We’ve all heard the war stories born out of wrong data: These stories don’t just make you and your company look like fools, they also cause great economic damages. And the more your enterprise relies on data, the greater the potential for harm. Here, we take a look at what data quality is and how the entire data quality management process can be improved.

On average, data-driven companies grow more than 30% every year. Because of the competitive advantage that data confers to incumbents who are capable of extracting value from it, it has been called the new oil. Companies are tapping into this well of resources because of the advantages that it has to offer: But using data to run your operations poses its own set of challenges.

Data architecture is a hot topic right now. And rightfully so. Technological advances bring out a myriad of new solutions that go beyond the traditional relational databases and data warehouses. They enable companies to accelerate their entire data pipeline (or at least remove painful bottlenecks) and shorten the analytic cycles. The portfolio of data assets managed by companies is also growing.

Over the past few months, we’ve been considering how to create a platform that’s accessible to everyone. With that said, we’re happy to announce that you can now use Keboola Connection for free! No contract, no talking to our (albeit incredibly lovely) sales team - just jump in and start building.

Companies use their data to accelerate business growth and overtake their competitors. To achieve this, they invest a lot in their ETL (extract-transform-load) operations, which take raw data and transform it into actionable information. It’s no wonder, then, that ETL testing is a crucial part of a well-functioning ETL process, since the ETL process generates mission-critical data.

Random forest is one of the most widely used machine learning algorithms in real production settings.

The decision tree algorithm - used within an ensemble method like the random forest - is one of the most widely used machine learning algorithms in real production settings.

Cluster analysis is a process used in artificial intelligence and data mining to discover the hidden structure in your data. There is no single cluster analysis algorithm. Instead, data practitioners choose the algorithm which best fits their needs for structure discovery. Here, we present a comprehensive overview of cluster analysis, which can be used as a guide for both beginners and advanced data scientists.

Linear regression, alongside logistic regression, is one of the most widely used machine learning algorithms in real production settings. Here, we present a comprehensive analysis of linear regression, which can be used as a guide for both beginners and advanced data scientists alike.

Imagine going to work only to find that your inbox is flooded with customers telling you how happy they are with your software. People are in such a hurry to download your app, you need to scale your servers to meet the demand before the infrastructure crashes. Your phone rings: it’s a tech journalist trying to book an interview with you about your company's growth. This is the dream for every business owner and entrepreneur. But the reality is often in stark contrast to the scenario above.