Today, data intelligence is a decisive factor for managers who want to gain relevant insights from the flood of big data. While big data refers to enormous amounts of raw information, data intelligence stands for the targeted processing and utilisation of this data in order to manage strategies and processes in a well-founded manner. This enables managers to make more precise decisions and secure competitive advantages.<\/p>\n
Big data comprises huge amounts of data from a wide variety of sources - including social media, sensors and market analyses. However, these huge amounts of data are often unstructured and require time-consuming processing. This is precisely where data intelligence comes in: Automated processes and intelligent algorithms are used to quality assure, classify and contextualise data. This turns purely quantitative data into valuable smart data that can be utilised in a precise and targeted manner.<\/p>\n
Practical examples support this understanding: a production company uses data intelligence to analyse machine data in real time and predict maintenance intervals. In retail, the structured analysis of customer data enables personalised offers that increase sales figures. In the financial sector, intelligent data processing enables risks to be better assessed and investment decisions to be made more reliably.<\/p>\n
The difference between big data and smart data can be described with a simple comparison: While big data stands for the sheer volume of data, smart data focuses on the quality and usability of the information. Smart data means using filters and algorithms to select unfiltered raw data in order to extract only the information relevant to the respective business model.<\/p>\n
In the automotive industry, smart data analyses support the development of driver assistance systems with precise sensor data. In the energy sector, consumption-relevant data is intelligently evaluated in order to optimise network controls. Telecommunications also benefit from smart data by recognising and rectifying network failures at an early stage.<\/p>\n
This high data quality leads to faster, more well-founded decisions and helps to minimise business risks and use resources more efficiently. Modern tools that automate data intelligence and make it accessible to managers are essential for this.<\/p>\n
Managers are faced with the challenge of developing data inte<\/p>","protected":false},"excerpt":{"rendered":"