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

Start » Unleashing data intelligence: Big Data & Smart Data for Decision Makers

4 November 2025

Unleashing data intelligence: Big Data & Smart Data for Decision Makers

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

Data intelligence is a key competency for decision-makers across a wide range of industries today. This involves not only collecting large volumes of data, but above all, strategically utilising this data and transforming it into valuable insights. The transformation of Big Data into Smart Data forms the foundation for many companies to make more efficient and informed decisions.

Why Data Intelligence is Essential for Decision-Makers

Decision-makers across all industries face the challenge of managing enormous volumes of data daily. Big Data describes these immense and diverse volumes, which are also generated at high speed. However, data intelligence means more than this mass – it stands for the targeted extraction and filtering of relevant, high-quality information, so-called Smart Data.

For instance, financial companies can use data-driven intelligent analysis to base portfolio decisions on solid facts rather than guesswork. In logistics, the targeted use of data helps to make supply chains more transparent and to identify bottlenecks early. In healthcare too, personalised therapies are gaining increasing importance through data intelligence by precisely analysing individual patient data.

Thus, it becomes apparent that data intelligence not only considers the volume of data, but above all focuses on its quality and usability.

Data Intelligence: Big Data and Smart Data Working Together

Big Data describes the raw data streams from a variety of sources – from social media and sensors to transaction data. However, this data is often unstructured and comprises many different formats. Smart Data, on the other hand, is the selectively processed information obtained from Big Data to support decision-making.

For example, an industrial company uses sensor data from production to enable predictive maintenance. This is where data intelligence comes into play: from the wealth of sensor information, precisely those data points that announce an impending failure are filtered out. This allows costly downtimes to be avoided.

In marketing, many companies are focusing on Smart Data to precisely segment target audiences and individually tailor campaigns. This helps to reduce wastage and strengthen customer loyalty.

Big data is analysed automatically using artificial intelligence and machine learning to extract smart data from it. This significantly increases efficiency and precision in decision-making.

Best practices from practice

BEST PRACTICE at the customer (name hidden due to NDA contract) A logistics company used data intelligence to extract precise KPIs from vast amounts of data. This allowed delivery times to be predicted more accurately and inventory levels to be managed more effectively. The result was significant cost savings and improved customer satisfaction.

BEST PRACTICE at the customer (name hidden due to NDA contract) A marketing agency implemented data-intelligent systems that analysed customer behaviour in real time. This allowed campaigns to be adjusted flexibly and wastage to be reduced, which noticeably increased sales and consolidated customer loyalty.

BEST PRACTICE at the customer (name hidden due to NDA contract) In the manufacturing industry, data intelligence has made it possible to continuously monitor production parameters and counteract deviations immediately. This has minimised downtime and improved product quality.

Practical tips for using data intelligence

For leaders, data intelligence begins with the selection of relevant data sources. Not every piece of information contributes to success; the targeted integration of data that aligns with business processes is important.

Secondly, companies should use technologies such as AI and machine learning to quickly generate actionable smart data from the flood of data. Automated analyses relieve employees of this burden and increase the speed of decision-making processes.

Third, it is advisable to develop a company-wide data strategy. This ensures consistent data quality and prevents the creation of data silos, which can diminish the value of data intelligence.

Application examples from various industries

In retail, data-driven systems make it possible to identify customer preferences and offer personalised promotions. This increases conversion rates and customer satisfaction.

In finance, analysed smart data helps to better assess risks and make more informed investment decisions.

In the healthcare sector, targeted data analyses are improving diagnostics and enabling individually tailored treatment plans.

My analysis

Data intelligence represents a crucial resource for decision-makers to profitably leverage the immense volumes of data in today's world. The combination of Big Data and the targeted transformation into Smart Data creates an information base that enables agile and well-informed decisions. Examples from various industries show that data-intelligent solutions have diverse applications and provide sustainable benefits.

Decision-makers who systematically engage with data intelligence create better conditions for innovation and competitive advantages. Crucially, this involves not only implementing technologies but also fostering data-driven mindsets and adapting processes accordingly.