Data analysis in transition: How KIROI Step 3 is paving new paths with Big & Smart Data
Data analysis is increasingly taking centre stage in modern companies because it provides crucial impetus for innovation and efficiency [1]. It is no longer just about collecting huge amounts of data, but also about the targeted utilisation of valuable information. transruptions coaching supports organisations in successfully making this transition from big data to smart data - making data analysis processes fit for the future.
Where big data reaches its limits - and why data analysis needs to think ahead
Big data refers to huge, sometimes unstructured volumes of data that originate from various sources such as sensors, social media or transactions and grow on a daily basis [1][5]. Companies often report challenges: The abundance of information overwhelms traditional analysis tools, important findings remain undiscovered and decisions are delayed. This is precisely where the third step of the KIROI model comes in, because it rethinks data analysis - away from collecting at all costs and towards intelligent utilisation.
An example from the retail sector: a chain of stores collects millions of customer interactions every day, from movement data in the store to online reviews. Without targeted data analysis, this information threatens to drown in the sea of data. Transruption coaching helps teams to look for patterns that increase sales or improve customer satisfaction.
In the logistics industry, sensors provide real-time information about stock levels and supply chains. Anyone who analyses this data without filtering it quickly loses track of things. It is crucial to filter out relevant key figures - such as delivery times or failure rates - and analyse them in real time.
The benefits of smart data analysis can also be seen in the healthcare sector: patient data from various sources is processed in such a way that doctors can make faster and more targeted diagnoses. This does not create new data silos, but rather integrated solutions.
The leap to smart data: data analysis with a focus on quality and relevance
Smart data is created when big data is filtered, cleansed and contextualised [2][3]. This transforms data analysis from a quantitative to a qualitative discipline. The result: smaller but high-quality data sets that provide targeted answers to operational questions [4].
In the manufacturing industry, for example, this means that machine data is not simply stored, but analysed directly on the production lines. Predictive maintenance is thus possible because algorithms derive indications of impending failures from smart data.
Another example is a bank's CRM system, where customer data from various channels flows together. Targeted data analysis allows individual offers to be generated that are tailored to the behaviour and needs of the customer - in real time.
In the marketing sector, companies use smart data to dynamically adapt campaigns. Adverts are no longer simply scattered, but sent to those who are demonstrably interested. This saves resources and increases the conversion rate.
Data analysis in practice: three examples from everyday coaching practice
BEST PRACTICE with a customer (name hidden due to NDA contract): A medium-sized company from the consumer goods industry was faced with the challenge of making its supply chains more transparent. In transruptions coaching, key figures were jointly defined that are really relevant - such as stock turnover, delivery punctuality and returns rate. These were analysed on an ongoing basis so that the company could react more quickly to bottlenecks and save costs. The data analysis became a continuous improvement process.
Another example: An energy supplier wanted to optimise its customer service. Complaints and enquiries were systematically recorded and analysed during coaching. This made it possible to identify hotspots and initiate targeted training for employees. Customer satisfaction increased measurably.
Data analysis is also having an impact in public administration. One authority uses smart data to speed up application processes. By analysing processing times and queries, bottlenecks can be identified and measures derived to reduce the workload on employees.
Data analysis as a success factor: How to get started in the world of smart data
The key to successful data analysis lies in the interplay of technology, processes and the right questions. Companies benefit in particular if they define clear goals from the outset and focus their data analysis on specific use cases [4].
A tip: Start with an inventory. Which data is already being collected today - and which is actually being used? It often turns out that even small adjustments can have a big impact. Transruption coaching helps you to realise this potential and create sustainable structures.
A further step is the integration of modern analysis tools. Machine learning and artificial intelligence help to recognise patterns and make predictions. It is crucial that the results are also presented in an understandable way - because only then can they be used in everyday life.
Cooperation between specialist departments and IT should also be strengthened. Data analysis is not an end in itself, but serves to improve operational processes. Regular workshops and training courses ensure that knowledge remains within the company.
My analysis
Today, data analysis is more than just analysing large amounts of data - it is the driving force behind innovation and efficiency in companies of all sizes. The transition from big data to smart data is successful when information is specifically filtered, analysed and placed in an operational context. transruptions coaching supports teams in actively shaping this transformation and establishing sustainable structures.
Companies that take data analysis seriously gain agility, reduce costs and strengthen their competitive position. Experience shows: Investing in smart data analysis today lays the foundation for the challenges of tomorrow.
Further links from the text above:
Big Data Analytics - IBM[1]
Big data vs. smart data - Dataversity[2]
Difference Between Big Data and Smart Data - ESA Automation[3]
Big Data vs. Smart Data: Is More Always Better? - Netconomy[4]
What Is Big Data? - Oracle[5]
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)