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

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

Start » Unleashing Data Intelligence: KIROI 3 – From Big Data to Smart Data

29 March 2025

Unleashing Data Intelligence: KIROI 3 – From Big Data to Smart Data

Digital Leadership Big data and smart data Digital transformation HR work and Teams 4.0 Industry and Factory 4.0 Artificial intelligence

Experience data intelligence: from abundance to added value

Data intelligence, often referred to as the engine of digital transformation, is no longer just a buzzword, but a crucial lever for companies looking to actively manage their data. Many customers ask us how a flood of information can be turned into clear recommendations for action and how the full potential of big data can be unlocked. Too often, knowledge remains locked away in isolated databases because the bridge to implementation is missing, yet data intelligence offers the right framework to specifically generate value from huge datasets.

What does data intelligence achieve in various industries?

Whether retail, healthcare, manufacturing, or technology – data intelligence is a cross-sector topic. This is because it helps to recognise patterns, optimise processes, and cater to individual customer wishes. For example, retailers benefit from combining sales figures, weather data, and customer habits to strategically reorder inventory and avoid shortages [1]. In healthcare, it is possible to accelerate diagnoses, individualise care pathways, and adjust treatment methods based on data [2][9]. In industrial manufacturing, machine statuses can be monitored in real-time, minimising downtime and reducing maintenance costs [4].

BEST PRACTICE with one customer (name hidden due to NDA contract) A technology startup wanted to understand how users actually use their product. Together, we used data intelligence to systematically evaluate user behaviour. This enabled the company to specifically adapt internal training offerings, respond to support requests more quickly, and iteratively develop the product further. Customer satisfaction increased noticeably because the adaptations were based on real needs.

BEST PRACTICE with one customer (name hidden due to NDA contract) A retail group implemented data-intelligent systems to revolutionise inventory management. By analysing historical sales data, external market trends, and weather forecasts, orders could be calculated with greater precision. The result: significantly reduced storage costs and a noticeably higher availability of popular products.

BEST PRACTICE with one customer (name hidden due to NDA contract) In the healthcare sector, we supported a clinic in implementing image analysis software. By intelligently linking X-ray images with electronic patient records, the time to diagnosis was significantly reduced. At the same time, error rates decreased because the software reliably detected deviations and specifically alerted doctors to abnormalities.

Data intelligence means transparency and decisiveness

Companies often face the challenge of streamlining their existing data landscapes. However, those who know which data lies where, how up-to-date it is, and how it can be used effectively gain a clear competitive advantage. Data intelligence requires not only technical understanding but also a cultural shift, as the willingness to truly leverage insights is often the sticking point[1][3].

Practical tips for getting started with data intelligence

How do you get started? A first step is to record and categorise your own data sources – this creates transparency about what is actually available. Tools for data discovery and data cataloguing support this. The next step is integration: linking data from different systems provides new insights. This can mean combining customer data from CRM with transaction data or even social media analyses, for example, to identify churn early on[4].

Another important point is the automated evaluation using machine learning methods. Algorithms recognise patterns, classify emails, forecast sales or identify quality problems in production processes – all of this is achieved more quickly and reliably than would be possible manually [6]. Those who regularly check and monitor data quality lay the foundation for robust analyses.

And last but not least: further training and conscious knowledge sharing ensure that the findings are actually implemented in day-to-day business operations. Clients often report that the involvement of all departments, from IT and controlling to the specialist departments, is crucial for the success of data intelligence projects.

Data intelligence in transruption coaching

Many organisations face the question of how to successfully manage transformation projects, particularly when it comes to data intelligence. Through transruptions coaching, we support companies as sparring partners and provide impetus on how to achieve change. Together, we analyse initial situations, define clear goals, and design processes that anchor data-driven decisions within the company culture.

This involves reflecting not only on technical implementation steps but also on change management aspects. This is because it is often the human factor that determines success or failure. Through iterative learning cycles, regular retrospectives, and practical methodologies, ideas are transformed into tangible actions – and data into genuine knowledge.

My analysis

Data intelligence has the potential to elevate companies to a new level. Those who systematically use data gain efficiency and set the course for sustainable success. However, the path to achieving this requires planning, the courage to change, and the willingness to put insights into action. Companies that rely on data intelligence unlock new opportunities – and thus remain permanently capable of action in a dynamic economic world.