{"id":350754,"date":"2025-02-01T21:46:18","date_gmt":"2025-02-01T20:46:18","guid":{"rendered":"https:\/\/sauldie.org\/de\/datenintelligenz-big-data-zu-smart-data-12\/"},"modified":"2025-02-01T21:46:18","modified_gmt":"2025-02-01T20:46:18","slug":"data-intelligence-big-data-to-smart-data-12","status":"publish","type":"post","link":"https:\/\/risawave.org\/en\/datenintelligenz-big-data-zu-smart-data-12\/","title":{"rendered":"Unleash data intelligence: Mastering Big Data & Smart Data"},"content":{"rendered":"

The ongoing digitalisation is increasingly changing how companies deal with the flood of data. Data intelligence is gaining importance because it helps to extract valuable insights from large volumes of data. Especially in the area between Big Data and Smart Data, it becomes clear how important it is to use data in a targeted and quality-oriented manner in order to achieve competitive advantages.<\/p>\n

Data Intelligence: From Raw Material to Valuable Resource<\/h2>\n

Big Data describes the sheer volume of highly diverse data that accumulates daily within companies \u2013 for instance, transaction data in retail, machine data in industry, or customer data in the financial sector. However, this raw data alone is rarely directly usable because it is often unstructured and complex.<\/p>\n

Data intelligence means extracting truly relevant, structured, and high-quality information from this mass. This so-called smart data is created through the intelligent analysis and filtering of big data. In this way, companies transform the raw material of data into a valuable commodity that supports strategic decisions.<\/p>\n

For example, an online retailer uses data intelligence to analyse purchasing behaviour in order to create targeted individual offers. A logistics company, in turn, uses it to dynamically optimise routes, conserve resources, and shorten delivery times. And in the healthcare sector, intelligent data helps to tailor treatment plans more effectively to individual patients.<\/p>\n

The role of quality and context in data intelligence<\/h2>\n

The primary difference between Big Data and Smart Data lies in the quality and relevance of the data. While Big Data refers to sheer volume, Smart Data means that data is filtered, cleaned, and viewed in the correct context. Data intelligence starts here, because only then can truly meaningful and usable insights be generated.<\/p>\n

In practice, many companies report that unstructured or flawed big data provide only limited helpful results. An insurer utilising data intelligence, on the other hand, can assess claims more precisely by integrating relevant variables and filtering out incorrect details.<\/p>\n

Manufacturers also benefit by analysing machine data at the right time to predict maintenance, which reduces unplanned downtime. At the same time, the integration of governance and data protection guidelines protects sensitive data and fosters trust.<\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/h3>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> The company from the European mechanical engineering sector used data intelligence to link extensive sensor data from manufacturing with external market and weather data. This enabled them to flexibly adapt production processes, minimise downtime, and use energy more efficiently. The targeted use of smart data measurably reduced costs and noticeably increased output.<\/i><\/p>\n

Technologies and methods for data-driven processes<\/h2>\n

Data intelligence relies on modern technologies such as Artificial Intelligence (AI), Machine Learning, and Data Mining. They make it possible to recognise patterns and connections that would be difficult for humans to see alone.<\/p>\n

For example, a retail chain can use machine learning to analyse purchasing patterns and identify seasonal trends early on. This optimises stock orders and avoids overstocking. In the financial sector too, automated prediction models help to assess credit risks more accurately and detect fraud cases more quickly.<\/p>\n

Likewise, logistics companies rely on real-time data analysis to make fleet management efficient and to circumvent traffic disruptions. These data-driven processes reduce downtime and create added value for customers and businesses alike.<\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/h3>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> A leading insurance company implemented an AI-powered platform to automatically check incoming forms for completeness and inconsistencies. This significantly reduced processing times and noticeably improved data quality, enabling more informed risk analyses.<\/i><\/p>\n

Practical tips for using data intelligence in the company<\/h2>\n

For businesses looking to successfully unleash data intelligence, the following steps are helpful:<\/p>\n