Today, data analysis is an indispensable tool for companies that want to base their decisions on well-founded findings. Decision-makers in particular are faced with the challenge of efficiently processing large and complex amounts of data and gaining real impetus for action in the process. Step 3 in the KIROI process shows in a practical way how this task can be mastered systematically and how data-based strategies can have a lasting effect.
Understanding and processing data analysis
In many companies, mastering data analysis only begins with the accurate preparation of the data. This phase is fundamental, as it largely determines how reliable and meaningful the subsequent analyses will be.
Practical example: In a production plant, the systematic cleansing of machine data helped to recognise and correct incorrect sensor values. This meant that the analysis was not distorted, which made it possible to plan precise maintenance work.
Clean customer data is also a basis for marketing: after consolidating contact data from various systems, a service provider was able to design personalised campaigns in a more targeted manner and measurably improve customer loyalty.
In retail, data processing facilitates the analysis of purchasing behaviour. A bricks-and-mortar shop analysed data from checkout systems together with online tracking in order to optimise its product range and product placement.
Tips for decision-makers on data preparation
- Clarify with your teams which data sources are relevant and how data formats can be standardised.
- Use data cleansing tools that automatically recognise duplicates and inconsistencies.
- Promote the documentation of data origin and meaning so that specialist departments can understand the key figures.
AI-supported data analysis in practice
Step 3 of the KIROI process goes beyond traditional data preparation. The use of modern methods such as artificial intelligence (AI) and machine learning opens up new perspectives for automatically recognising patterns and correlations.
Example from manufacturing: A company used smart data and ML algorithms to analyse sensor data in such a way that production bottlenecks became visible at an early stage. The result was a significant reduction in downtime and better resource planning.
In the area of marketing, customer data was analysed using NLP technologies based on AI in order to recognise customer sentiment in reviews. This supported the campaign team in the targeted customisation of content.
A healthcare project benefited from the networking of heterogeneous data sources in order to identify at-risk patients at an early stage using predictive models. Data analysis thus made an important contribution to better patient care.
Recommendations for the integration of AI in data analysis
- Start with clearly defined questions so that AI methods can be used in a targeted manner.
- Ensure that your team is continuously trained to implement technological innovations.
- Choose scalable tools that keep pace with growing data volumes.
Communication of analysis results for decision-makers
The best data analysis is of little use if the results are not communicated in an understandable way. Decision-makers need clear, actionable insights, not just columns of figures.
In the automotive industry, data-analytical visualisations were used to identify which components show increased failure rates. This enabled targeted quality improvement.
In retail, clear dashboards lead to faster decisions when adjusting product ranges.
A consultancy firm used storytelling elements to present its analysis results to clients in such a way that they could be used directly for strategic planning.
Practical tips for the presentation of results
- Use clear visualisations to illustrate trends and correlations.
- Contextualise figures with examples from the operational business.
- Offer options for action that can be derived from the data.
BEST PRACTICE with one customer (name hidden due to NDA contract) As part of a smart data project, KIROI supported a company that collects large amounts of sensor data from production. Through targeted data cleansing and subsequent analysis, inefficient production steps were identified and optimised. The project team received continuous impetus and was supported in the selection of suitable tools so that they could carry out further analyses independently.
Decision-makers benefit from systematic support in step 3 of the KIROI model. This means that data analysis is not an end in itself, but a real support for the business.
My analysis
Data analysis enables companies to gain meaningful insights from extensive and often confusing data. The third step in the KIROI process shows how data analysis can be successfully mastered through careful data preparation, the use of modern AI technologies and targeted communication. This enables decision-makers to manage projects more effectively and provide sustainable impetus for growth. In this way, data analysis becomes a real competitive advantage.
Further links from the text above:
The 6 steps of data analysis - Martin Grellmann
Mastering data analysis: KIROI Step 3 to Smart & Big Data
Steps in data analysis - Modern statistics
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