In today's business world, the topic of Data intelligence<\/b> is becoming increasingly important. Companies are faced with the challenge of using large amounts of data efficiently in order to optimise processes and remain competitive. The intelligent linking of big data - i.e. large raw data sets - and smart data - high-quality and contextualised data - plays a central role in this. Valuable insights that pave the way to success can only be gained through targeted analysis and structured processing.<\/p>\nWhat does data intelligence mean for companies?<\/h2>\n
Data intelligence refers to the ability to generate usable information from diverse and sometimes unstructured data volumes. This process includes not only the collection of data, but also its analysis, validation and targeted utilisation. Companies benefit from this because they can make well-founded decisions based on real-time data and high data quality. This allows risks to be reduced, costs to be better controlled and innovation potential to be recognised more quickly.<\/p>\n
Practical examples show how different Data intelligence<\/b> is used: A logistics company uses it to control the supply chain using real-time data and avoid delays. A manufacturer analyses production data to plan maintenance work in advance and reduce downtime. A marketing team evaluates customer behaviour in order to precisely adapt campaigns and increase conversion rates.<\/p>\n
BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> The customer used data intelligence to optimise its stock levels with automated analysis of sales history. This led to a reduction in excess stock and improved availability of goods without incurring additional storage costs.<\/i><\/p>\nData intelligence in practice: mastering big data and smart data<\/h2>\n
Companies are often faced with the challenge of converting big data into usable smart data. Big data describes the sheer volume of information collected, but it is only through intelligent processing that it can be turned into useful insights. A bank, for example, analyses extensive transaction data and filters out relevant patterns in order to detect attempted fraud at an early stage. Online retailers use algorithms that generate consistent recommendations from unstructured click and purchase data. In industry, too, smart data helps to monitor machine statuses and avoid breakdowns.<\/p>\n
It is important to continuously ensure data quality. Automated checking processes identify errors or duplicates and thus help to ensure that decisions are based on valid information. Access rights and data protection guidelines also play a major role in protecting sensitive information and ensuring compliance.<\/p>\n
BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> With the help of a data intelligence solution, complex production data could be monitored in real time and bottlenecks in the production process could be predicted. This transparency made it possible to react quickly and increase efficiency by several percentage points.<\/i><\/p>\nSpecific application examples from various industries<\/h3>\n
Support in the healthcare sector Data intelligence<\/b> the analysis of patient data to develop personalised therapies and better predict disease progression. For example, the treatment of cardiological diseases can be improved through precise data analyses and patient safety can be increased.<\/p>\n
In retail, the analysis of purchasing and behavioural data enables a targeted customer approach as well as the flexible adaptation of the product range to seasonal fluctuations. A DIY store chain reports a significant improvement in the ability to plan customer service resources by analysing service requests.<\/p>\n
The financial sector uses data intelligence to assess risk and forecast market movements. Intelligent algorithms help to tailor individual customer offers and fulfil regulatory requirements at the same time.<\/p>\n
BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> A financial services provider used data intelligence to assess credit risks with the help of machine learning models. This led to faster decisions and improved risk profiles, which had a positive impact on customer satisfaction.<\/i><\/p>\nTips for the successful implementation of data intelligence projects<\/h2>\n
A clear definition of objectives supports the success of data intelligence projects. Companies should know exactly which questions they want to answer with data. It helps to bring together interdisciplinary teams of IT specialists and specialist departments.<\/p>\n
The structured design of modern data architectures such as Data Mesh or Data Fabric also enables better governance and facilitates data integration across different systems. Autom<\/p>","protected":false},"excerpt":{"rendered":"