The ability to utilise data intelligently is becoming increasingly important in today's digital world. With the right approach, companies can gain valuable insights from huge amounts of data. The practice of data intelligence makes it possible to transform big data into lean, relevant and high-quality information that significantly supports business success. The focus is no longer on the sheer volume of data, but on analysing and applying it in a targeted manner.
Data intelligence: the path from data volume to usable information
Companies today have enormous volumes of data, often from a wide variety of sources - such as CRM systems, IoT sensors or external databases. However, this big data is often unstructured and complex. The challenge lies in filtering this flood of information in a meaningful way and processing it so that it is available in a precise, contextualised and data protection-compliant manner - in short, smart data is created. Data intelligence describes precisely this process of targeted refinement and intelligent utilisation of data.
One logistics company, for example, uses data intelligence to continuously analyse large volumes of delivery and route information. Dynamic route optimisation enables them to reduce transport times and cut costs. Similarly, retail companies use data intelligence by evaluating customer behaviour on the website, enabling personalised offers that increase customer satisfaction. In industry, smart analyses of sensor data enable predictive maintenance to prevent expensive machine breakdowns.
How data intelligence supports practical decisions
The transformation from big data to smart data takes place in several steps. Firstly, the different data sources are linked together, for example by integrating customer, production and market data. This is followed by careful data cleansing, in which incomplete or incorrect information is sorted out. State-of-the-art algorithms then support the analysis by recognising patterns and creating forecasts. The insights gained are visualised in clear dashboards to facilitate decision-making processes.
A financial services provider uses such data-intelligent processes to recognise fraudulent transactions at an early stage. Automated pattern recognition enables the company to minimise potential risks and react more quickly. An insurance company, on the other hand, uses data intelligence to evaluate sensor-based telemetry data in order to calculate individual tariffs based on actual customer behaviour. In the healthcare sector, data-intelligent analyses help to evaluate treatment success and better predict patient progression.
Data intelligence and technological innovations: From Artificial Intelligence to Machine Learning
Artificial intelligence (AI) and machine learning play a key role in the realisation of data intelligence. They make it possible to derive relevant smart data from the often confusing big data sets without the need for human experts to check each data set manually. This not only makes processes faster, but also increases the accuracy of the insights gained.
In e-commerce, the use of machine learning algorithms helps to recognise purchasing patterns and dynamically adjust stock levels. In the manufacturing industry, AI systems are used to optimise production processes by evaluating real-time data from machines and making automatic adjustments. Marketing departments use AI-based segmentation to generate individual customer profiles that enable targeted campaigns.
BEST PRACTICE with one customer (name hidden due to NDA contract) A medium-sized industrial company integrated data intelligence into its production lines. By analysing sensor data with AI, maintenance requirements were identified at an early stage and downtimes were significantly reduced. At the same time, resources were utilised more efficiently and production costs were reduced.
Data intelligence as part of strategic corporate development
The strategic use of data intelligence is now essential in many industries. It is not just about technical solutions, but also about rethinking the corporate culture: data is recognised as a valuable resource and actively used. Consistent data management with clear governance guidelines ensures the quality and protection of sensitive information.
A telecommunications provider, for example, uses data intelligence to predict network outages and proactively manage service requests. In retail, sales data is analysed in real time to drive dynamic pricing strategies. Similarly, mechanical engineering uses data-intelligent applications to shorten development cycles and bring innovations to market faster.
BEST PRACTICE with one customer (name hidden due to NDA contract) An IT service provider introduced a data-intelligent platform that bundles various internal and external data sources. This significantly improved project forecasts and resource utilisation planning. The increased transparency led to greater customer satisfaction and a better competitive position.
My analysis
Today, data intelligence is a decisive success factor for companies in every industry. The difference between simply collecting data and using it intelligently lies in the quality and relevance of the information. Smart data helps companies to work more efficiently, minimise risks and act in a more customer-oriented manner. The combination of modern technology, a well thought-out data strategy and expert knowledge forms the basis for unleashing the full potential of big data and achieving sustainable competitive advantages.
Further links from the text above:
What is smart data?
Big data vs. smart data: is more always better?
How big data becomes smart data for decision-makers
How to secure your lead with Big & Smart Data
Data intelligence - big data and smart data for decision-makers
For more information and if you have any questions, please contact Contact us or read more blog posts on the topic Artificial intelligence here.
Deutsch 
English (UK)