Data analysis** is a key competence for decision-making processes in modern companies. It makes it possible to gain valuable insights from large and complex amounts of data. As part of KIROI Step 3, data analysis expertise is systematically strengthened so that decision-makers can make data-driven decisions with confidence and on a sound basis. This step accompanies the transformation of raw data into strategically relevant smart data. The following section presents practical examples and best practices that facilitate the use of data analysis and maximise the benefits.
Data analysis in KIROI step 3: From raw material to strategic asset
In the third step of the KIROI methodology, the focus is on generating smart, focussed information from extensive big data inventories. The challenge is not only to collect data, but also to process it in a targeted manner and check its relevance. Companies from different industries are supported in mastering data analysis in order to improve their processes and secure competitive advantages.
The industrial sector is an example of how sensors on production machines continuously supply data. With the help of intelligent data analysis, malfunctions are detected at an early stage and maintenance cycles are optimised. This reduces downtimes and saves costs.
In the retail sector, companies analyse purchasing behaviour and inventory data in order to make targeted recommendations. This results in personalised offers that increase customer satisfaction and sales.
In the healthcare sector, too, the structured analysis of large patient data sets supports the early detection of diseases and enables customised therapies.
Practical implementation of data analysis
In order for data analysis to be effective, decision-makers should pay attention to several key factors:
- Data quality: Only valid and consistent data provides reliable findings.
- Goal-orientation: Clear questions help to focus on relevant evaluations.
- Infrastructure: The technical equipment must be adapted to the data volume and complexity, for example cloud services or specialised databases.
- Interdisciplinary teams: The combination of technical expertise and professional understanding strengthens analytical work.
- Validation: Analysis models should be checked regularly to ensure accuracy and validity.
In logistics, one company was able to improve delivery times by 20 per cent thanks to these measures. In marketing, agencies reported that targeted data analysis contributes to significantly better conversion rates.
Methodological diversity in data analysis
Data analysis uses various methods to recognise patterns, correlations and trends. Classic statistical methods such as regression and variance analysis help to explore relationships between variables.
For example, a financial services provider uses regression analyses to identify cases of fraud. Analysing group differences in customer segments supports customised offers.
Dynamic methods such as time series analyses record changes over time, which contributes to early fault detection in production. Fourier transforms are also used in the energy industry to analyse fluctuations in consumption.
Modern approaches combine these traditional methods with AI-based systems. Artificial intelligence speeds up analysis and finds complex correlations that remain hidden from human analysts. This is used in quality control in mechanical engineering, for example, to identify production errors more precisely.
BEST PRACTICE with one customer (name hidden due to NDA contract)
BEST PRACTICE with one customer (name hidden due to NDA contract) In a manufacturing company, KIROI supported the introduction of a data analytics system for analysing machine data. Inefficient production steps were identified through targeted filtering and cleansing of the large volumes of data. The company received impulses for the selection of suitable analysis tools and was closely supported during implementation so that it could carry out its own analyses independently in future.
Recommendations for decision-makers when dealing with data analysis
Decision-makers should understand data analysis as a continuous process. This includes testing the appropriate tools at an early stage and developing a clear strategy.
Practical tests can be used to check the user-friendliness, scalability and integration of new systems. In the financial sector, a structured tool test meant that risk analyses could be implemented more quickly and reliably. Supply chains in logistics benefit from optimised planning tools based on sound analyses.
In addition, KIROI supports the development of analysis skills within the team so that technical and specialist departments can work together effectively. This ensures that data analysis becomes a real basis for decision-making.
Best practice from customer support
BEST PRACTICE with one customer (name hidden due to NDA contract) A logistics company used the KIROI methodology to optimise its transport planning through careful data cleansing and intelligent data analysis. Customer enquiries were processed more quickly and delivery times were noticeably reduced. The team was continuously supported in order to strengthen its own analysis skills in a targeted manner.
My analysis
Targeted data analysis forms an important basis for well-founded decision-making processes. KIROI Step 3 supports decision-makers in processing large amounts of data in a meaningful way and transforming it into smart information. Practical examples from industry, retail and healthcare illustrate how data-driven methods can increase productivity, reduce costs and improve customer focus. Decision-makers benefit from clear recommendations for action, proven methods and support from expert teams. This makes data analysis a valuable tool for successfully leading organisations into a data-based future.
Further links from the text above:
[1] Mastering data analysis: KIROI step 3 with big & smart data
[2] Mastering data analysis: KIROI Step 3 to Smart & Big Data
[4] Classic and AI-based data analysis
[5] Mastering data analysis: Step 3 to smart data with KIROI
[7] AI Data Analysis: How to analyse data with AI - IONOS
[10] Testing the tool: How step 2 of the KIROI methodology works
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