Our services - tailored to you.
How can we help you?
Also, we've put together some FAQs for you:
Why do we need a data warehouse?
Unless you are keen on losing historical data and not enrich data cross-sources, then you should consider DWH. DWH is here to help you getting things in order and even enhance data to bring it to a whole new experience level.
What is the difference between data warehouse and data lake?
In a data lake the data is being stored in the lake aka landing zone within the same data platform.
Whereas a data warehouse is a framework helping to transform and enrich data sets.
Whereas a data warehouse is a framework helping to transform and enrich data sets.
WHY DO WE NEED DATA MODELLING?
Data modelling is essential for combining many different data sources available to to you these days. We will build the foundation of your modern data warehouse stack.
How can I calculate the benefits of automation?
With our automation dashboard.
What are the benefits of fivetran?
They say 80% of warehousing is about integration of you sources into a landing zone. Combined with the rest of human labour this is a no-brainer to implemenent.
What are the advantages of Snowflake?
Dynamic pricing, fully managed, automatic query optimization, highly scalable
Where is my data stored when I use Snowflake?
Google Cloud (GCP), Amazon Web Services (AWS) or Microsoft Azure. You can chose the region, if available.
Tableau vs Power BI: Why use tableau if Power BI is cheaper?
The TCO (Total Cost of Ownership) of Tableau is more favourable. PowerBI Dashboards take more time to build and maintain. Also, some features in PowerBI request additional payments.
Is bi concepts also offering other tools besides Fivetran, Snowflake and Tableau?
We strongly recommend this tool stack, but if you wish, we are happy to help finding the right solution for you.
What is the best way to introduce data culture to an organisation?
Introducing data culture to an organization can be a challenging task, but it is also a crucial step in leveraging data to make better decisions and improve business outcomes. Here are a few suggestions for introducing data culture to an organization:
Start with the basics: Make sure that everyone in the organization understands the basics of data and its potential value. This can include training on topics such as data literacy, statistics, and data visualization.
Get leadership buy-in: It is important to have support from leadership in order to successfully introduce data culture to an organization. This can involve setting goals and metrics for data-driven decision making, as well as providing resources and support for implementing data-related initiatives.
Encourage collaboration: Data culture is all about using data to make better decisions, so it is important to encourage collaboration and cross-functional teamwork. This can involve setting up regular meetings for discussing data-related issues and making sure that different teams have access to the data they need to do their jobs effectively.
Focus on using data to drive decision making: The ultimate goal of introducing data culture to an organization is to use data to make better decisions. This can involve incorporating data into regular decision-making processes, as well as using data to test hypotheses and evaluate the effectiveness of different strategies.
Overall, introducing data culture to an organization is a process that requires patience, commitment, and a focus on using data to drive decision making. By following these suggestions, you can help your organization to become more data-driven and better equipped to make informed, evidence-based decisions.
Start with the basics: Make sure that everyone in the organization understands the basics of data and its potential value. This can include training on topics such as data literacy, statistics, and data visualization.
Get leadership buy-in: It is important to have support from leadership in order to successfully introduce data culture to an organization. This can involve setting goals and metrics for data-driven decision making, as well as providing resources and support for implementing data-related initiatives.
Encourage collaboration: Data culture is all about using data to make better decisions, so it is important to encourage collaboration and cross-functional teamwork. This can involve setting up regular meetings for discussing data-related issues and making sure that different teams have access to the data they need to do their jobs effectively.
Focus on using data to drive decision making: The ultimate goal of introducing data culture to an organization is to use data to make better decisions. This can involve incorporating data into regular decision-making processes, as well as using data to test hypotheses and evaluate the effectiveness of different strategies.
Overall, introducing data culture to an organization is a process that requires patience, commitment, and a focus on using data to drive decision making. By following these suggestions, you can help your organization to become more data-driven and better equipped to make informed, evidence-based decisions.
Why Work with bi concepts?
BI Concepts is a reliable and responsible partner for your DWH projects. We can provide for all the requirements from data integration, storing and transforming and visualisation, to mention the most important ones. (Orchestration, data quality assurance, permission management, etc.)
Cost and Time: While many of our competitors will stay for months on your premises to find out again and again what a functional basic setup is, we come in with predefined configurations and can roll out our proven base setup from the beginning.
Cost and Time: While many of our competitors will stay for months on your premises to find out again and again what a functional basic setup is, we come in with predefined configurations and can roll out our proven base setup from the beginning.
What are the elements to consider when calculating the ROI of a modern warehouse stack?
When calculating the return on investment (ROI) of a modern warehouse stack, there are several key elements to consider. These can include the initial cost of the warehouse technology, the ongoing costs of maintaining and operating the technology, the expected increase in productivity and efficiency, and any potential cost savings from implementing the technology. It is also important to consider the potential for increased sales and revenue as a result of implementing the technology. Additionally, the ROI calculation should take into account any potential risks or challenges associated with implementing the technology, as well as the overall goals and objectives of the business.