{"id":347188,"date":"2025-05-20T12:08:35","date_gmt":"2025-05-20T10:08:35","guid":{"rendered":"https:\/\/sauldie.org\/de\/datenanalyse-big-data-smart-data-zusammenspiel\/"},"modified":"2025-05-20T12:08:35","modified_gmt":"2025-05-20T10:08:35","slug":"data-analysis-big-data-smart-data-interplay","status":"publish","type":"post","link":"https:\/\/risawave.org\/en\/datenanalyse-big-data-smart-data-zusammenspiel\/","title":{"rendered":"Mastering Data Analysis: KIROI Step 3 with Big & Smart Data"},"content":{"rendered":"

**Data analysis** is a key process today for gaining valuable insights from large and complex datasets. Particularly in the third phase of the KIROI approach, the combination of Big Data and Smart Data plays a central role. These two aspects complement each other ideally, because Big Data offers comprehensive information through its sheer volume of data, while Smart Data provides the relevant, filtered, and high-quality data. This enables companies to make better decisions and design operational processes more efficiently.<\/p>\n

Data Analysis \u2013 Big Data and Smart Data working together<\/h2>\n

Big Data refers to very extensive and diverse datasets, which mostly originate from the most varied sources and at high speed. Smart Data, on the other hand, is the result of targeted processing and filtering of this raw data. It focuses on quality and relevance in order to enable well-founded analysis. This significantly increases the efficiency of data analysis.<\/p>\n

A practical example from logistics shows how Smart Data improves the analysis of sensor data from vehicle fleets. By intelligently filtering only relevant data points, routes can be optimised, which reduces fuel costs and shortens delivery times. This also allows for a faster response to changes in traffic or environmental influences.<\/p>\n

In healthcare, precise **data analysis** using smart data supports the individual tailoring of therapies. Clinics specifically analyse selected patient data to optimise treatment processes and reduce costs. This enables patient-centred care, which is gaining increasing importance.<\/p>\n

BEST PRACTICE with a client (name redacted due to NDA agreement): For an industrial company, we collected data from machines, processes, and the environment and processed it using Smart Data. This improved production capacity, significantly reduced downtime, and increased efficiency. Through the targeted combination of Big and Smart Data, the company was able to operate more resource-efficiently and economically.<\/p>\n

Smart Data as a crucial lever in data analysis<\/h3>\n

Smart data is created by intelligent algorithms that extract only the truly important information from large datasets. This data quality is essential, as an analysis is only as good as the data it is based upon. Companies across various industries frequently report significant challenges in ensuring data quality.<\/p>\n

For example, manufacturers use smart data to predict the maintenance requirements of their machines and avoid unplanned downtime. Financial institutions benefit from precise transaction analyses that help to detect fraud attempts earlier. Retailers also use smart data by filtering sales data to precisely control stock levels and anticipate demand trends.<\/p>\n

In transport systems, authorities analyse sensor data for real-time traffic light control, thereby improving traffic flow and reducing waiting times. These examples illustrate that smart data, as part of data analysis, is driving efficiency gains and innovations across industries.<\/p>\n

Practical tips for the successful implementation of data analysis with Big & Smart Data<\/h2>\n

For companies aiming to master data analysis, finding the right balance between data volume and data quality is essential. The following insights can help with this:<\/p>\n