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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

17 July 2026

With data intelligence from big data to smart data

Big data and smart data Digital transformation Industry and Factory 4.0 Artificial intelligence AIROI Step 3: Big Data and Smart Data

Why are so many businesses drowning in floods of data while others gain real competitive advantages from the exact same amounts of information?

The answer lies in a fundamental shift in perspective. With Data Intelligence from Big Data to Smart Data the decisive step is achieved. Companies today collect more information than ever before in the history of business. However, raw data mountains alone do not create added value. Only intelligent processing, filtering and contextualisation transforms unstructured floods of information into actionable insights. This transformation process determines whether organisations use their digital resources profitably or collapse under the weight of unused data volumes.

The difference between quantity and quality in the modern data world

Many managers still confuse data volume with data value. This confusion leads to costly bad decisions. A medium-sized engineering company spent years storing all sensor data from its production facilities. Storage costs rose continuously. Yet valuable insights into maintenance cycles and efficiency potentials remained hidden. Only the systematic analysis and filtering of the relevant parameters enabled predictive maintenance. As a result, unplanned downtimes fell by more than a third.

A logistics company recorded millions of position data points from its vehicle fleet every day. The sheer volume completely overwhelmed existing analysis capabilities. Through intelligent aggregation and pattern recognition, meaningful route optimisations were ultimately created. Fuel consumption decreased noticeably. At the same time, delivery punctuality improved significantly. These examples illustrate the necessity of a targeted data strategy.

In retail, companies collect transaction data, customer movements and inventory levels. Without intelligent linkage, however, these information silos remain worthless. A retail group systematically linked these data streams together for the first time. This resulted in precise demand forecasts for individual branches. Excess stock fell significantly. At the same time, the availability of popular products for customers improved.

From Big Data to Smart Data through structured processes using data intelligence

The transformation process requires clear methodological foundations. First, organisations must define their data goals. Which business questions are to be answered? This questioning determines the necessary data quality and granularity. Without this prior consideration, costly data graveyards without practical use are created.

An energy supplier wanted to improve its customer service. The previous analysis focused exclusively on complaint statistics. Through expanded perspectives, weather data, consumption patterns and social media sentiments suddenly also flowed in. The combined analysis enabled proactive customer outreach for expected problems. Customer satisfaction rose measurably. Particularly remarkable was the reduction in incoming complaints.

In the insurance industry, intelligent data utilisation is revolutionising risk assessment. Previously, premium calculations were based on a few static factors. Modern approaches integrate dynamic behavioural analyses and external data sources. One insurer used anonymised movement profiles for differentiated tariff models. Customers with a proven safe driving style received more attractive terms. This customisation sustainably strengthened customer loyalty.

Best practice with a AIROI customer

An internationally active manufacturing company came to us with a typical challenge. The production facilities were generating several terabytes of sensor data daily. These volumes of data were being stored in full, but hardly analysed. The IT department was struggling with growing storage costs and a lack of overview. At the same time, the management demanded better insights into production efficiency and quality key performance indicators. As part of our support, we jointly developed a multi-stage data strategy. First, we identified the data fields that were actually relevant for decision-making. Out of originally over a thousand recorded parameters, fewer than two hundred proved to be truly business-relevant. The remaining data was henceforth only aggregated or archived with a time delay. Subsequently, we implemented real-time dashboards for production managers and quality managers. These visualised critical KPIs clearly and enabled rapid responses to deviations. The integration of machine learning methods for anomaly detection was particularly effective. The system identified atypical patterns even before obvious errors occurred. The scrap rate fell by around fifteen percent within a few months. At the same time, storage costs were reduced by more than forty percent. This project illustrates how transruptions coaching can support companies in complex transformation projects.

Technological foundations for intelligent data processing

Modern analytics platforms form the technical foundation for data transformation. Traditional relational databases reach their limits when dealing with unstructured information. NoSQL technologies enable the flexible processing of diverse data formats. For the first time, a pharmaceutical company combined research data, patient information and scientific publications in a unified analytics platform [1]. The identification of promising drug candidates was accelerated considerably.

Cloud-based analytics services are democratising access to advanced methods. Medium-sized enterprises are also using tools today that were previously reserved exclusively for large corporations. An automotive supplier implemented cloud-based quality analytics without its own IT infrastructure. The implementation time was reduced to a few weeks instead of many months. At the same time, capital expenditure remained manageable and predictable.

Artificial intelligence and machine learning exponentially enhance analytical capabilities. These technologies recognise complex patterns in high-dimensional data spaces. A telecommunications provider successfully deployed AI-supported churn prediction. The system identified customers at risk of leaving with a high degree of accuracy. Targeted retention measures reached precisely this customer group in good time.

Organisational Prerequisites for Sustainable Data Intelligence

Technology alone does not guarantee success. The human component remains crucial. Data literacy must be developed at all levels of the company. A consumer goods manufacturer invested specifically in the further training of its sales staff. They learned to use analytical insights in customer meetings. Conversion rates subsequently improved noticeably.

Data-driven corporate cultures do not emerge overnight. Leaders must model and support the change. A financial services provider initially established data-based decision-making processes within the executive board. This role model effect radiated to all levels of the company. By now, even operational teams use analytical tools for their work on a daily basis.

The collaboration between specialist departments and IT experts requires new forms of communication. Domain knowledge and technical skills have to flow together. A media company created interdisciplinary data teams with mixed staffing. Editors, marketing experts and data analysts worked together on personalisation projects. The resulting recommendation algorithms significantly outperformed all previous approaches.

Data intelligence from big data to smart data as a continuous improvement process

The transformation towards intelligent data utilisation is not a one-off project. Markets, technologies and customer requirements are constantly evolving. Organisations must adapt their data strategies accordingly. An industrial company systematically reviews its analysis processes on a quarterly basis. Both technical key performance indicators and business benefits are evaluated in the process.

Feedback loops between data users and data providers continuously improve quality. A construction company implemented structured feedback processes for its project managers. They communicated regularly about which analyses were helpful and which information was missing. As a result, the data platform evolved to meet their needs.

External impulses valuably enrich internal perspectives. Industry conferences, trade publications, and expert consulting broaden the horizon [2]. Clients frequently report surprising insights through external guidance. Familiar thought patterns are questioned and new possibilities are recognised.

Best practice with a AIROI customer

A growing e-commerce company approached us with a paradoxical situation. Despite increasing visitor numbers and extensive data collection, conversion rates were steadily declining. Internal analysis had not identified any clear causes. Together, we initiated a structured diagnostic process. First, we thoroughly checked the quality of the collected data. This revealed significant inconsistencies between various tracking systems. The customer journey could not be fully traced. Important touchpoints remained invisible or were counted multiple times. After cleaning up this data foundation, a completely new picture of customer behaviour emerged. The actual drop-off points in the purchasing process became visible for the first time. Surprisingly, the main problem did not lie in the checkout process. Rather, a confusing product categorisation frustrated many potential buyers early on. The subsequent redesign of the navigation was based on concrete behavioural analyses. Within a few weeks, conversion rates improved by more than twenty percent. The client described the collaboration as an eye-opening experience. This example shows how external guidance can break through entrenched perspectives.

Observe ethical and legal frameworks

Intelligent data use requires responsible action. Data protection regulations set important boundaries and create trust [3]. A healthcare provider developed transparent communication formats for its data use. Patients thus understood the added value of data analysis for their own care. The willingness to share data increased measurably.

Algorithmic decision-making systems must be designed to be transparent and fair. A recruitment agency checked its AI-supported pre-selection processes for unconscious patterns of discrimination. Critical biases were identified and corrected in the process. The improved algorithms now delivered more diverse and higher quality candidate suggestions.

Data security not only protects against economic damage. It is also a matter of trust towards customers and business partners. A technology company proactively invested in advanced encryption technologies. These security measures were actively addressed in customer communication. The differentiated security promise became a genuine competitive advantage.

My AIROI Analysis

The transformation with Data Intelligence from Big Data to Smart Data presents companies with complex challenges. Technological tools are now widely available and affordable. The actual bottleneck often lies in strategic alignment and cultural embedding. Many organisations collect data without a clear perspective on its utilisation. Others have powerful analytical tools at their disposal, but only use them superficially.

The successful examples from various industries demonstrate common patterns of success. First, these companies define concrete business objectives for their data initiatives. Then, they focus on truly relevant information rather than maximum data volumes. Finally, they develop the necessary competencies at all levels of the company.

Our guidance in such transformation projects has shown that external impetus can significantly accelerate the process. Established thought patterns are questioned and new perspectives are opened up. At the same time, companies benefit from cross-industry experience and proven methods. Investing in data-driven capabilities pays off in the long run. Those who lay the foundations today secure their competitiveness for the years to come. The path to intelligent data use requires perseverance and continuous learning. Yet the results repeatedly justify this effort.