Data analysis** is at the heart of modern corporate strategies and project support when dealing with large amounts of data. The successful handling of data is a key challenge, particularly in the context of complex processes relating to big and smart data. This is precisely where KIROI Step 3 comes in and offers structured support to overcome this hurdle. This article shows in a practical way how companies and teams can use their data efficiently with the help of targeted support and thus create sustainable added value.
The basics of data analysis: from raw data jungle to clear insights
Data analysis does not begin with complex algorithms, but with targeted data preparation. Large amounts of data, such as those generated today in industries ranging from manufacturing to e-commerce, often contain unstructured and heterogeneous information. This makes direct analysis difficult.
It is therefore essential to filter, clean and convert the data into usable formats as a first step. For example, data preparers in the automotive industry help to clean vehicle sensor data and recognise incorrect values. In retail, on the other hand, structured sales and customer data is used to carry out trend analyses and adjust product portfolios. This turns a more or less confusing mountain of data into structured information that can be further processed using traditional methods and AI-supported analyses.
The combination of big data, i.e. large volumes of data, and smart data, i.e. the targeted selection and utilisation of information, is of fundamental importance for modern data analysis projects. While big data describes quantity, smart data focuses on relevance and quality.
Data analysis in practice: examples from different industries
In mechanical engineering, the targeted analysis of operating and sensor data enables predictive maintenance. This enables companies to identify potential machine failures at an early stage and optimise the timing of maintenance work. This reduces downtimes and saves costs.
In the area of marketing, companies analyse customer data in order to personalise campaigns. Retailers use these insights to tailor seasonal product recommendations and cater to individual customer preferences, for example.
In the healthcare sector, data analysis supports the early detection of diseases. For example, doctors can derive risk profiles from extensive patient data and develop customised treatment strategies.
KIROI's method for data analysis: Structured support for Big & Smart Data projects
In the third step, KIROI's support focusses on the concrete implementation and practical application of data analysis. This enables companies to organise their data projects in a methodically secure and goal-oriented manner. This means, among other things, that specific challenges, such as data integration or visualisation, are systematically tackled.
For example, KIROI supports manufacturing companies in analysing production data in such a way that quality deviations are detected at an early stage. This allows processes to be improved and reject rates to be reduced. In logistics, the use of smart data analyses helps to optimise supply chains, for example by improving delivery time planning through accurate data forecasts.
KIROI monitoring is also used in the financial services sector. Banks and insurance companies use data analysis to recognise fraud-relevant patterns and better assess risks. The structured support promotes efficient and transparent implementation.
BEST PRACTICE with one customer (name hidden due to NDA contract) A medium-sized electronics manufacturer used KIROI to introduce a comprehensive data analysis system that integrates production and quality data from various departments. This enabled patterns of unexpected defects to be quickly identified and corresponding processes to be adapted. The project resulted in significantly less downtime and improved product quality.
Practical tips for successful data analysis
Interdisciplinary collaboration is a success factor in data analysis. Technical, specialist and IT teams should be closely involved in order to realistically address data challenges and develop practical solutions.
It is also important to define clear questions right from the start. This makes it possible to determine which data is really valuable in a targeted manner. Focussing avoids an overload of irrelevant information.
The use of modern visualisation tools also pays off. These make results tangible and support the communication of findings to various stakeholders, for example in management or specialist departments.
Finally, data analysis should be understood as an iterative process. Regular feedback and adjustments continuously improve both methods and results.
My analysis
**Data analysis** is essential today in order to remain competitive in a data-driven world. With structured support, such as that offered by KIROI in the third step, companies can not only master their big data projects, but also generate sustainable value. The combination of state-of-the-art technology, methodical support and practical examples from various industries demonstrates how diverse and beneficial this analysis is.
Those who consistently process data, evaluate it in a targeted manner and communicate it in an understandable way receive impetus for better decisions, process optimisation and innovative business models. The focus on smart data helps to filter out the information with the greatest added value.
Further links from the text above:
Smart + Big Data | Artificial Intelligence
What does smart data mean and what are the application scenarios?
Big and smart data - from statistics to data analysis
Big Data Analytics - Methods and Applications (University of Ulm)
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