AIROI - Artificial Intelligence Return on Invest
The AI strategy for decision-makers and managers

Business excellence for decision-makers & managers by and with Sanjay Sauldie

AIROI - Artificial Intelligence Return on Invest: The AI strategy for decision-makers and managers

Start » With data intelligence from big data to smart data

25 November 2025

With data intelligence from big data to smart data

Automation Big data and smart data Digital transformation Artificial intelligence AIROI Step 3: Big Data and Smart Data

Why do so many companies drown in their own data volumes, while others gain strategic advantages from them?

The answer lies in a fundamental change in perspective. Raw information alone does not create added value. It is only the intelligent processing that makes the difference. With Data Intelligence from Big Data to Smart Data The crucial step has been achieved. Companies collect millions of data points every day. However, this abundance overwhelms many organizations. The art lies in identifying relevant patterns. Only then can actionable insights be generated. In this article, you will learn how to achieve this transformation process.

The challenge: From the data ocean to clear vision

Modern companies generate continuously enormous amounts of information. Sensors in production plants deliver measurement data at a rate of one second per second. Customer interactions leave digital traces across various channels. Employees create documents and communicate electronically. This data flow continues to grow exponentially. However, more information does not automatically mean better decisions. Often, clients report a veritable overload. They don’t know where to begin. The sheer volume literally overwhelms them.

For example, a logistics company collects location data for all vehicles. In addition, it logs delivery times and customer feedback. Weather data and traffic information are also included. All of this information often resides in different systems. Rarely is there any connection between them. This means valuable connections are hidden. The potential remains untapped.

In retail, the picture is similar. Cash register systems record every sale with precision. Customer cards store purchase histories over the years. Online stores track surfing behavior in minute detail. Social media channels provide insights into customer sentiment. But without intelligent analysis, these treasures remain valueless. Transformation requires a systematic approach.

Data intelligence from big data to smart data: The transformation process

The journey from unstructured mass data to actionable insights involves several stages. First, all relevant data sources are collected. Next, quality issues are identified and resolved. After that, integration into a unified platform follows. Only then can the actual analysis begin. Each step requires specific expertise and appropriate technologies.

A mechanical engineering company typically starts with its production data. Temperature patterns, pressure values, and vibration patterns are continuously recorded. This information enables predictive maintenance. Failures can often be predicted. This significantly reduces downtime. Productivity increases measurably.

Insurance companies use similar approaches in risk assessment. Claims are automatically analyzed. Patterns in fraudulent applications can be identified. At the same time, customer service is improved through personalized offers. The data intelligence supports several business areas simultaneously.

Best practice with a AIROI customer

A medium-sized energy provider faced a complex challenge in the area of consumption analysis and customer support. The company had collected enormous amounts of meter data over the years without systematically evaluating it. Customer communication was reactive and little individual. As part of a transruptive coaching project, we supported the project team in strategically reorienting the business. First, we jointly analyzed the existing data sources and identified hidden potential. Subsequently, we developed a roadmap for the stepwise implementation of intelligent analysis tools. The employees received impulses to change their working methods and to utilize the new insights. After about six months, the company was able to offer personalized energy saving tips to its customers. According to internal surveys, customer satisfaction increased significantly. At the same time, the costs for manual data processing dropped significantly. The project impressively demonstrated how the guidance provided by experienced coaches accelerated the transformation process.

Technological Foundations for Intelligent Data Utilisation

Technical infrastructure forms the foundation of any successful data strategy. Cloud platforms enable flexible scaling in response to fluctuating demand. Data lakes store different data formats in a central location. Modern analytics tools extract insights from these repositories. Machine learning algorithms detect patterns that would be overlooked by humans.

In healthcare, the possibilities are particularly impressive. Patient records contain valuable information about disease processes. Imaging methods provide enormous amounts of data daily. Laboratory values and vital parameters complement the picture. Intelligent systems can help to make diagnoses more quickly [1]. This often leads to a noticeable improvement in treatment quality.

Financial institutions are also relying on advanced analytical technologies. Transaction data are monitored in real time. Unusual patterns automatically trigger alarms. Fraud prevention works significantly more efficiently. At the same time, the insights enable better investment recommendations for customers.

Strategic aspects of data transformation

Technology alone is not enough. Cultural change in organizations often proves to be the biggest hurdle. Employees must learn to make decisions based on data. Executives need a fundamental understanding of the possibilities. Silos between departments hinder the free flow of information. Breaking through these barriers requires patient guidance.

For example, a automotive supplier had excellent production data. However, the quality department did not have access to sales information. It was therefore not possible to link complaint patterns to production batches. Only cross-departmental collaboration enabled real improvements. The data intelligence of Big Data to Smart Data requires organizational changes.

In tourism, similar challenges are evident. Booking systems, hotel ratings, and social media feedback provide different perspectives. Marketing and operations often operate separately. An integrated view of the customer journey is often lacking. Transruptive coaching can help overcome these limitations [2].

Data intelligence in practical implementation

Successful implementation requires a structured project approach. First, the team defines concrete business questions. These determine which data is actually needed. Subsequently, the technical preparation takes place. Finally, the findings are integrated into business processes. Each phase presents specific challenges.

A telecommunications provider wanted to improve customer retention. The project team identified relevant early indicators of churn. Usage behavior, complaint history, and contract data were combined. The resulting model detected at-risk customers early on. Targeted retention measures could thus be initiated in a timely manner.

In e-commerce, retailers use similar approaches for product recommendations. Purchase behavior is analyzed and compared with similar profiles. Personalized suggestions measurably increase conversion rates. At the same time, the shopping experience for customers improves. The intelligent use of data creates value for all involved [3].

Best practice with a AIROI customer

A regional retail chain with over thirty stores was looking for ways to optimize its product mix and inventory management. The challenge was to have the right product available at the right time and in the right place. Previous decisions were largely based on the experience of the store managers. In transruptive coaching, we worked together on a data-driven solution for this complex task. First, we consolidated the sales data from all stores in a centralized analysis platform. Next, we identified seasonal patterns and regional differences in purchasing behavior. The team learned to use these insights for ordering decisions. The integration with local event calendars and weather data was particularly valuable. This enabled better anticipation of demand peaks. The inventory levels decreased by a significant percentage on average. At the same time, the availability of goods improved significantly from a customer perspective. The employees reported a new understanding of data-based decision making.

Challenges and approaches

Transformation brings typical obstacles with it. Data quality regularly proves to be an underestimated problem. Missing values, inconsistent formats, and outdated information hinder analysis. Data protection requirements limit the handling of personal data. A shortage of skilled workers makes it difficult to implement ambitious projects.

One pharmaceutical company experienced these challenges in an exemplary manner. Clinical study data came in various formats. Integration required significant manual effort. At the same time, strict regulatory requirements for data protection applied. The solution consisted of a step-by-step approach with clear priorities.

In the public sector, additional complexities arise. Authorities often operate with outdated IT systems. Budget constraints limit investment opportunities. Long procurement processes delay modernization. Nevertheless, intelligent data utilization offers enormous potential for better citizen services.

Success factors for sustainable data transformation

Successful projects share certain characteristics. Executive sponsorship at the highest level secures the necessary resources. Clear governance structures regulate responsibilities and access rights. Agile methodologies enable rapid learning outcomes. Continuous training keeps the team up to date.

One media company demonstrated these success factors impressively. The management visibly and emphatically supported the initiative. A cross-functional team combined various competencies. Regular sprint reviews showed progress transparently. The employees participated in training courses on data analysis.

The topic is also gaining importance in the construction industry. BIM models generate extensive project data during the planning phase. Site sensors provide real-time information on progress. Integrating these sources enables better project control. Delays can be detected and addressed early on.

My AIROI Analysis

The journey from unstructured mass data to actionable insights presents companies with multifaceted challenges. Technological solutions are available and are becoming increasingly powerful. Yet many initiatives fail due to organizational and cultural barriers. The human component regularly proves to be the decisive factor of success. Employees must understand and embrace the change. Leaders need a fundamental understanding of the possibilities and limits. Silos between departments must be actively overcome.

From my experience in numerous consulting projects, certain patterns emerge. Companies that start out too ambitiously often experience disappointment. A focused pilot approach with quick visible results motivates the team more. The data intelligence of big data to smart data requires patience and perseverance. At the same time, the strategic vision must not be lost sight of. The balance between short-term success and long-term transformation is best achieved with experienced guidance.

Transruptions coaching provides valuable support for project teams. The external perspective helps identify blind spots. Methodological insights accelerate the learning process. The combination of technical expertise and change management skills proves particularly effective. Companies that adopt this integrated approach often achieve their goals more quickly and sustainably.