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

Start » From big data to smart data with data intelligence: how to manage

27 October 2025

From big data to smart data with data intelligence: how to manage

Automation Big data and smart data Digital transformation Artificial intelligence Digital Toolbox

In today's digital world, the term Data intelligence has become indispensable. Companies are confronted with enormous amounts of data, known as Big Data. The key is to transform this raw data into so-called Smart Data, which enables efficient and well-informed decisions. Data intelligence accompanies organisations in extracting qualitative added value from the mass of data and setting sustainable impulses for processes and innovations.

Data Intelligence: From the big data ocean to a valuable treasure trove of knowledge

Big Data describes vast amounts of data that are generated daily within companies in diverse ways and at high speed. Examples include payment transaction data in finance, sensor data in industrial production, or customer interactions in retail. The challenge lies in filtering only the truly relevant data from this abundance, data that provides a clear picture. This is precisely where data intelligence comes in: it helps to refine the raw material of Big Data and transform it into Smart Data, which represents structured, reliable, and actionable information.

A retail company uses data intelligence to analyse the purchasing behaviour of different customer groups. This enables personalised marketing campaigns that better address customer needs, thereby increasing sales. Similarly, a car manufacturer optimises its production processes with data intelligence: sensors continuously supply data on machine statuses, allowing maintenance to be planned predictively and downtime to be minimised.

In healthcare, data intelligence and smart data are leading to more personalised treatment approaches. Large volumes of patient data from electronic health records, lab results, and wearables are being processed, enabling doctors to make informed decisions more quickly. This significantly reduces costs while simultaneously improving the quality of care.

Practical ways to implement data intelligence

Data intelligence doesn't require magic, but the right approach. The following steps help companies turn big data into smart, usable data:

Data integration: Various sources such as CRM systems, IoT devices or external data services are linked together.
Data cleansing: Inconsistent or incorrect information is sorted out or corrected.
Data Analysis and Algorithms: Patterns are recognised and forecasts are made using statistical models and artificial intelligence.
Visualisation: Key insights are clearly displayed on dashboards.
Data protection and governance: Clear rules ensure that sensitive data is handled responsibly.

For example, an energy supplier uses smart meter data to predict the energy consumption of its customers. With the help of data-intelligent analyses, the company was able to avoid grid bottlenecks and at the same time optimise the integration of renewable energies. In addition, a dynamic price adjustment was created, which both reduced costs and improved customer satisfaction.

A provider in the medical technology sector is now also automating the analysis of large image datasets. The combination of AI and data intelligence significantly reduces the susceptibility to errors in diagnostics and supports treating specialists with contextualised information.

BEST PRACTICE with a client (name withheld due to NDA) The introduction of data intelligence in production led to a significant reduction in unproductive downtime. Real-time data monitoring and continuous optimisation of machine parameters allowed for precise adjustment of maintenance intervals and avoided costly unplanned failures. Additionally, product quality improved through targeted control of critical manufacturing steps.

Data intelligence as the key to sustainable success in the company

The permanent availability of large amounts of data harbours opportunities, but also risks. Data intelligence provides support here by enabling companies to make quick decisions based on verified information. Particularly in sectors such as finance, trade, industry, energy and healthcare, this can increase efficiency and secure a competitive edge.

Clients often report that while they have technical solutions for data collection, they require support with interpretation and practical application. This is precisely where Transruption Coaching comes in. It provides impetus for the correct approach to data intelligence, assists in selecting suitable methods and technologies, and fosters a holistic data strategy that aligns with the business model.

BEST PRACTICE with the client (name withheld due to NDA) In the public sector, data-intelligent analyses have improved the quality of service in resource allocation. This provides authorities with clear decision-making foundations to monitor economic developments and deliver services to citizens more efficiently.

Another example comes from the insurance industry. Here, data intelligence helped with risk assessment and fraud detection. The combination of real-time data and algorithms led to more precise premiums and significantly reduced losses due to insurance fraud.

My analysis

Data intelligence is at the heart of the successful transformation of Big Data into Smart Data. The strength lies in targeted preparation and analysis, which allows companies to make data-driven decisions more efficiently and accurately. Practical examples from industry, healthcare, retail, energy, and public services clearly show that data-intelligent work opens up diverse fields of application. Furthermore, it becomes evident that consulting solutions such as transruptions coaching offer valuable support in sustainably anchoring data strategies and thus securing competitive advantages.