The increasing amount of data in companies makes it essential to manage and utilise this information in a targeted manner. Here Data intelligence plays a decisive role. It describes the ability not only to collect complex data volumes, but also to process them intelligently and derive well-founded insights from them. Especially in times of big and smart data, data intelligence enables strategic advantages to be realised and projects to be successfully supported.
Data intelligence paves the way for well-founded decisions
Companies from various industries use data intelligence to improve their business processes. For example, a large retail chain uses data intelligence to continuously analyse the purchasing behaviour of its customers. This enables customised offers to be developed and stock levels to be managed efficiently. Another example from the automotive industry shows how production data from large assembly lines is monitored with the help of sensors and machine learning in order to avoid unplanned downtime through early maintenance. An energy supplier is also using data intelligence to recognise household consumption patterns and better integrate renewable energies.
BEST PRACTICE with one customer (name hidden due to NDA contract) The consumer goods company used data intelligence to intelligently analyse supply chain data in order to identify delivery bottlenecks at an early stage and plan alternative routes. This significantly improved delivery reliability without incurring additional costs.
How data intelligence masters big & smart data
Big data stands for the enormous volume of data that companies generate nowadays. Smart data, on the other hand, describes the targeted extraction of information that is actually relevant from this volume of data. Data intelligence combines these two concepts by using advanced technologies such as artificial intelligence (AI) and machine learning to analyse large volumes of data in a structured way. This creates an effective interplay that not only collects data, but also provides it with high added value.
In practical terms, this means in the financial sector, for example, that credit risks are assessed more precisely by intelligently combining customer data, market information and payment behaviour. Retail companies use these models to forecast seasonal fluctuations in demand and adapt their strategies in an agile manner. In logistics, on the other hand, transport data is used to optimise routes, shorten delivery times and reduce costs.
BEST PRACTICE with one customer (name hidden due to NDA contract) A medium-sized logistics provider implemented data-intelligent systems that use real-time data from vehicle fleets and traffic information to improve capacity utilisation and enable dynamic supply chain management. This significantly increased customer satisfaction and operational efficiency.
Technological components of data intelligence
The core technologies that support data intelligence include data lakes, data warehouses and data catalogues. These store and classify data from a wide variety of sources. AI-supported algorithms scour the information for patterns, anomalies and correlations. This gives decision-makers access to structured, transparent and trustworthy data - a prerequisite for targeted measures.
An insurance company, for example, benefits from data-intelligent solutions to analyse claims and detect fraudulent activities at an early stage. This saves considerable costs and increases security for all parties involved.
BEST PRACTICE with one customer (name hidden due to NDA contract) A bank used data intelligence to personalise its customer management. Analysing account activity in combination with external data led to customised offers, which increased customer loyalty and opened up new sales potential.
Targeted use of data intelligence: Tips for success
The start of data-intelligent projects requires a clear definition of objectives. Companies should know exactly which questions need to be answered and which processes need to be optimised. Accompanying coaching can help to make the complexity manageable. This includes selecting suitable technologies, training employees and developing a sustainable data strategy.
It is essential to establish data quality standards and promote an open data culture. In this way, managers can ensure that the insights gained are actually utilised and further developed. Regular reviews of data models and processes guarantee that data intelligence keeps pace with changing market requirements.
In practice, it has been shown that teams that use data-intelligent methods increase their efficiency and respond better to challenges. Optimising the integration of specialist knowledge and technology helps to achieve innovative and sustainable solutions.
My analysis
Data intelligence is an indispensable key to successfully managing today's flood of data. By combining big and smart data with modern technologies, it opens up a wide range of opportunities for companies to optimise and innovate. Clear strategies and targeted support make it possible to utilise the potential of data intelligence in the best possible way. This enables organisations to create sustainable competitive advantages and develop a sustainable data culture.
Further links from the text above:
Data intelligence - what is it? [1]
Data intelligence: mastering big data & smart data [2]
Data intelligence: application and examples [4]
Data Intelligence explained by IBM [5]
Data Intelligence Guide - BARC [8]
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