The world of data analysis presents companies with a variety of challenges, especially when it comes to handling large volumes of data. Big & Smart Data plays a decisive role in step 3 of the KIROI method. It is not just about the sheer volume of information, but also about analysing it intelligently and using it in a targeted manner for the corporate strategy. Data analysis helps to recognise hidden patterns and make well-founded decisions.
Big data and smart data - the basis and compass for successful data analysis
While big data describes the collection and storage of enormous amounts of data, smart data focuses on refining this raw data in a meaningful way and using it in a targeted manner. For example, e-commerce companies collect millions of transaction data (big data) and then convert this into clear purchase recommendations (smart data) in order to strengthen customer loyalty and increase sales[3][4].
In manufacturing, sensor data from machines (big data) serves as the basis for precise process optimisation. Here, data analysis methods help to recognise anomalies at an early stage and plan maintenance work efficiently[2][6]. Data analysis thus becomes a tool for higher production quality and cost savings.
The relevance of big and smart data is also becoming clear in the healthcare sector. Large-scale data from patient records and wearables enable personalised therapies and strengthen preventive care. Data analysis often reveals patterns that can provide important impetus for diagnoses and treatment strategies[4][3].
How data analysis transforms big data into smart data
The path from big to smart data is not a sure-fire success. Companies need clear goals, suitable technologies and qualified experts in order to extract meaningful information from the flood of data. Multi-stage processing using methods such as data mining, machine learning and statistical analyses is important[1][6].
BEST PRACTICE with one customer (name hidden due to NDA contract) The collaboration focused on the implementation of a data analytics system that analyses production data in real time. This enabled anomalies to be identified at an early stage and repair cycles to be optimised, resulting in a measurable increase in plant availability. The data analysis supported the process managers in making well-founded decisions.
Logistics companies also benefit from data analysis. The evaluation of tracking information makes it possible to optimise delivery processes, dynamically adapt routes and shorten delivery times. The transformation of big data into smart data makes this possible by enabling precise interpretation of usage data[1][5].
Technical and methodological foundations for smart data analysis
The technical infrastructure forms the basis for meeting the requirements of Big & Smart Data. Cloud storage, scalable databases and powerful analytics platforms are essential to ensure that data volumes can be processed efficiently[3][6].
Methodologically, data analysis is based on various levels: from descriptive analysis, which summarises data, to predictive models, which forecast future developments, to prescriptive analysis, which derives specific recommendations for action[8].
A telecommunications provider, for example, uses these stages to predict customer churn and increase customer satisfaction with targeted offers. Both user data and external influences are included in the analysis[1][8].
Analyses of traffic and environmental data are also used in the area of smart cities. The intelligent processing of this information helps cities to develop sustainable mobility concepts and improve the quality of life[7].
Challenges and opportunities in the implementation of data analysis projects
Despite the many advantages, dealing with big and smart data also brings challenges. Data protection and data security are key issues, especially when it comes to sensitive personal data. Transparency and clear regulations are essential in order to create trust and comply with legal requirements[3].
Another issue is the skills that are often still lacking in the company. Clients often report that the integration of data analysis processes requires long-term support and qualified advice in order to fully utilise the potential. This is where professional support can provide impetus and impart tried and tested methods that specifically help with the transformation from big to smart data[5][6].
In addition, the art lies in selecting the right data. Because not all information contributes to the goal. The focus is on filtering out from the wealth of available data series those that are relevant to the respective business and can provide meaningful insights[2][8].
My analysis
Successful mastery of data analysis begins with the understanding that big data alone does not improve decisions. It is the transformation to smart data that makes the difference. This requires both technical expertise and a strategic focus that is aligned with specific business objectives. Companies from a wide range of industries can benefit from this approach - be it manufacturing, retail, healthcare or public administration.
Data analysis helps to make complex relationships understandable and to visualise the benefits of large amounts of data. The integration of intelligent analysis technologies supports companies on their way to making progress measurable and securing competitive advantages.
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 at DLR
Data Analytics - Fraunhofer SCS
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