Data analysis: Rethinking between big data and smart data
The challenges of data analysis are constantly growing with the amount of data. Companies often report the problem that big data does not always deliver the desired insights despite the huge volume. This is why a new approach that emphasises the quality and speed of data is gaining in importance: Smart Data. Decision-makers are increasingly asking how they can rethink data analysis in order to achieve better, more targeted results and successfully support their projects.
Big data and smart data: a difference in approach
Big data stands for the processing of large, complex data sets with a focus on quantity and variety. Smart data, on the other hand, aims to filter and select these huge amounts of data and only filter out the really relevant, high-quality information. This results in clearer, actionable insights that can significantly support decision-making processes. While big data often requires complex processing and many resources, smart data can also be processed in real time, which brings immediate benefits for companies.
Many companies realise that collecting massive amounts of data does not automatically guarantee better decisions. Quite the opposite: unfiltered big data can be overwhelming and lead to misinterpretations. This is why clients often report that support in analysing data and rethinking smart data provides valuable impetus to work in a more targeted manner.
Targeted data analysis for greater efficiency
Targeted data analysis helps companies to utilise their resources more efficiently. Smart data makes it possible, for example, to identify trends in customer behaviour more quickly and precisely target marketing measures. The quality of the data is crucial here. Experience shows that inaccurate data can lead to bad investments - for example, if advertising is played out to unsuitable target groups.
KIROI BEST PRACTICE at company X (name changed due to NDA contract) In a manufacturing company, the material flow was optimised by carefully filtering the data from the logistics department and using smart data. This enabled bottlenecks to be recognised more quickly and interruptions reduced. The company reports a significantly better basis for decision-making and less wasted resources.
The benefits are also evident in the financial sector: Only relevant and high-quality data leads to better risk assessments and portfolio decisions. In this way, experts avoid being blinded by superfluous amounts of data and instead create added value through clear insights.
Smart data: customisation to individual needs
One of the key strengths of smart data is that it can be customised to specific industry requirements. A large company in the retail sector needs different data than one in the energy sector. When redesigning data analysis, these special features are taken into account so that decisions are not only data-based but also contextualised.
KIROI BEST PRACTICE at service provider Y (name changed due to NDA contract) A service company uses smart data to increase customer satisfaction. After targeted data processing, this information could be used to optimise service processes. The data analysis provides employees with valuable impetus to better understand customer needs and respond individually.
This specific focus is a key component when rethinking data analysis. Clients report that this customisation to their own needs facilitates collaboration and implementation.
Real-time decisions thanks to smart data
Fast decisions are often crucial in manufacturing or retail. Smart data can be processed in real time. This enables immediate reactions to market changes or internal process deviations. This allows companies to remain flexible and ensure competitiveness.
KIROI BEST PRACTICE at industrial company Z (name changed due to NDA contract) An industrial company uses smart data to monitor production data in real time. Deviations are recognised immediately and messages are sent to quality management. This has minimised downtimes and significantly improved product quality.
Rethinking data analysis with coaching processes
The transition from big data to smart data is best achieved with accompanying support. KIROI coaching offers practical support on request. It provides impetus for the development of a sustainable understanding of data and promotes the transfer to everyday work processes. In this way, data analysis is rethought - more targeted and more successful. Clients often report a significant gain in clarity and structure in the implementation of their projects.
The support helps companies to make data-driven decisions without being overwhelmed by the wealth of data. The coaching helps you to ask the right questions, set priorities and make optimum use of technical solutions. This turns data into real added value.
My analysis
Today, data analysis means making the transition from pure data masses to targeted, high-quality information. The combination of smart data and coaching services can support decision-makers in making better decisions and optimising operational processes. The practical examples show how companies from a wide range of industries work more efficiently with this approach. This makes it clear that rethinking data analysis means focussing on quality, individuality and speed of action.
Further links from the text above:
[1] Big Data vs. Smart Data: Is More Always Better?
[2] Big Data to Smart Data | The evolution of data science and AI
[4] Big Data vs. Smart Data: Key Insights for Operational Optimisation
[7] Big Data vs. Smart Data: Valuable Insights to Optimise
For more information and if you have any questions, please contact Data analysis or read more blog posts on the topic Data analysis here.
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