free of bias. Companies that establish responsible AI frameworks can strengthen customer trust in the long term.

Tip: Ethical principles should be integrated at every stage of the data strategy, and explainable AI methods should be used to increase transparency and trust in your company.

9. Balance complexity and create clarity

The data landscape is becoming increasingly complex. The trend is therefore towards simplified processes and clear, easily understandable metrics that can be understood and used across all disciplines. Companies that systematically simplify their data landscape save time and costs and increase efficiency (according to Gartner, Top Trends in Data and Analytics 2024).

Tip: Every data initiative needs a clear communication strategy that must be developed in advance. Less is often more—clear, meaningful metrics facilitate collaboration and foster a data-driven culture.

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