Rethinking data analysis means not only collecting large amounts of data, but also using it in a targeted and intelligent way. The transformation from big data to smart data is at the centre of this - a process that helps companies to gain valuable insights and support data-driven decisions in a well-founded manner. KIROI Step 3 offers structured support to successfully master this challenging phase in data management.
From big data to smart data: a new approach to data analysis
Big data stands for massive, diverse and rapidly emerging data sets from a wide variety of sources. The challenge lies in processing this mass of raw data in such a way that it is transformed into valuable information - that is smart data. In the KIROI process, the third step means supporting this transformation in a targeted manner while always keeping an eye on benefits, data quality and data protection.
Practical examples show how companies can benefit from this approach. A retailer can use smart data to precisely analyse purchasing behaviour, identify seasonal trends and thus dynamically adapt its product range. A logistics company, on the other hand, uses intelligent data analysis to optimise routes in real time and reduce costs. In marketing, high-quality data helps to finely segment customer profiles and precisely control personalised campaigns.
BEST PRACTICE with one customer (name hidden due to NDA contract) was to support the switch from conventional data management to smart real-time data processing. This gave this customer the opportunity to react faster and more precisely to changes in the market - with clearly measurable success in terms of increased sales and efficiency gains.
Methodological aspects of modern data analysis in KIROI step 3
The third step focusses on several core processes: Data preparation, pattern recognition and semantic analysis. Raw data is structured, errors are corrected and missing values are added. This is followed by the application of algorithms and machine learning, which are able to discover hidden correlations. This creates relevant knowledge from seemingly confusing information.
In practice, for a manufacturing company, for example, this means that production data is continuously analysed in order to predict downtimes and better plan maintenance cycles. An e-commerce provider uses similar methods to systematically recognise abandoned purchases and reduce them through targeted measures. In the healthcare sector, smart data analysis leads to better control of patient flows or data-based evaluation of treatment results.
BEST PRACTICE with one customer (name hidden due to NDA contract) The implementation of an automated anomaly detection system in production data was accompanied by a project. This enabled early warning of machine failures and significantly reduced unexpected downtimes.
KIROI's practical support in the development of smart data
KIROI's coaching support ensures that everyone involved understands the complexity of data analysis and can confidently implement it in their projects. A key element is the close collaboration between data experts and the operational teams in order to communicate requirements and results transparently.
For example, KIROI coaching supports a start-up in developing its data-driven marketing strategy by helping it to identify the right data sources and select analysis methods. Another client from the financial sector reports that the support in developing a dashboard for risk analyses significantly accelerated the decision-making processes. An industrial company uses the consultancy to harness customer data for personalised after-sales services.
BEST PRACTICE with one customer (name hidden due to NDA contract) KIROI supported the project team in developing an interdisciplinary understanding of data processes and confidently managing the transformation from big data to smart data - with sustainable knowledge transfer and continuous development.
Recommendations for successful data analysis
It is advisable for companies to first define clear questions. Data analysis works particularly well when it is focussed on specific problems or opportunities. Another important tip is to ensure the quality of the data. Correctly prepared data is a prerequisite for achieving reliable results.
In addition, the results should be presented in an understandable and accessible way. Visualisations and dashboards help to give even non-experts access to the findings. This creates a common understanding that promotes the implementation of data-based measures.
In practice, this can mean that a service provider analyses customer satisfaction data and communicates the findings to the service team in a manageable way. A retail company uses visual analyses of sales figures to dynamically adapt the product range. An insurance company uses data models to develop personalised offers based on actual customer needs.
My analysis
Rethinking data analysis and taking the step from big data to smart data is a strategic success driver for many industries. The combination of quality-assured data, intelligent analysis and practical support, as implemented in KIROI Step 3, creates transparency and enables well-founded decisions. Companies that support this development can provide impetus, optimise processes and secure competitive advantages. At the same time, the considered use of data opens up new potential for innovative products and services.
Further links from the text above:
[1] What is smart data? - b2bsmartdata.de
[3] Big data explained simply - expedition.digital
[6] With these data analysis methods you can optimise your - omr.com
[9] Mastering data analysis: KIROI step 3 - sauldie.org
For more information and if you have any questions, please contact Contact us or read more blog posts on the topic Artificial intelligence here.
Deutsch 
English (UK)