Nowadays, data analysis is becoming increasingly important as companies collect and process ever larger volumes of data. KIROI Step 3 is dedicated to precisely this topic: how can big and smart data not only be collected, but also utilised in a targeted manner and intelligently refined? The consistent application of modern methods helps to gain valuable insights for strategic decisions from the often complex data and to effectively support projects.
Data analysis: from simple collection to intelligent utilisation
Many companies are currently facing the challenge of managing large amounts of data from a wide variety of sources. Sales figures, social media interactions and external economic data are just a few examples. The crucial step is not only to store this raw data, but also to ensure its quality and filter out relevant information. Data analysis supports the optimisation of processes by making patterns, correlations and trends visible.
A practical example from the retail sector shows how stock management can be organised more efficiently by linking customer data with weather information. This makes it possible to plan targeted marketing campaigns that respond to seasonal fluctuations. Companies from the logistics sector also report that better route planning has been realised thanks to real-time data, resulting in more flexible and resource-efficient management.
Data analysis methods also facilitate the selection of relevant sources for clinical studies in the pharmaceutical industry. This reduces the time and effort required for analyses, while at the same time increasing the informative value of the results. In this way, complex data is prepared in an understandable way and well-founded decisions can be made.
KIROI Step 3: The practice of data analysis with Big & Smart Data
The third step of the KIROI model is primarily concerned with the specific implementation of the data analysis. Modern algorithms and statistical methods are used here. In addition to pure data processing, visualisation tools also play a key role in presenting findings clearly and concisely.
A real-life example shows how a company was able to better forecast seasonal fluctuations through the data-based identification of sales trends. As a result, the marketing department developed targeted campaigns, stock levels were optimised and costs were saved.
In addition, more and more companies are relying on artificial intelligence, which recognises patterns, correlations and irregularities in a matter of seconds. This helps to analyse large volumes of data more efficiently than would be possible manually. This enables faster decision-making and better planning.
BEST PRACTICE with one customer (name hidden due to NDA contract) In a data-driven project in the field of product development, the support helped to organise data flows and set priorities. This prevented teams from being overwhelmed by pure technological complexity. The insights gained were gradually incorporated into innovation processes and provided targeted support for management in making sustainable decisions.
Relevance of data quality and process expertise
An important issue when analysing data is the quality of the data used. Quality is a decisive factor in the validity of the results: „Garbage in - garbage out“ remains the basic principle. This is why the third KIROI step also includes careful data pre-processing in order to minimise errors and base the analysis on reliable data sets.
Process knowledge is just as crucial. Different applications require suitable analysis methods. Static methods are not necessarily suitable for highly dynamic data. For example, sensor data in production may require different analyses than customer data in marketing.
In the area of customer loyalty, data analysis helps to discover behavioural patterns and tailor offers individually. In the financial sector, they support risk management by recognising anomalies at an early stage. And manufacturing companies use data-based process analyses to optimise their production steps and reduce downtimes.
Data analysis to accompany sustainable projects
The biggest challenge often lies in integrating the complexity of data analysis into everyday project work. Support from experienced coaches helps with this by teaching relevant methods and focussing on the most important key figures. This keeps the topic tangible and enables employees to gain well-founded insights.
In marketing, numerous teams report that data-based findings lead to better campaign management. In healthcare, data analyses help to make patient flows more efficient. And in logistics, they underpin precise forecasts on demand trends.
BEST PRACTICE with one customer (name hidden due to NDA contract) Real-time data integration was implemented as part of a logistics project. The support ensured that relevant influencing factors were identified and route management was made more flexible. The result was improved resource utilisation and significantly increased confidence in the company's own data-based decisions.
My analysis
Data analysis is more than just collecting information. It thrives on the intelligent refinement of large amounts of data and its targeted application in practice. KIROI Step 3 shows how the interplay of algorithms, visualisation and sound process knowledge leads to better decisions. Companies that rethink their data analysis create valuable competitive advantages and master challenges more efficiently.
Further links from the text above:
Data analysis as a foundation for better decisions
AI Data Analysis: How to analyse data with AI - IONOS
Mastering data analysis: KIROI step 3 with big & smart data
Classic and AI-based data analysis
Mastering data analysis: KIROI Step 3 - Big & Smart Data
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