Data analysis
Data analysis is a powerful tool that companies can use to turn their data into valuable insights. I
What is data analysis?
In conjunction with effective solutions for data analysis can companies optimize data flow, improve collaboration, results visualize and thus achieve their goals and remain competitive in the digital age.
Data analysis is an important success factor for companies in the digital age. With the right solutions and a data-driven approach, companies can strengthen their competitiveness and achieve their goals. We are happy to support you with our services relating to Reporting & BI
Data analysis includes
- Data collection: Collecting data from various sources, such as internal systems, external databases, and sensors.
- Data preparation: Clean and transform data to eliminate errors, inconsistencies, and missing values.
- Exploratory Data Analysis (EDA): First study of data to identify patterns, trends, and outliers.
- Modeling: Development of statistical or machine learning models to analyze data.
- Evaluation: Evaluation of the performance of the models and selection of the best model for the task.
- Visualizing: Presentation of data analysis results in the form of charts, graphics and dashboards.
- Communication: Communicate the results of the data analysis to decision makers and other stakeholders.
Data analytics solutions
They help companies take these steps by:
- A centralized environment to store, manage, and analyze data provide.
- Tools for exploratory data analysis, statistical modeling, machine learning, and visualization offer.
- Collaboration between data analysts and other stakeholders promote.
- Access to data and analysis tools from anywhere enable.
- Predefined features and analytics for specific industries offer.
An exemplary selection of solutions for data analysis:
Data management platforms
- IBM InfoSphere Data Platform - https://www.ibm.com/products/information-server-for-data-integration
- Microsoft Azure Data Lake Storage - https://azure.microsoft.com/en-us/products/storage/data-lake-storage/
- Amazon Web Services (AWS) Lake Formation - https://aws.amazon.com/de/lake-formation/
- More about Data Management Platform (DMP)
- More about our services related to Data architecture and Data Management
Data analytics platforms
- Tableau - https://www.tableau.com/en-gb
- Dataiku - https://www.dataiku.com/
- KNIME - https://www.knime.com/
Data discovery tools
- Qlik Sense - https://www.qlik.com/
- ThoughtSpot - https://www.thoughtspot.com/
- Alteryx - https://www.alteryx.com/
Cloud-based solutions
- Google Cloud Platform (GCP) - https://cloud.google.com/
- Amazon Web Services (AWS) - https://aws.amazon.com/
- Microsoft Azure - https://azure.microsoft.com/en-us
Industry-specific solutions
- SAS Healthcare Analytics - https://www.sas.com/de_de/industry/life-sciences.html
- SAP Retail Analytics - https://www.salesforce.com/eu/form/industries/salesforce-retail-demo
- Oracle Financial Analytics - https://www.oracle.com/financial-services/analytics/
Choosing the right solution for data analysis depends on the specific needs and requirements of a company. Key factors in the selection include the size and complexity of the data landscape, the required analytical capabilities, the budget, and the company's technical capabilities.
Additional implementation tips
- Define clear goals and use cases: What do you want to achieve with data analysis?
- Create a data governance strategy: How is data managed and used?
- Invest in employee training: Make sure employees can use solutions effectively.
- Monitor and optimize your solutions: Measure the success of your data analytics initiatives and adjust your solutions as needed.
With the right solutions and a strategic approach companies can effectively use data analysis to:
- Make informed decisions based on data and not on intuition
- To increase efficiency by optimizing processes and reducing costs.
- Drive innovation by developing new products and services based on data.
- To improve customer satisfaction by creating personalized offers and better understanding customer needs.
- Minimize risks by identifying and evaluating risks and developing strategies to minimize risks
More information about our services Data Strategy, Reporting & BI, Data Organization , Data Governance and Data culture.
Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.
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