Data analysis as a success factor: Optimizing processes through intelligent analysis.
In an increasingly digitalized world, where data has become an essential part of our business processes, the ability to extract information from data and use it effectively is a key success factor for companies. Data analysis plays a central role here and makes it possible to optimize complex processes intelligently.
Companies and organizations are constantly generating data in almost all areas of their operations - be it customer data, sales statistics, logistics information or social media. But the sheer volume of data alone is not enough to give you a competitive edge. Do you recognize yourself here?
Intelligent analysis is required to discover and utilize the valuable insights contained therein. Easier said than done, you may be thinking. That's why we give you a comprehensive insight into the world of data analysis. We will present our holistic approach to how you can optimize your business processes using intelligent analysis methods. Whether you already have experience in data analysis or are just starting out, here you will find valuable information and tools to take your analysis skills to the next level.
These are the steps for a successful data analysis project.
By following a structured approach in the following steps, the data analysis project is effectively planned, implemented and reviewed together with you in order to derive the greatest possible benefit from the data and achieve your desired goals.
Phase 1: Target definition
This phase is about understanding what the customer wants to achieve with the data analysis. Clear objectives are defined, such as the improvement of business processes or the identification of opportunities to increase sales.
Phase 2: Connection to upstream systems
Data sources are identified from which the required information originates. These can be databases, Excel files or APIs, for example. These data sources are then linked to the analysis tool. A reliable data pipeline is established.
Phase 3: Data analysis
The data is examined more closely to identify patterns, trends and correlations. An exploratory data analysis is carried out to understand what information they contain and how they relate to each other. The aim is to gain initial insights.
Phase 4: Data cleansing
The data often contains errors or is incomplete. In this step, the data is cleansed by removing duplicates, adding missing values and dealing with outliers. This ensures that the data is suitable for analysis and provides accurate results.
Phase 5: Building the data model
In this phase, models or algorithms are developed to analyze the data and make predictions. These can be statistical models or machine learning algorithms. The aim is to structure the data in such a way that it can be analyzed and useful information can be derived from it.
Phase 6: Visualization of the data
Complex data alone is often difficult to understand. It is therefore important to present the results of the analysis in a more visually appealing and meaningful way. These can be charts, graphs or interactive dashboards that make it possible to present the data in a clear way and recognize trends or patterns.
Phase 7: Evaluation and testing by the customer
Once the analysis has been carried out, it is important to check the results for accuracy, reliability and relevance. In consultation with the client, we ensure that the results meet expectations and provide useful insights.
Phase 8: Deployment
In this final phase, the results of the analysis are integrated into operations. This means that the models, visualizations and findings developed are implemented in the business processes. This means they can be used effectively to make well-founded decisions and bring about positive changes in the company.
Would you like to create added value for your company and still have questions about the structured process of a data analysis project? We will be happy to answer your specific questions.
Success with the right software.
It is important that companies consider their specific requirements, business objectives and area of application when selecting a software solution. It can also be useful to carry out a thorough evaluation to compare different options, view demos and check customer references to find the most suitable solution for individual needs.
The following software solutions can support you with data analysis, but in different ways. "Corporate Planner" and "Qlik Sense" are two different tools with which we have been able to generate excellent results for our customers. Here is a brief description of how each of these solutions can help you analyze your data:
Corporate Planner
Corporate Planner ist eine Softwarelösung, die bei der Unternehmensplanung, Budgetierung und Analyse unterstützt. Das Tool ermöglicht die Erfassung und Zusammenführung verschiedener Unternehmensdaten, wie zum Beispiel finanzielle Daten, Vertriebszahlen oder Personalinformationen. Corporate Planner bietet Funktionen zur Datenkonsolidierung, -modellierung und -analyse. Es kann dabei helfen, umfangreiche Datensätze zu verarbeiten, Finanzprognosen zu erstellen, Szenarioanalysen durchzuführen und Berichte zu generieren. Die Lösung bietet auch die Möglichkeit, Daten in übersichtlichen Dashboards und Diagrammen darzustellen, um wichtige Kennzahlen und Trends zu visualisieren.
Qlik Sense
Qlik Sense ist eine Data-Discovery- und Visualisierungsplattform, die bei der Datenanalyse und Entscheidungsfindung unterstützt. Es ermöglicht die Integration verschiedener Datenquellen und die Erstellung interaktiver Dashboards und Berichte. Mit Qlik Sense können Nutzer Daten visualisieren, explorative Analysen durchführen und tiefergehende Einblicke in die Daten gewinnen. Die Plattform bietet eine Vielzahl von Diagrammen, Grafiken und Filteroptionen, um Daten in einer verständlichen und interaktiven Weise darzustellen. Qlik Sense unterstützt auch die Zusammenarbeit und den Austausch von Analyseergebnissen durch die Möglichkeit, Dashboards und Berichte mit anderen zu teilen.
Comparison of data analysis functions: Corporate Planner vs. Qlik Sense.
Both software solutions therefore offer functions for data analysis, albeit with different focuses. While Corporate Planner focuses more on financial planning and analysis, Qlik Sense offers a wide range of functions for data visualization and exploratory data analysis. The choice between the two depends on the specific requirements and the area of application in which the data analysis is to be carried out.
Both interesting for you?
Great added value can be achieved if the Corporate Planner and Qlik Sense solutions are used together in the company, as they can complement each other. Both solutions are combined via an interface. This enables companies to use their data even more comprehensively. They are able to improve their financial planning and analysis as well as perform comprehensive data visualization and analysis. This makes it possible to make well-founded decisions and increase the company's success.
Become a data-driven company now
Together, we want to create a world in which data is not just seen as mere numbers and information, but as an important raw material to revolutionize your business processes. Companies that work in a data-driven way can make faster and more precise decisions, optimize their processes and ultimately increase their competitiveness. We are convinced that intelligent data analysis is the key to success - and we would like to help you use this success factor to your advantage.
