If an organization is to be productive, it needs to work with the right data at the right times exactly where it needs them, across the whole structure and in each of its departments.
Heureka Group is an online shopping advisor that prides itself in providing simple, fast, secure, and enjoyable e-commerce and price-comparison solutions across central and eastern Europe. In less than 15 years, the company has grown to more than 20 million monthly users, becoming one of Europe’s leading e-commerce platforms. Heureka Group continues to build on that success by launching and acquiring e-commerce clients across the region.
Olfin Car is a leading seller of new and used cars in the Czech Republic with additional services in the field of financing, authorized car service, and insurance. They have sales of up to two billion CZK and sell over 2500 cars yearly. By combining data analysis, reporting and targeted marketing Olfin Car was able to fundamentally improve the company results both in online sales and in working with data. They ended up running all data processes in Keboola with the help of our partners Marketing BI.
Revolt BI is a consultancy and data implementation agency that provides comprehensive business intelligence solutions for companies of all sizes, by implementing best-of-breed solutions available on the market. For DataOps, they swear by Keboola to bring all the data neatly together and to automatically process it according to the needs of their clients which in total have over 6,9 billion dollars in revenue combined.
Experimentation and data drive growth. But the question is, how can you set up a self-driving experimentation machine that helps you run marketing tests. And is that the best use of your marketing team’s time?
Understand the tradeoffs to make the best choice “Why are you still doing ETL pipelines?” “The Data Warehouse is the only way you can keep data quality high, despite the extra data modeling needed.” “Have you not heard of data mesh beforehand? It solves all centralization problems.” When it comes to data management, there are as many opinions as there are data managers. This is not to say there aren’t any good answers or right principles to follow.
Companies move as fast as their slowest insight. In the era of big data, information is often produced faster than it can be consolidated and analyzed throughout the Enterprise. Analysts battle for the limited engineering resources, so they can construct data sets for their analytics endeavors to ultimately provide insights to fast-growing enterprises.
Data integration is the data engineering process of combining data from disparate sources into a single unified view of the data. The process begins with data ingestion from different source systems. This includes data extraction from disparate sources, data transformations or cleaning, and loading the data into a single repository - anything from Excel data sets to Enterprise data stores.