In the age of digitalisation, the ability to identify the decisive added value in large volumes of data is key for managers. The combination of big data and smart data, also known as Data intelligence offers effective support for well-founded decision-making. It is not just about collecting data, but about gaining actionable insights that help companies to act in a future-orientated and competitive manner.
Understanding data intelligence: From a flood of data to actionable knowledge
Large data streams, also known as big data, describe the sheer volume of different types of information that companies generate on a daily basis. This can come from customer interactions, machine data or social networks. However, mass alone does not guarantee benefits. This is where the concept of Data intelligence It filters, sorts and analyses these data streams so that they are transformed into smart data - in other words, high-quality, relevant data that offers direct added value.
In retail, for example, data-intelligent analyses make it possible to recognise customer trends at an early stage and adjust stock levels effectively. In the manufacturing industry, sensor monitoring and intelligent data evaluation stabilise production and optimise maintenance cycles. Insurance companies are also increasingly relying on these methods to assess damage risks more precisely and design customised offers.
Smart data at the heart of data intelligence
Smart data ideally complements big data. While big data is primarily characterised by volume, variety and speed, smart data focuses on data quality and context. Intelligent algorithms sift through huge amounts of data, eliminate noise and highlight relevant patterns. Decision-makers thus receive precisely the information that specifically supports their business strategy.
In marketing, for example, smart data can be used to make personalised campaigns more targeted and achieve significantly higher success rates. In the energy sector, consumption analysis is optimised in order to develop sustainable and cost-efficient concepts. Banks, in turn, benefit from more precise analyses in credit risk management, creating a more secure basis for decision-making.
BEST PRACTICE at the customer (name hidden due to NDA contract) A global logistics service provider used data-intelligent methods to generate smart data from big data. Route planning was optimised through the targeted evaluation of traffic data and shipment tracking. Delays were measurably reduced, which significantly improved customer satisfaction and the cost structure.
Use of data intelligence in complex projects
The implementation of data-intelligent solutions presents organisations with challenges. They require a clearly structured analysis architecture, suitable technologies and trained employees. Clients often report that accompanying coaching in the implementation of new data-driven approaches provides valuable impetus and confidence. This allows projects such as digitalised production control or customer-focused sales optimisation to be managed systematically.
In the telecommunications industry, data-intelligent coaching helped a provider to avoid customer churn by recognising usage patterns at an early stage. This was achieved by combining usage data, feedback analyses and service histories. The data-based support led to more stable customer relationships and significantly improved offers.
Data intelligence as a competitive advantage for managers
Data intelligence is not a short-term trend, but a strategic success factor. Decision-makers who utilise data-intelligent methods gain a time advantage, reduce risks and tap into new potential. The correct interpretation of smart data can promote innovation, optimise processes and open up markets more effectively.
An example from the automotive industry shows how data-intelligent systems are revolutionising vehicle maintenance: Sensor data is used to recognise potential defects at an early stage. This allows workshop appointments to be scheduled instead of expensive emergency repairs. This increases customer satisfaction and reduces costs.
Intelligent data analyses are also providing impetus for individual patient care in the healthcare sector. For example, clinics analyse disease progression and treatment data in order to better plan therapies. This increases treatment efficiency and helps to deploy resources in a more targeted manner.
My analysis
Today, data intelligence is indispensable for decision-makers who want to turn traditional floods of data into valuable competitive advantages. The combination of big data and smart data creates reliable insights that help companies to effectively align their strategy and efficiently support projects. Practical application examples show how data-intelligent action promotes success across all industries and makes them future-proof.
Further links from the text above:
[1] Data intelligence: big data and smart data for decision-makers
[2] Big data vs. smart data: is more always better?
[4] Data analysis - big data & smart data for decision-makers
[5] Data intelligence: How decision-makers use big & smart data ...
[7] Smart data: definition, application and difference
[8] What is smart data?
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