The ability to gain relevant insights from complex and extensive amounts of data is indispensable for decision-makers today. This expertise, known as Data intelligence, Data intelligence supports companies in deriving targeted decisions from the wealth of information. While big data seems increasingly unmanageable, data intelligence transforms this flood of data into smart, valuable information.
The importance of data intelligence in the modern corporate world
Many companies are faced with the challenge of collecting large amounts of different data on a daily basis. Examples of this include transaction data in the financial sector, customer interactions in retail or sensor data in Industry 4.0. Big data describes these huge amounts of raw data, which are often unstructured and heterogeneous. However, decision-makers do not need masses of data, but well-prepared information that they can use in a targeted manner. This is where Data intelligence It analyses, filters and interprets big data to generate smart data - precise, relevant data sets that provide directly usable insights.
One retailer, for example, uses data-intelligent processes to make targeted recommendations for product range optimisation based on customer purchases and stock levels. In the financial sector, smart data helps to recognise market developments at an early stage and better assess risks. In manufacturing, too, intelligent data helps to optimally plan maintenance cycles by evaluating machine sensors, thereby minimising downtimes.
Understanding data intelligence: Big Data versus Smart Data
While big data focuses on the sheer quantity and variety of data, smart data emphasises the quality and usability of the information. Big data can be seen as a raw material from which high-quality smart data is specifically extracted using data-intelligent processes. This data has already been checked, filtered and often contextualised so that it can be used directly to support decision-making.
One example is a technology company that uses data intelligence to precisely analyse customer data, enabling personalised offers in real time. Another case can be seen in the automotive industry, where continuous analysis of vehicle data not only allows maintenance intervals to be predicted, but also new service concepts to be developed. In the healthcare sector, smart data offers individualised therapeutic approaches, as patient data, laboratory values and device data are intelligently combined and analysed.
Practical tips for more data intelligence in your company
In order to use data intelligence sensibly, it is important to first define clear objectives. Which decisions should be better supported by data? Companies can then identify relevant data sources and focus on the quality and relevance of the data. Structured data preparation, including cleansing and consolidation, creates the basis for meaningful analyses.
The use of modern methods such as machine learning or AI can facilitate the automatic extraction of smart data from large volumes of data. It is also advisable to break down silos and promote cross-divisional data integration. This creates a holistic picture that enriches decision-making processes.
BEST PRACTICE for a customer (name concealed due to NDA contract) is that a data-intelligent project in the area of sales not only increased the closing rate through targeted analysis of customer preferences and behaviour, but also optimised customer service. The data intelligence made it possible to recognise relevant patterns and derive quick, individual decisions from them, which led to improved customer satisfaction.
Data intelligence as a companion for digital transformation projects
More and more decision-makers are reporting that they experience data intelligence as a valuable support in transformation processes. Whether in the introduction of new IT systems, the development of data-driven business models or in marketing - intelligent use of data creates transparency and supports agile reactions. This enables companies to avoid bad investments and recognise opportunities more quickly.
Companies in the logistics sector are also relying on data-intelligent solutions to make supply chains more efficient, for example through real-time data analysis for route optimisation. In the energy sector, smart data helps to accurately predict consumption and generation, which reduces costs in the long term. Last but not least, insurance companies are using data intelligence to better assess damage risks and offer customised policies.
My analysis
Today, data intelligence is a key success factor for companies in all sectors. The transformation of big data into valuable smart data enables well-founded and quick decisions. Decision-makers who actively shape data intelligence create better conditions for efficiency, innovation and market advantages. This is not just about technology, but above all about the intelligent combination of data-based insights with human expertise. Practical experience shows that data-intelligent guidance supports projects, creates trust and makes sustainable results possible.
Further links from the text above:
Data intelligence - big data & smart data for decision-makers
Big data vs. smart data: is more always better?
Data intelligence: big data and smart data for decision-makers
Big data: the utilisation of large amounts of data
Smart data: definition, application and difference to big data
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