At a time when companies are confronted with huge amounts of data on a daily basis, the ability to utilise this information in a meaningful way is becoming increasingly important. Data intelligence describes precisely this expertise: extracting high-quality, reliable and contextualised smart data from unstructured big data. This not only provides insights, but also concrete impulses for action in day-to-day work. Many clients come to us because they feel that their data potential has not yet been fully utilised. They are looking for ways to generate real added value from the flood of data.
Data intelligence: the bridge between big data and smart data
Big data stands for the huge amounts of raw data that come from a wide variety of sources. Think of sensor data from machines, customer interactions in online shops or logistics information from the supply chain. However, collecting this data alone does not bring any benefits. It is only through data intelligence that this raw data is converted into targeted, meaningful information. This creates smart data that can be used directly for decision-making.
Example from industry: A manufacturer analyses sensor data from production systems. Using data intelligence, he recognises patterns that indicate imminent maintenance requirements. This enables them to avoid breakdowns and reduce downtimes. In the financial sector, intelligent analyses help to detect attempted fraud at an early stage. Smart data is also used in marketing to precisely address target groups and increase customer loyalty.
Practical examples for the use of data intelligence
Data intelligence in production
In the manufacturing industry, sensor data from machines is continuously recorded. Data intelligence can be used to identify wear patterns and optimise maintenance intervals. This increases system availability and productivity. Many companies report that this approach saves them money and improves the quality of their products.
Another example: a car manufacturer uses vehicle data to plan maintenance work with foresight. This minimises downtimes and increases customer satisfaction. The automation of processes is also supported by data intelligence. This enables machines to react independently to changes and make adjustments.
A third example: a logistics company analyses freight data globally. It uses data intelligence to optimise routes and reduce transport costs. Delivery times are shorter and customer satisfaction increases.
Data intelligence in marketing and sales
Marketing agencies use data intelligence to automatically optimise campaigns. They analyse customer and web data in order to tailor offers more precisely. This increases the conversion rate and personalises the approach. Many clients report that this approach reduces wastage and increases the efficiency of their campaigns.
Another example: an e-commerce company segments its target groups with the help of smart data. It recognises which products are particularly relevant for which customer groups. This enables it to launch targeted advertising campaigns and strengthen customer loyalty.
A third example: a service company uses data intelligence to improve the customer journey. It analyses how customers interact with its services and adapts its communication accordingly. This increases customer satisfaction.
Data intelligence in the financial world
Banks use data intelligence to identify market trends and dynamically adjust investment portfolios. They analyse large volumes of data in order to identify risks at an early stage and exploit opportunities. This enables them to advise their customers better and strengthen their competitiveness.
Another example: An insurance company uses intelligent analyses to detect fraud attempts at an early stage. It analyses patterns in the data and identifies suspicious transactions. This enables it to avoid losses and increase the security of its customers.
A third example: a financial services provider uses data intelligence to create personalised offers. It analyses the behaviour of its customers and adapts its products accordingly. This increases customer satisfaction and loyalty.
BEST PRACTICE at the customer (name hidden due to NDA contract) A medium-sized company from the logistics sector wanted to optimise its route planning. With our support, a system was implemented that analyses freight data in real time. Thanks to data intelligence, the routes could be organised more efficiently. Transport costs fell, delivery times were shortened and customer satisfaction increased noticeably. The company reports that this measure has not only enabled it to save costs, but has also increased its competitiveness.
My analysis
Today, data intelligence is a decisive factor for the success of companies. It makes it possible to generate targeted smart data from big data and derive specific impulses for action from it. Many clients come to us because they feel that their data potential has not yet been fully utilised. With the right support, they can successfully implement their data intelligence projects. Clients often report that data intelligence not only enables them to tap into efficiency potential, but also to gain new competitive advantages.
Further links from the text above:
Data intelligence: Big & smart data for better decision-making
Big data vs. smart data: is more always better?
Big data explained simply: definition and significance for the professional world
Smart + Big Data | Artificial Intelligence
Smart data: definition, application and difference to big data
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