Data analysis plays a central role in the modern corporate world, especially when it comes to mastering big data and gaining strategic advantages from it. Many organisations are faced with the challenge of analysing huge amounts of data in a meaningful way and extracting smart, usable information from it. KIROI Step 3 offers a structured method for utilising Big & Smart Data in a targeted manner and thus supporting data-driven decisions.
Data analysis as the key to understanding big data
Big data describes the accumulation of huge, heterogeneous data sets from a wide variety of sources. The sheer volume often presents companies with technical and methodological challenges. Data analysis** makes it possible to extract relevant patterns, correlations and trends from this abundance and transform them into smart data sets (smart data). This provides information that can be used specifically for the optimisation of processes or strategic decisions[1][3][4].
An example from industry shows how sensor data from production machines is evaluated using data analysis in order to predict failures and optimise maintenance intervals. In retail, the analysis of purchasing behaviour is used to personalise recommendations, which leads to increased sales. In the healthcare sector, the analysis of large patient data sets also supports the early detection of illnesses and the development of personalised therapies.
KIROI step 3: From big data to smart data with targeted data analysis
The third step in the KIROI model focusses on not only collecting large amounts of data, but also intelligently refining it. By using methods such as data mining, machine learning and statistical processes, big data is transformed into smart data. This means that data can be used not only as raw material, but also as a targeted basis for knowledge[1][5][6].
In the energy supply sector, for example, companies analyse consumption data in real time and thus create the basis for smart grids. Telecommunications providers evaluate network loads in order to dynamically adjust capacities and minimise downtimes. Logistics companies also benefit from the evaluation of transport data, which optimises supply chains and reduces costs.
BEST PRACTICE with one customer (name hidden due to NDA contract) In the manufacturing industry, we were able to reduce production downtimes by 15 % through targeted data analyses. By integrating smart data, maintenance cycles were planned more precisely, which reduced costs and significantly increased plant availability.
Practical tips for successful data analysis
The following points should be taken into account to ensure that data analysis provides effective support:
- Ensure data quality: Only valid and consistent data leads to usable results.
- Define goal-orientated questions in order to gain relevant knowledge.
- Select a technical infrastructure that matches the data volume and complexity, e.g. cloud solutions or specialised databases.
- Deploy interdisciplinary teams that combine both technical and specialist expertise.
- Regularly validate the analysis models to ensure validity and accuracy.
For example, one logistics company has improved its delivery times by 20 % and processed customer enquiries faster by using these measures. In the marketing sector, agencies report that personalised campaigns significantly increase conversion rates through smart data analysis.
Data analysis as a companion for projects with big & smart data
The integration of data analyses into corporate projects relating to Big & Smart Data is a supporting tool that accompanies and improves many processes. The implementation of new data-driven applications in particular shows how important well-founded analyses are in order to minimise risks and make better use of opportunities. Topics such as data integration, data security and data protection are often addressed[3][7].
An example from the healthcare sector illustrates how data analyses can be used to make hospital processes more efficient and reduce treatment times. In the transport sector, cities use data analyses to optimise traffic flows and reduce environmental pollution. In the financial sector, the analysis of large transaction data supports fraud detection and risk assessment.
BEST PRACTICE with one customer (name hidden due to NDA contract) from the healthcare sector, patient throughput times were reduced through data analytical support. This enabled better utilisation of resources, which significantly improved patient care.
My analysis
The importance of **data analysis** in dealing with big and smart data is undisputed. It enables companies to gain targeted insights from complex data sets and thus make well-founded decisions. KIROI Step 3 provides valuable support here in order to effectively refine data sets. Many industries have shown that the integration of data analytics expertise stabilises processes and secures competitive advantages. Companies that take this approach support their sustainable development and innovative capacity.
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 - DLR
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