Nowadays, data analysis is a decisive key to sustainable success in many industries. The precise processing and interpretation of this information poses a challenge, particularly in the context of complex projects in which large amounts of data are collected. KIROI Step 3 therefore focuses on the combination of big data and smart data in order to strategically master data analysis and make valuable insights visible.
The importance of big & smart data for data analysis
Big data describes the collection and storage of enormous amounts of data from various sources, such as sensors, web applications or social media. However, the sheer volume of raw data alone offers little benefit. This is where smart data comes in: it is specifically analysed, high-quality data that is precisely tailored to specific issues. Only through this transition from big data to smart data is it possible to gain real insights in data analysis and effectively support decisions.
In industry, for example, sensor data from production facilities is collected and analysed. A major manufacturer of mechanical engineering products was able to reduce downtimes and optimise maintenance cycles by detecting anomalies at an early stage. In retail, data analysis helps to understand customer preferences based on purchasing behaviour and to develop personalised marketing campaigns. The energy industry also benefits: Consumption patterns are analysed in order to control power grids more efficiently and avoid peak loads.
BEST PRACTICE with one customer (name hidden due to NDA contract) For a medium-sized company in the mechanical engineering sector, we implemented a comprehensive data analysis that combined big data streams with intelligent smart data methods. This enabled hidden patterns to be discovered and predictive maintenance to be established, resulting in 20 % fewer unplanned downtimes.
KIROI step 3: Data analysis using intelligent technologies and methods
The third step in the KIROI approach is the actual data analysis. Modern technologies such as machine learning, statistical modelling and advanced visualisation techniques are used here. The aim is to process and interpret data volumes and transform them into meaningful information. Structured data analysis helps to recognise trends, make predictions and support decision-making processes.
For example, data analysis can be used in logistics to optimise supply chains with real-time data and identify bottlenecks at an early stage. In the healthcare sector, it supports pattern recognition in large patient data and enables personalised treatment approaches. Data analysis is also used in the financial sector to better assess risks and improve fraud detection systems.
BEST PRACTICE with one customer (name hidden due to NDA contract) For a company in the logistics sector, we developed an analysis platform that integrated both historical and real-time data. This enabled delivery times to be accurately predicted and dynamically adjusted, which significantly increased customer satisfaction.
Practical tips for implementing data analysis
To ensure that data analysis projects are successful, managers should consider the following points:
- Always check the quality and relevance of the data. Only valid and consistent data leads to reliable results.
- Define clear analysis objectives so that methods and tools can be selected appropriately.
- Putting together interdisciplinary teams, as complementary competences from IT, statistics and specialist areas are essential.
- Proceed step-by-step: Open up large amounts of data step by step through explorative analyses and visualisations.
- Prepare results in an understandable way in order to feed them into decision-making processes and derive recommendations for action.
In practice, many companies report that starting with a clear question and focussing on strategically important data supports data analysis enormously. We often support projects in which we provide impetus to better structure complex data landscapes and utilise the appropriate tools.
Data analysis: the key to sustainable success in modern companies
The importance of data analysis will continue to grow in all industries. Companies that utilise big data and smart data in a targeted manner will gain a competitive advantage by acting faster and in a more informed manner. The combination of innovative technologies and methodological expertise in particular makes the difference. KIROI Step 3 provides specific support in exploiting this potential and enabling data-based decisions.
BEST PRACTICE with one customer (name hidden due to NDA contract) In an energy supplier project, we helped to intelligently analyse consumption data. This solution made it possible to better manage the grid load at peak times, which led to significant cost savings and improved security of supply at the same time.
My analysis
In today's world, data analysis is an indispensable skill for companies that want to optimise their processes and make them fit for the future. The integration of big data and smart data in KIROI step 3 promotes effective information gathering. This enables more informed strategic decisions to be made and operational processes to be managed more efficiently. Sophisticated data analysis helps to visualise potential and provide innovative impetus.
Further links from the text above:
Smart + Big Data | Artificial Intelligence [1]
Big and smart data - from statistics to data analysis [3]
Smart data: definition, application and difference to big data [5]
Making decisions with smart data [6]
For more information and if you have any questions, please contact Contact us or read more blog posts on the topic Artificial intelligence here.
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