Get trial
English
De-Risking Data Management for Reporting & Control - Video
This video shows how AnalyticsCreator reduces complexity in data management by combining metadata-driven automation with agile methodology. It demonstrates how new data sources and requirements can be integrated quickly into an existing data warehouse and reporting solution.
Duration: 46:29
Updated: Mar 2026
Level: beginner
Platform: AnalyticsCreator
For: Data Engineers, BI Developers
This video answers the following:
- How does AnalyticsCreator reduce complexity in data management?
- How does AnalyticsCreator support data warehouse automation?
- How are new data sources integrated into an existing model?
- How do agile methods and CRISP-DM fit into this approach?
- How does AnalyticsCreator help reduce vendor lock-in?
Platform shown
Microsoft Fabric
Related tooling
Azure Data Factory, Power BI
Key takeaways
- AnalyticsCreator uses metadata-driven automation to generate data warehouse structures, code, documentation, and lineage.
- The webinar positions automation as a way to reduce repetitive development work and improve responsiveness to business requests.
- The generated code has no runtime dependency on AnalyticsCreator, so it continues to run even if the subscription ends.
- The methodology section combines agile sprint-based delivery with CRISP-DM for step-by-step data warehouse development.
- In the demo, a new CSV source for returns is integrated into the existing model and reflected in the dashboard within a short implementation cycle.
Transcript
Okay, so my name is Rosario D Lorenzo. I'm in charge of the AnalyticsCreator global partner organization and during the past 15 years I really loved helping partners around the world to write success stories. This is my passion. Today we have an interesting topic and of course I'm not doing it alone. Today I have Tobias from BI Automation with me, who will give you a short demonstration of the tool, and Hao from ESG for CFO who will comment on the demo and provide some insight from a methodology point of view. Welcome Tobias, welcome Hao.
AnalyticsCreator is a German company based in Munich, founded in 2017. However, the software is much older. Currently we are in the third generation and are proud that we had the chance to evangelize the data automation practice during the past 15 years. We currently have more than 50 value-added partners with a strong emphasis on Microsoft, because the technology is built on the Microsoft stack. We have around 690 active data engineers and developers using AnalyticsCreator and recommending it to others. We are mainly focused in Europe and APAC. We have a clear mission at AnalyticsCreator. We want to help developers reduce repetitive tasks and the usage of the huge amount of software required to build a data warehouse. Developers need to connect data from multiple systems, manage pipelines, and finally deliver dashboards. Current technology stacks do not necessarily reduce workload or increase responsiveness to business requests. We also see a growing risk of vendor lock-in.
We believe that technologies which simplify and automate the modeling of analytics platforms are the best solution for developers and companies to regain control of their data and reduce operational cost. With AnalyticsCreator, we focus on engineering freedom and generating SQL code without runtime dependency. We believe customers should control their data, not vendors. Let me show the architecture of AnalyticsCreator. It starts with connecting sources, which are stored in a data catalog including dependencies. The key feature is the wizard, where after connecting a source you can generate a full data warehouse draft within minutes. All stages, historization, and imports are created automatically. You can then modify each layer and deploy the result to your preferred technology such as ETL tools or Power BI datasets. Everything is stored in an open repository based on metadata, not data itself.
AnalyticsCreator focuses on metadata modeling. We do not access actual data but model the structure and dependencies. This allows partners or teams to collaborate efficiently by working on the model directly. The automation engine generates code, documentation, and lineage automatically.
Many customers struggle with slow development, manual coding, and lack of transparency. This leads to delays, data quality issues, and high pressure on teams. Hiring more developers does not solve the problem. With automation tools like AnalyticsCreator, companies regain control, improve efficiency, and reduce total cost of ownership. The generated code has no runtime dependency. Even if a company stops using AnalyticsCreator, the generated code continues to run. This ensures independence from vendors. Customers gain more time to focus on business value instead of repetitive technical work. Independent analyst firms like BARC evaluate vendors based on customer feedback. AnalyticsCreator has been ranked highly for multiple years, especially in usability, self-learning, and operational capabilities.
Now we move to methodology. Traditional waterfall approaches do not work well anymore. Instead, agile approaches like Scrum are required. Development is divided into sprints, allowing prioritization and flexibility. A recommended approach for data warehouse projects is the CRISP-DM method. It starts with business understanding, then identifying data sources, modeling, evaluation, and deployment. These steps are executed iteratively within sprints. After a few sprints, results can already be shown to the business. This allows faster feedback and reduces risk. Combining methodology and automation leads to faster delivery and better outcomes.
We now move to the demo. Tobias will demonstrate how a dashboard is created and extended. We start with an existing sales dashboard showing metrics like sales and profit. The dashboard shows key metrics, filters, and visualizations. However, returns are not yet included. This is identified as a requirement for the next sprint.
In AnalyticsCreator, we add a new CSV connector. The tool supports many connector types. We import the CSV file and automatically detect its structure. Using the data warehouse wizard, the new source is integrated into the existing model. The staging, historization, and structure are generated automatically. We add the new table to the fact table and define the join logic. We create a new column to represent returns. This is done using simple expressions.
After synchronization, the model is updated. We deploy the changes using an existing deployment package. The system generates the required SSIS packages automatically. After execution, the dashboard is updated. Returns are now visible and metrics are adjusted accordingly. This demonstrates how quickly new requirements can be implemented.
