Data analysis as a key to gaining insight
Data analysis plays a central role in current Smart & Big Data projects, enabling the extraction of relevant insights from extensive data sets. It helps companies to recognise trends and make more informed decisions. Data analysis is not a singular process, but a sequential procedure where data is first carefully selected, then cleansed and transformed before it can be analysed.
As part of guided coaching, it often becomes apparent that employees from various industries approach us with the question of how they can master the complexity of large amounts of data. Clients frequently report that they have difficulty assessing the relevance of data correctly and finding the right analytical methods for their project.
Data preparation as the foundation for successful analyses
It is important to prepare the data for use even before the actual analysis. Thorough data cleansing is particularly time-consuming. Here, irrelevant or erroneous information is sorted out, such as spam data or incorrect entries. This is followed by data transformation, where heterogeneous data formats are standardised to make them comparable and analysable.
An example from the logistics industry shows how transport times could be better predicted through careful data cleansing and transformation, leading to more efficient route planning and cost savings.
In healthcare, a client reported that consolidating patient data from various sources prior to analysis helped them identify patterns in treatment success rates that were previously hidden.
The financial industry also uses these steps, by processing transaction data to detect and respond to fraudulent activities early on.
Numerous steps to mastering data analysis
Data analysis involves more than just evaluating individual numbers. After data preparation comes the actual analysis, often supported by machine learning algorithms. These methods make it possible to discover patterns, correlations, or anomalies in the volume of data that are hardly discernible through manual evaluation.
In the manufacturing industry, the analysis of machine data has led to more precise planning of maintenance work and a reduction in downtime, which has lowered production costs.
In marketing, data-driven insights frequently provide new impetus on how campaigns can be tailored more precisely to specific customer segments. For example, individual projects that benefited from data analysis support recorded a significant improvement in customer satisfaction.
Retailers use data analysis to better understand purchasing behaviour and to align their product assortments accordingly. In supervised projects, it was also demonstrated how valuable optimal data analysis can be in expanding market share.
KIROI BEST PRACTICE at company XYZ (name changed due to NDA contract)
As part of a smart data project, KIROI supported a company that collects large amounts of sensor data from manufacturing. Through targeted data cleansing and subsequent analysis, inefficient production steps were identified and optimised. The project team received continuous input and was guided in selecting suitable tools, enabling them to conduct further analyses independently.
KIROI BEST PRACTICE at ABC (name changed due to NDA contract)
A Big Data project helped a service provider that manages marketing campaigns for various industries. KIROI supported the team in systematically segmenting large volumes of customer data. The insights gained were used to purposefully adapt the content strategy, which improved customer loyalty.
KIROI BEST PRACTICE at DEF (name changed due to NDA contract)
In a health project, KIROI supported the networking of various data sources. The aim was to develop early warning systems for at-risk patients using data analysis methods. The project received impetus from KIROI regarding the selection of analysis algorithms and received support in interpreting the results.
Support and assistance as a success factor
Data analysis doesn't have to be a solitary pursuit. Practical experience shows that guidance from experienced coaches offers numerous benefits. This allows for the targeted use of appropriate methods and tools, while simultaneously promoting the professional interpretation of results. This increases acceptance within the team and makes the implementation of data-driven measures more successful.
This involves considering the variety of data formats, the speed of data generation, and the vast amounts of data – precisely the challenges that Smart & Big Data projects entail. Sound data analysis helps to meet these challenges by providing crucial answers that can be used to optimise processes and strategies.
My analysis
The core principle is the understanding that data analysis is a methodical, multi-stage process characterised by preparation, transformation, and targeted evaluation. Projects benefit from support that considers the application context alongside technical aspects and provides impetus for practical solutions. This is evident in a wide range of industries, from manufacturing and marketing to healthcare, which tackle their challenges and further develop their business models with the help of data analysis.
Further links from the text above:
[1] How knowledge emerges from Big Data in 7 steps
[4] What is Big Data analysis?
[7] Big Data: Definition, Analytics, Technologies, Tools
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