Data intelligence is increasingly becoming an indispensable tool for creating real added value from the wealth of information available. In particular, the move from big data to smart data, as described in the third section of the KIROI model, opens up new opportunities for companies to better understand their data and utilise it more efficiently. This article shows how companies can master this transition and use data intelligence to improve their decision-making processes.
How data intelligence is paving the way from big data to smart data
The challenge for many companies is to structure and process large volumes of data - known as big data - in such a way that they provide practical insights. Data intelligence refers to precisely this process: raw data is analysed, classified and placed in the appropriate context so that it is transformed into valuable smart data. This creates the basis for well-founded decisions and targeted process optimisation.
For example, a European retailer uses sales figures and customer preferences to create personalised offers, predict demand more accurately and manage stock levels efficiently. In the logistics industry, companies analyse GPS and traffic data to shorten delivery times and optimise routes in the long term. Financial service providers also analyse transaction data in order to detect fraud attempts at an early stage and thus minimise risks.
BEST PRACTICE with one customer (name hidden due to NDA contract) An international medium-sized mechanical engineering company reduced its machine downtimes by 20 %. The data-intelligent evaluation of real-time sensor data helped to initiate specific maintenance measures in good time and thus minimise downtime.
The role of artificial intelligence and machine learning
Without the use of modern technologies, the step from big data to smart data would hardly be possible. Data intelligence uses AI and machine learning to recognise patterns in data volumes and make predictions. This enables companies not only to understand what has happened in the past, but also to derive future trends and risks.
A mobility management service provider, for example, used data-intelligent analyses to reduce CO₂ emissions and cut costs by evaluating and managing traffic flows in real time. In the financial sector, predictive models enable a more accurate credit risk assessment. E-commerce platforms optimise their pricing strategies dynamically based on customer behaviour and stock levels.
Data intelligence practice rooms: efficiency, risk and customer loyalty
Data intelligence supports managers in using company data to make targeted decisions. For example, a food delivery service increases its efficiency by analysing sales data and adjusting stock levels in line with demand. An investment manager uses data-intelligent forecasts for more reliable market analyses. Producers are also using data intelligence to optimise their production processes in order to conserve resources and avoid downtime.
Such applications show how data intelligence can be flexibly tailored to different industries and help to master the complexity of large data streams. It enables faster reactions to market developments and helps to minimise risks with in-depth knowledge.
BEST PRACTICE with one customer (name hidden due to NDA contract) A retail company uses data-intelligent analyses to anticipate seasonal sales peaks and design its product range accordingly. As a result, it benefits from greater customer satisfaction and lower storage costs.
Tips for the successful implementation of data intelligence projects
A clearly structured roadmap is recommended for the successful introduction of data intelligence. Companies should first systematically record and evaluate their data sources, followed by the selection of suitable analysis tools. Involving experts in AI and data science and training employees are crucial to promoting a data-driven culture.
It is equally important to ensure data quality through regular data maintenance and governance. This is the only way to avoid wrong decisions. In addition, companies should rely on open communication channels so that all relevant stakeholders benefit from data intelligence findings.
My analysis
The move from big data to smart data using data intelligence is a key success factor for modern companies. Through intelligent data processing, the use of AI and machine learning, decision-makers gain faster and more reliable insights. This helps them to organise processes more efficiently, assess risks better and strengthen customer relationships. At the same time, data intelligence brings more flexibility and promotes a transparent data culture. In this way, the KIROI model provides companies with targeted and practical support for data-based transformation.
Further links from the text above:
What is data intelligence and what does it mean?
Data intelligence: big data and smart data for decision-makers
Data intelligence: How decision-makers use big & smart data
What is Data Intelligence? (IBM)
Data intelligence or the art of turning data into gold
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