The digital transformation is fundamentally changing companies. Data analysis is becoming a key skill for decision-makers and experts. KIROI Step 3 focuses on the intelligent use of big data and smart data. This combination enables organisations to gain actionable insights from enormous amounts of data. Data analysis forms the basis for faster, better and more informed business decisions. Companies that consistently follow this path create significant competitive advantages.
What does data analysis mean in the digital transformation?
Data analysis is more than just collecting information. It is about the intelligent processing and interpretation of data. Companies generate large amounts of raw data every day. This is generated in production, sales, marketing and customer service. Without structured data analysis, however, this information remains useless.
KIROI Step 3 teaches precisely these skills. Data analysis professionals learn to recognise relevant patterns. They identify hidden correlations and derive recommendations for action. This only works with suitable methods and modern technologies.
In the manufacturing industry, companies use data analysis to optimise production processes. They recognise deviations in quality control and can prevent machine failures in advance. In e-commerce, companies use data analysis to understand customer preferences. They create personalised product recommendations and thus significantly increase conversion rates. Energy supply companies analyse consumption patterns and optimise their electricity distribution in real time.
The difference between big data and smart data
Big data simply refers to large amounts of raw information. Smart data, on the other hand, are intelligently processed data sets. The distinction is crucial for successful data analysis. Raw big data alone does not lead to any insights and does not solve any business problems.
Smart data is created through intensive data analysis processes. Specialists extract relevant information from millions of data points. They utilise advanced techniques such as machine learning and artificial intelligence. The result is focussed, high-quality and validated data sets. This smart data supports managers in making critical business decisions.
A logistics company collects information on delivery routes, vehicle statuses and delivery times on a daily basis. That is big data. However, through targeted data analysis, it recognises which routes are inefficient in certain seasons. It identifies which vehicle types cause higher operating costs. These usable insights are smart data. It enables the company to redesign routes and reduce costs by ten to twenty per cent.
Data analysis methods for converting raw data
The transformation of big data into smart data follows a structured process. Data analysis specialists use various methods and technologies. These methods build on each other and complement each other.
Data analysis begins with data cleansing and validation. Incorrect or incomplete entries are corrected or removed. This is followed by the exploratory analysis of large volumes of data. This involves identifying patterns, trends and anomalies. Machine learning algorithms provide automated support for these processes. The results are then visualised. Dashboards and diagrams make complex findings understandable.
A retail chain collects sales data from a hundred shops. Data analysis reveals that products are bought differently in northern Europe than in the south. Seasonal fluctuations are clearly evident. The chain uses this information to optimise its stock levels regionally. Supply bottlenecks disappear and customer satisfaction increases. A financial institution uses data analysis to recognise fraud. It compares current transaction patterns with historical data. Suspicious activities are immediately identified and blocked.
Practical applications of data analysis in various industries
The possibilities of modern data analysis extend across almost all sectors of the economy. Each industry utilises these technologies differently and taps into industry-specific advantages.
Data analysis in healthcare and medicine
Clinics record patient data every day. Data analysis helps doctors to recognise illnesses earlier. They analyse laboratory values, vital signs and patient histories. Machine learning models support diagnoses and treatment recommendations. Pharmaceutical companies use data analysis for drug development. They recognise which patients respond to new drugs. This speeds up clinical trials considerably.
Insurance companies in the healthcare sector use data analysis to predict risk. They identify patients at high risk of expensive illnesses. Preventive measures reduce costs in the long term. A rehabilitation centre uses data analysis to measure the success of therapy. It compares movement sequences before and after treatment. Therapists recognise which exercises are most effective.
Smart data and data analysis in Industry 4.0
Modern factories are full of sensors and digital devices. These continuously generate large amounts of data. Data analysis converts this information into production optimisation. Machine data shows when maintenance is required. Preventive maintenance prevents downtime. Production managers recognise bottlenecks and can eliminate them.
A car manufacturer uses data analysis for quality control. Sensors at every assembly point record process parameters. Deviations are recognised and corrected immediately. Reject rates are significantly reduced. A mechanical engineering company uses data analysis for predictive maintenance. Vibration patterns and temperature curves indicate impending defects. The customer is warned in advance and can plan repairs. This saves downtime costs and emergency repairs.
BEST PRACTICE with one customer (name hidden due to NDA contract)An electric machine manufacturer collects data from thousands of installed motors. It uses data analysis to identify energy efficiency problems in its customer fleet. The customer uses the findings to replace outdated motors with new ones. Energy costs in the production plants are reduced by eighteen per cent. The company earns twice: through motor sales and through consulting services based on the results of the data analyses.
Data analysis in retail and e-commerce
Online retailers generate huge amounts of customer data. Data analysis helps them to understand and predict purchasing behaviour. Personalised recommendations significantly increase sales. Retail chains use data analysis for inventory optimisation. They analyse sales per shop and per item. Expensive stock disappears.
A large fashion house uses data analysis to predict trends. It monitors social media activities and search queries. It then orders specific items of clothing for upcoming seasons. Overstocks and mispurchases are greatly reduced. An online marketplace uses data analysis to prevent fraud. It recognises suspicious seller patterns and buyer behaviour. Unauthorised transactions are blocked before any damage is done.
Challenges in the implementation of data analysis
The implementation of successful data analysis strategies encounters obstacles in practice. Companies need to understand these challenges and address them in a targeted manner.
The biggest problem is often the shortage of skilled labour. Data analysis experts are scarce and expensive. Many companies do not have specialised staff. At the same time, there is a lack of investment in technical infrastructure. Cloud platforms and storage systems require considerable initial investment. Data protection and compliance pose further hurdles. Companies must observe strict regulations and protect customer data.
Data quality is a constant problem. Incorrect entries lead to poor analyses. The data silos in many organisations make integration projects more difficult. Different departments use different systems. However, data analysis only works if all sources come together. Cultural resistance delays introductions. Some employees mistrust new technologies and automation.
BEST PRACTICE with one customer (name hidden due to NDA contract)A medium-sized logistics company wanted to introduce data analysis. At first it seemed impossible. The IT infrastructure was outdated, there was a lack of experts and employees were sceptical. The company started small with a pilot project. It analysed delivery routes using existing tools. After three months, travel costs fell by ten per cent. The success convinced sceptics and management. The company then invested in modern systems and provided employees with targeted training. Today, several departments regularly use data analyses to make better decisions.
The role of coaching and support in data analysis projects
Data analysis projects have high success rates when professional support is available. An experienced coaching team supports organisations right from the start. This starts with strategy development and ends with the utilisation of results.
Transruption coaching provides companies with targeted support for data analysis projects. It helps to clarify the right questions. It supports the selection of suitable methods and technologies. The coaches convey an understanding of data analysis within the team. They train employees and help to overcome resistance. They support companies in deriving concrete measures from data analysis results.
A classic data analysis mistake is to only see the figures and not the reality behind them. Experienced coaches help to interpret findings correctly. They support decision-makers during implementation. They check whether measures are actually achieving the desired results. This support makes the difference between successful and failed projects.
A financial services provider used data analysis to identify customers with a high risk of churn. Without coaching, the management would have ignored the results. With support, the company recognised which measures made sense. Personalised customer offers were developed. The churn rate fell by fifteen per cent. An energy supply company wanted to predict energy consumption. Data analysis was possible, but the results were initially confusing. A coach helped to interpret the data correctly. Savings measures were then derived and implemented.
Technologies and tools for modern data analysis
Technical equipment is a prerequisite for professional data analysis. Companies have many options to choose from. These technologies differ in terms of performance, cost and complexity.
Cloud platforms have established themselves as the standard for data analysis projects. They offer scalable storage capacity and computing power. Companies only pay for what they use. Machine learning platforms enable automated data analysis without specialised programming knowledge. Visualisation tools make complex results understandable. They create interactive dashboards that allow users to explore data themselves. SQL databases and NoSQL systems store different types of data efficiently. Business intelligence solutions combine data collection, analysis and reporting in one system.
Many companies start with simple tools such as spreadsheet software. These work for small amounts of data and initial projects. Later, they switch to specialised systems. One production company initially used Excel to analyse quality control. As the data volumes grew, it migrated to a cloud platform. An insurance company used Python and R to develop data analysis models. An e-commerce portal uses machine learning to predict customer preferences.
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