"`html
Data intelligence is no longer an add-on, but the centre of entrepreneurial excellence. Companies that draw smart, useful insights from huge mountains of data increase their innovative strength and position themselves at the forefront of the competition. Today's decision-makers are asking themselves: How can the step from big data to real, targeted business impulses be achieved - and what role does data intelligence play in this? At the heart of this is the ability to intelligently analyse large and complex amounts of data and use them to make reliable decisions. Those who unleash data intelligence not only utilise information flows passively, but also actively shape processes, markets and innovations [5][11].
Big data as a raw material for data intelligence
Let's start at the beginning: companies today collect a huge amount of data. Sensors measure machine statuses, digital channels continuously supply customer data and internal systems constantly store process data. This flood of data - often referred to as big data - is characterised by its large volume, high speed and enormous variety [3]. However, such raw data alone rarely brings value. Only through clever analysis, targeted summarisation and process-specific filtering can big data be turned into what we call smart data, which in turn - through systematic integration into management - can be turned into genuine data intelligence [2][5].
Classic examples include manufacturing, logistics and finance: In mechanical engineering, sensors monitor the status of production facilities in real time, while logistics companies network freight data globally. In the financial sector, portfolios, market movements and customer needs are constantly changing. Big data provides the input here, but only smart data turns it into solution-orientated findings. This enables companies to identify bottlenecks at an early stage, optimise supply chains, assess market risks and react to changes in a targeted manner [11].
From a mountain of raw data to intelligent decisions
Big data is like a quarry: the mass is enormous, but only targeted processing brings value. Smart data is the result of intelligent filtering, quality checks and structuring using data analytics and artificial intelligence. This provides companies with data that can be used directly for decision-making [1][2]. Decision-makers benefit because precisely the data that is relevant to their issue is extracted from the flood.
Data intelligence means analysing and evaluating this information in real time and translating it into comprehensible recommendations for action. For example, a marketing agency can continuously measure customer behaviour and automatically adapt campaigns - wastage is minimised, the customer journey and target group approach become more precise [7][11]. In industry, smart algorithms trigger maintenance warnings before machines actually break down. This saves time and costs and increases plant availability [9]. In the financial sector, portfolio positions are dynamically managed on the basis of current market data and AI forecasts.
Data intelligence for more reliable decision-making
In coaching and in projects, decision-makers are often asked how they can minimise uncertainties in data processing. Many companies report a flood of information, but at the same time uncertainty as to which data is actually business-critical. Data intelligence is the key to creating clarity and making reliable decisions [11].
Data intelligence approaches help to filter big data according to business requirements: Only the relevant data is analysed, evaluated and converted into clear, meaningful key performance indicators (KPIs). Dashboards and analysis tools visualise results so that managers can see at a glance how their processes, customers and markets are performing. This creates agility and decision-making security, because the basis is not assumptions but reliable data.
How do you create real value from data intelligence?
The interplay of big data, smart data and targeted analyses ensures sustainable business success. Taking the example of an international logistics company: with the help of data intelligence, delivery times and stock levels were continuously forecast and optimised. This reduced costs, shortened delivery routes and significantly increased customer satisfaction. The database provided smart filtered key figures that were continuously updated and presented transparently on dashboards.
BEST PRACTICE at the customer (name hidden due to NDA contract) The logistics company developed a data intelligence tool that extracted relevant KPIs such as warehouse throughput time, delivery delays and transport costs per route from existing systems. The analyses were carried out in real time. This made it possible to identify areas of focus and adapt processes immediately. The result was fewer bottlenecks, shorter delivery times and better internal and external collaboration.
Another example from production: a machine manufacturer introduced a predictive maintenance system that used sensor data to signal impending failures at an early stage. Employees received targeted recommendations for action, which significantly reduced downtimes and increased productivity. Data intelligence can also be used in marketing to address target groups more precisely, as customer behaviour is continuously analysed and evaluated. The increase in sales is measurable because wastage is minimised and the conversion rate is increased.
BEST PRACTICE at the customer (name hidden due to NDA contract) A large marketing agency relied on data intelligence to record and evaluate customer behaviour in real time. The analysis of interactions led to personalised content recommendations and dynamically adapted campaigns. The result: customer loyalty increased, advertising costs fell and sales increased demonstrably. The data intelligence platform became the central control instrument for the entire customer journey.
A third example from the financial sector: an asset manager used an AI-supported analysis platform to continuously adapt portfolio strategies to market changes. The platform filtered relevant market and customer data from extensive sources, filtered out noise and fed recommendations for action into the portfolio management system. Performance became more transparent and risk more controllable.
Unleashing data intelligence: Impulses for your practice
The implementation of data intelligence is most successful when companies define clear goals from the outset. Which decisions should be supported by data? Which questions need to be answered and which processes need to be optimised? Transruption coaching accompanies decision-makers on the path to a data-intelligent organisation. Use cases are developed together, data sources identified and analysis processes established.
The first step is a data-driven location analysis. Where does your company stand when it comes to data and analyses? Which systems are in use, which data is already being utilised and where is there potential? In coaching, we provide impetus to shift the focus from pure data collection to intelligent utilisation. People are at the centre of this: only when teams understand how to work with data intelligence can real change occur.
Transruption coaching also means using methods such as design thinking, agile processes and AI technologies in a targeted manner. This results in data strategies that do not bypass everyday working life but provide concrete answers. Consultancy creates space for new solutions, makes it easier to deal with resistance and supports change management.
Three specific tips for getting started
Start with small, measurable projects that quickly make an impact. This is how teams gain experience and confidence in data intelligence. Look for a use case that directly contributes to business development, such as the reduction of returns, the optimisation of delivery routes or the individualisation of customer offers [4]. Always start with a clear question.
Rely on modern analysis and visualisation tools. Dashboards provide a compact overview, while AI-based analyses make trends and anomalies visible. This allows you to switch from pure data collection to intelligent control. Build transparency: Only when everyone involved understands how data becomes added value will data intelligence come to life.
Network the areas of IT, management and specialist departments. Data intelligence thrives on dialogue and joint development. The best ideas are generated in cross-functional teams because different perspectives come together. Use external coaching to establish new thinking and methods - and to master the step from big data to smart data and true data intelligence.
My analysis
Data intelligence is more than just technology. It is attitude, culture and method all in one. Companies that use their data strategically gain agility, future viability and a competitive advantage. Big data provides the input, smart data the targeted selection, and data intelligence turns it into reliable decisions. However, the path to a data-intelligent organisation is a process - and is best achieved with clear objectives, practical projects and open exchange.
Transruption coaching accompanies decision-makers on this journey: We bring structure to the diversity of data, sharpen the focus on the essentials and jointly shape the next development step. Data intelligence is not a static state, but a continuous learning process. Those who unleash it actively shape the future - and secure a sustainable advantage in the digital age.
Further links from the text above:
Data intelligence: definition and application [5]
Unleashing data intelligence: Big Data & Smart Data [11]
Big data vs. smart data: is more always better? [2]
Smart data definition: What is smart data? [1]
Big data explained simply: definition and meaning [3]
Big data: utilising large amounts of data as an opportunity [4]
Smart data: definition, application and difference to big data [7]
Smart data, or the intelligent use of data [9]
For more information and if you have any questions, please contact Contact us or read more blog posts on the topic TRANSRUPTION here.
„`
Deutsch 
English (UK)