Data analysis is one of the key success factors for companies that want to utilise their digital potential in a targeted manner and thus take the step from mere data collection to a valuable basis for decision-making. The third step of KIROI in particular - mastering big data and smart data - shows how crucial focussed data analysis is for sustainable change processes. However, more and more companies are complaining about excessive amounts of data, unclear benefits and a lack of practical relevance. This is where transruptions coaching comes in and professionally supports companies in the successful implementation of their data analysis projects.
Data analysis: from theory to real-life practice
Data analysis never begins in isolation, but is always embedded in a specific corporate context. Many companies have huge amounts of data, but do not know how to systematically process, analyse and use it for the most important issues. Experts therefore recommend first focussing on the specific problems and only then selecting the appropriate methods and tools for data analysis. This saves resources, increases the relevance of the results and generates measurable added value more quickly.
Clients often report that they feel overwhelmed by the variety of tools and approaches. Transruption coaching builds bridges between theory and practice by developing customised solutions for individual requirements and providing the right experts from its own network.
Big data vs. smart data - which will bring the breakthrough?
While big data primarily stands for the quantity and variety of data, smart data emphasises the quality of the information. Only through targeted filtering, processing and analysis can raw data be turned into useful insights - and thus the basis for data-driven innovations[3][5]. Companies should therefore make targeted investments in data quality, as this is crucial to the success of projects[5].
A good practical example: a manufacturing company uses IoT sensors to monitor machine statuses in real time. Through targeted data analysis, it recognises anomalies at an early stage and can plan maintenance precisely. This reduces downtime and sustainably increases productivity[4].
Another example: A retail company analyses customer behaviour and order patterns in order to optimise logistics and warehousing. Only through high-quality data analysis can needs-based deliveries be realised and excess stock avoided[4].
There are also exciting use cases in the automotive industry: Modern vehicles constantly provide diagnostic data. Through targeted analysis, manufacturers can recognise quality problems at an early stage and thus increase customer satisfaction[2].
BEST PRACTICE at a customer (name concealed due to NDA contract): A customer from the industrial services sector was faced with the challenge of analysing a large amount of sensor data and developing predictive maintenance models from it in order to minimise downtimes. The coaching accompanied the integration of modern data analysis tools and trained the team in the use of machine learning. Within six months, downtimes were reduced by over 20 % because the data analysis focussed specifically on the critical parameters and the algorithms continuously learned from the empirical values. Today, data analysis is an integral part of internal reporting and serves as an early warning system for technical faults.
Practical tips for successful data analysis projects
To increase the success of data analysis projects, it is not enough to simply introduce tools. Companies should take a systematic approach and consider the following impulses:
1. data quality as the basis for reliable results
Most errors in data analysis are caused by incomplete, incorrect or outdated data. It is therefore advisable to pay attention to standards as early as the data collection stage and to carry out regular data cleansing processes[6][7]. Only clean data provides valid results and enables well-founded decisions.
One example: an energy supplier continuously checks its consumption data for plausibility and removes outliers to make forecasts more reliable. This allows energy-intensive processes to be optimised in a targeted manner and costs to be saved.
Data quality is also crucial in the retail sector: if customer movements in the shops are recorded accurately, product placements can be adjusted in a targeted manner, thereby increasing sales.
In the logistics industry, companies benefit from analysing routes and vehicle conditions on a daily basis. This is the only way they can recognise bottlenecks at an early stage and calculate alternative routes.
2. working with the right tools and competences
The selection of suitable analysis tools is essential, but not the only decisive factor. The skills of the employees who carry out and interpret the data analysis are just as important. Training, coaching and exchanges with experts support the development of expertise and promote the acceptance of new methods[1][7].
A practical example: A mechanical engineering company invested specifically in further training for quality management in the area of statistical analysis. Today, the teams analyse production data independently and derive improvement measures without having to rely on external service providers.
The use of visualisation tools is equally useful for presenting complex data sets clearly and thus creating the basis for discussion for strategic decisions[2].
Another example: a logistics service provider uses heat maps to analyse the capacity utilisation of its logistics centres. This makes it possible to optimise capacity planning and reduce empty runs.
3. incorporate findings into the control system in a targeted manner
The best results from data analysis are of little use if they are not incorporated into daily management and decision-making. Companies should therefore establish fixed processes for translating findings from data analysis into operational and strategic measures[7].
A practical example: A manufacturing company set up weekly data review meetings in which the current key figures are discussed and measures are derived directly. This creates a continuous improvement process.
The added value is also evident in the service sector: one insurer uses the results of the data analysis to sharpen risk profiles and thus optimise premium calculations.
Another example: A retailer regularly adapts the product range to the results of the sales data analysis and can thus react specifically to changes in demand.
My analysis
Data analysis is not an end in itself, but a key lever for data-driven innovation in companies. Those who make targeted use of big data and smart data can optimise processes, minimise risks and identify new business opportunities. The transformation of raw data into usable knowledge is best achieved when companies ensure data quality, build up technical and methodological expertise and systematically integrate the findings into management. Transruption coaching accompanies you professionally on this path - from the initial idea to sustainable implementation in everyday life.
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Further links from the text above:
Big and smart data - from statistics to data analysis - DGQ[1]
Intelligent Data Analysis Methods for Engineers (Master) - TUM[2]
Big data vs. smart data - Dataversity[3]
Big Data vs. Smart Data: Key Insights for Operational Optimisation - Oxmaint[4]
Big Data vs. Smart Data: Is More Always Better? - Netconomy[5]
Big Data Analytics: What It Is, How It Works, Benefits, And Challenges - Tableau[6]
5 Ways to Turn Big Data into Smart Data | Gate6[7]
KIROI 3: Data analysis with Big, Smart & Trusted Data for success - sauldie.org[8]
Smart data[9]
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