At a glance
- enables well-founded decisions, optimized processes, and early detection of problems
- can be used retrospectively, in real time, or for forward-looking analysis
- four main methods: descriptive, diagnostic, predictive, and prescriptive analytics – depending on the question and time horizon
- business intelligence tools help visualize and analyze complex data in an easy-to-understand way
What would you like to know about Data analysis?
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Data analysis definition
Data analysis is the systematic process of examining and evaluating raw data in order to derive useful information from it. Statistical methods and modern data analysis tools are used to identify patterns, trends, and correlations in the data. Companies use these insights to make well-founded decisions, minimize risks, and optimize their business processes.
Data analysis can be performed both retrospectively and in real time, enabling companies to respond flexibly to market changes and secure a competitive advantage. Typical areas of application include optimizing business strategies, identifying customer behavior patterns, and increasing the efficiency of operational processes.
„With DeltaMaster, we identify variances in contribution margin immediately and navigate via drill-down down to the customer and product level. This way, we find the cause in minutes instead of days.“Head of Controlling
Consumer goods manufacturer
Why is data analysis so important? – Relevance and benefits of data analysis
Data analysis is essential for business success, particularly in times of rapid market change, enormous volumes of data, increasing complexity, and growing competition. Some of the benefits that effective data analysis can provide for companies include:
- Data-driven decision-making: Supports companies in making well-founded decisions and securing competitive advantages.
- Transparency into processes and customers: Provides valuable insights into business processes, customer behavior, and relevant market trends.
- Increased efficiency and cost reduction: Analysis results can be used to optimize processes and sustainably reduce costs.
- Strategic adaptability: With suitable data analysis tools, companies can continuously optimize their strategies and respond flexibly to market changes.
- Early detection of problems: Potential weaknesses and risks within the company can be identified at an early stage through data analysis.
- Improved products and customer experiences: Insights gained from data analysis enable targeted product improvements and more personalized customer engagement.
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What data analysis methods are there?
Depending on the objective and complexity of the analysis, different analytical techniques are used to analyze data. The four main methods of data analysis are:
| Analytics method | Question | Typical example |
|---|---|---|
| Descriptive Analytics | What happened? | Revenue in the last quarter, production figures |
| Diagnostic Analytics | Why did it happen? | Identifying the causes of a decline in revenue |
| Predictive Analytics | What will happen? | Forecasting customer behavior or market developments |
| Prescriptive Analytics | What should be done? | Specific recommendations for action based on forecasts |
- Descriptive Analytics: This method of data analysis focuses on historical data. It shows what happened in the past, such as how high revenue was in the last quarter or how many products were manufactured. Descriptive analytics provides insights into what happened and whether the result was positive or negative, but does not explain why it happened.
- Diagnostic Analytics: This method examines why particular events occurred. Different datasets are compared to identify patterns and relationships that explain the causes of developments. Diagnostic analytics enables companies to gain insights into problems and their causes so that they can subsequently be addressed.
- Predictive Analytics: This form of analysis is used to forecast possible future events. Based on descriptive and diagnostic analyses, as well as algorithms, AI, and Machine Learning, trends and developments are forecast to predict, for example, customer behavior or market developments.
- Prescriptive Analytics: This advanced analytical method provides specific recommendations for action based on historical data, current information from various sources, and advanced technologies such as Machine Learning and neural networks. Prescriptive analytics uses forecasts to help companies capitalize on future opportunities or avoid problems by recommending appropriate actions.
How is data analysis performed? – Steps in data analysis
Data analysis is a multi-stage process consisting of data collection, processing, storage, and analysis, with the aim of making decisions based on well-founded information. These steps describe the typical data analysis process:
What is exploratory data analysis?
Exploratory data analysis is an approach to examining datasets in order to understand their underlying structures, patterns, and relationships before formal statistical models are applied.
Visual methods such as charts and statistical measures are used to identify notable trends, outliers, and relationships before more complex statistical models are applied. The aim is to generate hypotheses and gain deeper insights into the data without relying on predefined assumptions. Exploratory data analysis is therefore an important tool for companies that make data-driven decisions and want to explore data intuitively rather than through formalized methods.
Practical example: data analysis with DeltaMaster
In its monthly reporting, a consumer goods company finds that the contribution margin in one product group is significantly below plan – without the cause being immediately apparent.
With DeltaMaster from Bissantz, the analysis leads to action in just a few steps: the variance is detected and highlighted automatically. Using drill-down, the controller navigates with a few clicks or a natural-language query from the aggregated product group level down to individual product, region and customer level – and finds that the decline is attributable to a single major customer in one region who switched to a cheaper competitor product.
Descriptive analysis shows what happened. The diagnostic level explains why. Based on the predictive functions of DeltaMaster, you can estimate how the trend will develop further – and which countermeasures would have the greatest effect. In this way, a single figure in the report becomes a complete analysis process: from signal to decision.
Application examples: Where is data analysis used?
Data analysis is relevant in nearly all areas of a company. These examples show typical fields of application in which data give rise to insights relevant for management.
Where would you like to analyze data?
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Data analysis and Bissantz
With DeltaMaster, Bissantz provides a powerful tool for data analysis – from automated data integration and analysis to clear presentation in dashboards and reports. The software supports all common forms of analysis – descriptive, diagnostic, predictive, and prescriptive – and automatically prepares even large volumes of data so that users can immediately see what matters.
Thanks to consistent KPI logic, intelligent visualization, and AI capabilities, such as automatically highlighting variances, data analysis with DeltaMaster becomes an integral part of modern, data-driven business management. Exploratory data analysis is supported alongside standardized analyses, providing a basis for fact-based decisions at all levels.
However, the process rarely starts with the tool alone. Bissantz’s data analytics consulting services support companies from the outset – from the conceptual design of the analytical structure and selection of relevant KPIs to the definition of meaningful benchmarks. Effective analysis requires the right questions to be asked before the first data is processed: Which KPIs are genuinely relevant for business management? Which analytical method is appropriate for the question at hand? How must data be structured to ensure reliable drill-downs and variance analyses?
| Function | Benefit |
|---|---|
| Automatic data integration | consolidates data from various sources and prepares it automatically |
| Automatic variance highlighting | uses AI to show immediately what is relevant and what deviates from the expected value |
| Drill-down and variance analysis | navigates from aggregated values down to the level of individual products, regions and customers |
| Natural-language query | enables analysis without complex operating steps |
| Predictive functions | estimate the further development of trends and evaluate countermeasures |
„The automatic highlighting of relevant variances has noticeably accelerated our data analysis. We now make decisions on a reliable factual basis.“BI Manager
Industrial company
Frequently asked questions
What is data analysis in simple terms?
Data analysis means systematically examining raw data to find answers to specific questions. Why is revenue declining? Which customers buy what? What is likely to happen next? Data analysis turns figures into insights – and insights into better decisions.
What is the difference between descriptive and predictive analysis?
Descriptive analysis looks backward: what happened? Predictive analysis looks ahead: what is likely to happen? Both are part of a complete analytical process – descriptive data provides the foundation on which predictive models are built.
What tools are used for data analysis?
Typical tools range from spreadsheets and specialized statistical software to Business Intelligence platforms such as DeltaMaster. The key is that the tool fits the question, the volume of data, and the target audience – not every analysis requires complex algorithms.
What is exploratory data analysis?
Exploratory data analysis explores datasets without predefined hypotheses, with the aim of discovering patterns, outliers, and relationships that can then be examined in further analyses. It is the first step before formal models are applied.
How does Bissantz support data analysis in controlling?
DeltaMaster automates the entire analysis process – from data integration and pattern recognition to reporting. Variances are automatically detected, AI-supported functions provide explanations, and drill-downs enable rapid root-cause analysis – supporting well-founded decisions at all levels.
Summary
Data analysis is the foundation of data-driven business management: it transforms raw data into insights, insights into decisions, and decisions into competitive advantages. The range of modern analytical methods extends from descriptive retrospective analysis to prescriptive recommendations for action. Bissantz’s consulting services and software solutions make data analysis efficient and effective for companies.
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