In the age of digitalisation, data analysis is becoming increasingly important. Intelligent methods are required to gain relevant insights, especially when large amounts of data from a wide variety of sources are involved. The combination of big data and smart data offers enormous potential for optimising business processes and making well-founded decisions. In this article, you will learn how these concepts can be effectively combined and how transruptions coaching can provide support in this regard.
Data analysis: From mass to insight - Big & Smart Data at a glance
Big data describes the collection of enormously large and diverse data sets from a wide variety of sources. This mass of information is initially unstructured and can only be utilised to a limited extent. This is where data analysis comes to the fore in order to discover patterns and correlations. However, the sheer volume alone is not enough to work efficiently. This is why smart data is gaining in importance - specifically prepared and quality-checked data records that provide information relevant to decision-making.
For example, online retailers use big data to observe customer behaviour, identify purchasing patterns and dynamically adapt product recommendations. The subsequent smart data-supported analysis then makes it possible to develop customised marketing campaigns that create lasting customer loyalty. In industry, data analysis plays an important role in improving production processes. Machine and sensor data is collected in order to identify process fluctuations and minimise downtime through targeted measures. Another example is the transport sector. Here, the intelligent evaluation of large volumes of data helps to optimise traffic flows and support the development of smart mobility concepts.
The path from big data to smart data in practice
Simply collecting data is just the beginning. The data must be structured, cleansed and analysed using advanced statistical and machine learning methods. Irrelevant or redundant information is filtered out and the valuable data points are extracted. The result is smart data that provides clear recommendations for action.
In the healthcare sector, for example, smart data analysis can be used to recognise at an early stage which patients are at increased risk of certain diseases. Hospitals use such findings to make treatment plans more precise. At the same time, they enable better resource planning. In the energy sector, smart data processes are used to analyse consumption patterns and thus make energy supply more sustainable. Logistics is also benefiting from this development: optimised route planning based on smart data reduces costs and leads to faster deliveries.
Tools and platforms that rely on modern cloud technologies and distributed databases are an integral part of these processes. These enable the efficient storage and processing of large amounts of data in real time, which is essential for sensor technology in production facilities, for example.
Practical examples of data-based optimisation with transruption coaching
Many companies are faced with the challenge of utilising their large volumes of data effectively. This is precisely where transruptions coaching comes in to support teams and managers on their journey from data collection to data-based decision-making.
BEST PRACTICE with one customer (name hidden due to NDA contract) The introduction of smart analyses in a manufacturing company led to a significant reduction in reject rates. The coaching helped to overcome technical barriers in data processing and helped the workforce to effectively interpret and implement data-driven findings. This enabled process optimisations to be introduced in real time, which significantly increased product quality.
BEST PRACTICE with one customer (name hidden due to NDA contract) In a service company, the coaching helped to recognise the potential of large amounts of customer data for targeted marketing campaigns. Through the consistent use of smart data, customer segments were defined more precisely, which improved the ROI of campaigns and increased customer loyalty.
BEST PRACTICE with one customer (name hidden due to NDA contract) A logistics service provider used data-based analysis to optimise supply chain management. The coaching helped to select suitable methods and integrate the analysis results into daily practice. Building on this, routes were adjusted and stock levels managed more efficiently, resulting in tangible savings.
Recommendations for the successful use of big and smart data
For successful data analysis, it is important to define clear objectives. Only when it is clear which insights are to be gained can the data be selectively analysed. It is also advisable to use interdisciplinary teams that contribute both technical know-how and specialist expertise.
Companies should also rely on modern analysis tools and AI-supported processes in order to recognise patterns more quickly and improve forecasts. Data protection and data security must also be taken into account to ensure that large amounts of data are used responsibly and that applicable legal requirements are complied with.
Transruption coaching can provide impetus here by developing concrete implementation strategies and promoting an understanding of data-based processes. This results in a sustainable change in the corporate culture towards more data-driven action.
My analysis
The combination of big data and smart data opens up new opportunities for companies to make informed, data-based decisions. With targeted data analysis, processes can be optimised, customers better understood and future-oriented strategies developed. The intelligent processing of data plays a central role in deriving real impulses for action from the mass of information. Transruption coaching effectively supports this change and accompanies organisations on the path to data-based excellence.
Further links from the text above:
Smart + Big Data | Artificial Intelligence
Big and smart data - from statistics to data analysis
Glossary - Big Data
Smart data: definition, application and difference to big data
Making decisions with smart data
Data analysis: from big data to smart data
Big and smart data
Data analytics: Data and methods - Fraunhofer SCS
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