English
De-risking the complexity of data management in Reporting and Controlling
AnalyticsCreator enables fast, automated data warehouse creation and seamless integration of new data sources into Power BI dashboards. It reduces manual work by generating code, lineage, and transformations automatically. This allows teams to deliver new insights—like adding returns data—within minutes instead of days.
Questions
- How does AnalyticsCreator automate data warehousing?
- How can new data sources be integrated quickly?
- What is the Data Warehouse Wizard?
- How does historization work in a data warehouse?
- How can Power BI dashboards be updated automatically?
- What are the benefits of metadata-driven automation?
Key Takeaways
- Data warehouse creation in 2–3 minutes via wizard
- Automated historization and data lineage
- Integration of new sources (e.g. CSV) in minutes
- No runtime dependency → full ownership of code
- Strong fit for agile / sprint-based development
- Reduced engineering workload and faster delivery
- Immediate impact on dashboards (e.g. returns added)
- Supports ESG and multi-source reporting scenarios
Transcript
My name is Rosario Di Lorenzo, and I am responsible for the AnalyticsCreator global partner organization. Over the past 15 years, I have had the pleasure of helping partners around the world create and share their success stories.
Today, we have a very interesting topic, and I am joined by Tobias from BI Automation and Hao from ESG for CFO. AnalyticsCreator is a German company based in Munich, founded in 2017. However, the software itself has a much longer history, and we are currently working with its third generation.
We have more than 50 value-added partners and around 690 active data engineers and developers. Our mission is to help developers reduce repetitive tasks and automate data modeling.
My name is Rosario Di Lorenzo, and I am responsible for the AnalyticsCreator global partner organization. Over the past 15 years, I have truly enjoyed helping partners around the world create and share their success stories. This is my passion.
Today, we have a very interesting topic, and of course, I am not doing it alone. I am joined by Tobias from BI Automation, who will give you a short demonstration of the tool, and Hao from ESG for CFO, who will comment on the demo and provide insights from a methodology point of view. Welcome, Tobias. Welcome, Hao.
AnalyticsCreator is a German company based in Munich and was founded in 2017. However, the software itself is much older. We are currently working with the third generation and are proud to have helped promote data automation practices over the past 15 years.
Today, we have more than 50 value-added partners, with a strong focus on Microsoft because the technology is built on the Microsoft stack. We also have around 690 active data engineers and developers using AnalyticsCreator.
Our customers not only enjoy using AnalyticsCreator but also take pride in recommending it to others.
We want to help developers reduce repetitive tasks and minimize the number of tools required to build a data warehouse.
Developers often have to connect data from multiple systems using different tools, making sure everything works together before a dashboard can finally be created.
The industry provides many tools, but this does not necessarily reduce the workload or increase the ability to respond quickly to business requests.
At the same time, the risk of vendor lock-in has never been higher.
We believe that technologies that simplify and automate the modeling of analytical environments are the best solution.
With AnalyticsCreator, we focus on restoring engineering freedom and making SQL code independent from runtime dependencies.
We connect metadata, not data. This allows partners to collaborate directly on the model, while code, documentation, and lineage are created automatically.
The process starts with the source.
The metadata lands in a data catalog. Once connected, you can start the Data Warehouse Wizard.
Within two to three minutes, you get a full data warehouse draft, including staging layers, connections, and activated historization.
From there, you can go into each stage and start transforming the data.
Customers are often overwhelmed by business requests and delayed by manual coding and documentation.
Teams are under pressure, data quality issues appear, and projects struggle to reach completion.
In many cases, there is not enough transparency.
The generated code has no runtime dependency.
Even if customers stop using AnalyticsCreator, the generated code continues to work.
The customer controls the future of the data, not the vendor.
Traditional waterfall approaches no longer work.
Today, you need agility and flexibility. Development should be divided into sprints and supported by methods such as CRISP-DM.
This means understanding business requirements, identifying data sources, modeling the data, evaluating the results, and deploying iteratively.
We start with a simple sales dashboard.
You can see total sales, total profit, time selection filters, and product categories.
This is a basic example.
When I look at the dashboard, I notice that something is missing.
Are returns shown?
No, they are not.
We have the returns in a CSV file, and we will integrate them now.
We create a new connector, select CSV, define the file path, and automatically retrieve the structure.
In this case, the only relevant column is the Order ID.
We open the Data Warehouse Wizard and select the new CSV connector.
After importing the table, the system automatically creates staging, historization, and integration structures.
We join the returns table to the fact table and define the SQL join.
Then we convert the return flag to true or false, save the transformation, and synchronize the data warehouse.
We go to deployment, select the deployment package, and add the new SSIS packages.
After checking the settings, we deploy the changes.
The system helps prevent data loss during deployment.
We execute the workflow package.
The dashboard updates, returns are now visible, and the numbers are adjusted.
Many companies collect ESG data in Excel and need to integrate it into a central system.
AnalyticsCreator makes this integration easy, allowing multiple sources to be combined.
Automation generates code, improves data governance, and supports scalable architecture.
It enables continuous improvement, integrates new sources in minutes, standardizes development across teams, and reduces project risk.
