In the digital age Data intelligence has become a key success factor for companies. It is not just about collecting large amounts of data, but above all about generating valuable smart data from big data using analytical methods. Only in this way can decision-makers make well-founded and at the same time practically relevant decisions that secure competitive advantages in the long term.
Understanding data intelligence: From the flood of data to a clear basis for decision-making
Big data describes the sheer mass of different types of information that companies generate on a daily basis. This data comes from various sources, such as customer interactions, machine or sensor data, social media and many more. However, the sheer volume of data does not make it usable. This is where Data intelligence It filters, refines and analyses this information in order to generate smart data with concrete benefits.
In retail, for example, data-intelligent systems make it possible to recognise customer preferences at an early stage and adapt product ranges accordingly. In industry, intelligent data evaluation optimises machine maintenance and improves production processes. Insurance companies use data intelligence to assess damage risks more accurately and design customised policies. This turns large amounts of data into valuable information that makes day-to-day business much more effective.
BEST PRACTICE at the customer (name hidden due to NDA contract) A manufacturing company used data intelligence to analyse sensor data from production. This enabled faults to be identified at an early stage and maintenance work to be planned more efficiently. The result was less machine downtime and a noticeable increase in productivity.
Big data and smart data: the combination for valuable insights
Big data stands for extensive, diverse and fast data volumes, while smart data describes high-quality and specifically selected information that is obtained from this data. The true power of Data intelligence lies in transforming big data into smart data using modern technologies such as artificial intelligence, machine learning and data analytics.
Although many companies collect huge amounts of data, they find it difficult to derive specific recommendations for action from it. Smart data closes this gap by eliminating unnecessary information and providing precisely the data that is relevant for strategic decisions. This saves time and resources and increases the quality of decision-making.
For example, data-intelligent analyses in the financial sector help to base portfolio decisions on reliable data instead of relying on unstructured amounts of information. In logistics, the targeted filtering of big data enables supply chains to be made more transparent and bottlenecks to be recognised at an early stage.
BEST PRACTICE at the customer (name hidden due to NDA contract) A logistics company used data intelligence to extract relevant KPIs from big data. This made it possible to forecast delivery times more accurately and manage stock levels better. This helped to reduce costs and increase customer satisfaction.
Smart data as the basis for successful project decisions
Decision-makers often report how data-intelligent approaches provide valuable impetus in the planning and implementation of complex projects. By selecting relevant data, risks can be minimised and opportunities identified more quickly. In this way, data intelligence helps managers to react in a well-founded and agile manner.
The manufacturing industry benefits from this by monitoring key production figures and initiating adjustments promptly. In marketing, smart data enables more precise targeting and greater campaign efficiency. In healthcare, too, personalised therapies are prepared with the help of intelligent data analysis.
BEST PRACTICE at the customer (name hidden due to NDA contract) A marketing agency implemented data-intelligent systems to analyse customer behaviour in real time. This enabled campaigns to be flexibly adapted and wastage significantly reduced. This led to a noticeable increase in sales and improved customer loyalty.
Increase data intelligence: Practical tips for decision-makers
If you want to specifically promote data intelligence, there are several levers you can use. Firstly, a clear definition of the analysis objectives is important in order to identify relevant data. Secondly, modern analysis tools and AI methods should be used to automatically filter and interpret large volumes of data.
Thirdly, a company-wide data strategy helps to break down data silos and thereby improve the flow of information. In addition, the ongoing training of employees in handling data helps to create greater awareness of quality and data protection. Finally, transparent processes promote the acceptance of data-driven decisions in all areas.
Practical use cases from e-commerce show how data intelligence enables personalised recommendations. In manufacturing, it facilitates the predictive maintenance of machines. In the financial sector, it leads to more precise risk analyses. These examples reflect the broad applicability across various industries.
My analysis
To summarise, it can be said that Data intelligence is indispensable for decision-makers today in order to derive real added value from the flood of data. The combination of big data and smart data not only enables faster and more precise decisions, but also sustainable competitive advantages. Companies that integrate data-intelligent methods improve their efficiency, minimise risks and open doors to innovation. The trend towards greater data quality and utilisation will continue to accompany key projects and support companies in many sectors.
Further links from the text above:
Smart data: How intelligent data is shaping our future
Big data vs. smart data: is more always better?
Data intelligence: How decision-makers use big & smart data ...
Smart Data: Definition, application and difference to Big ...
Unleashing data intelligence: Big Data & Smart Data for ...
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