Google Data Studio is a beautiful visualization tool that turns your data into compelling story-telling reports. But before you can visualize your data, you have to collect it, clean it, and validate it. This is where Keboola comes in. Keboola is the Data Stack as a Service (DaaS) platform that helps you with all your data operations - from building and automating ETL pipelines to data governance.

In the era of big data, the world is producing more information than it can consume. Every minute of the day: Smart companies took notice of the growth in data and turned it into an opportunity for company growth. But having a lot of data is just part of the recipe. You also need to have technical data experts, who can turn the raw data into manageable operations that deliver revenue-generating insights. This led to the job roles of the data engineers and data scientists, that joined data teams.

Data analytics is invaluable to companies that want to drive growth. Research from giants like McKinsey, the Financial Times, and Google confirms it: Companies that rely on data analytics to drive business decision-making grow 2.5x faster than their lagging competitors. So how does descriptive analytics fit into the wider frame of data analytics?

We’ve got some exciting news! Are you ready? Drum roll, please… We are proud and excited to announce our partnership with ThoughtSpot! You can now design and deploy your end-to-end data stack and analytics in a few clicks. It’s a major shift from how much time and resources it used to take to get from data to insights. This partnership unlocks your data capabilities and supercharges your growth. Keboola gets all your data nice and clean and delivers it to the destination of your choice.

Yes, I’ve said it. The modern data stack is a pain to work with. But it wasn’t always like that. As companies realized they can leverage data to accelerate growth new data tools were invented. From NoSQL databases that specialize in processing specific data structures (graph anyone?) to the Python-Pandas-like Spark ecosystem that allows you to run queries on Big Data (capital B, mind you). But with every new tool added to the data stack, the complexity increased.

ETL pipelines help companies extract, transform, and load data so it is ready to provide insights and value to the company. But running a smooth data operation depends on building reliable and scalable data ingestion pipelines. SaaS vendors like Keboola and Azure Data Factory take away the heavy lifting.

Home Credit Group is an international provider of consumer finance with over 117,000 employees and operations across three continents and nine countries. With 28 million active customers and central financial sector regulators, keeping a close eye, handling data safely, accurately, and swiftly is a top priority.

To understand the current challenges and future priorities of data operations, we surveyed 85 data leaders across multiple global verticals - from retail to marketing, from software to fintech. The CEOs, CTOs, CCOs, VPs of Data, and Heads of Strategy all chipped in to unveil how they run data operations today and what they foresee in the future of data operations.

Data warehouses are the central data repository that allows Enterprises to consolidate data, automate data operations, and use the central repository to support all reporting, business intelligence (BI), analytics, and decision-making throughout the enterprise. But designing a data warehouse architecture can be quite challenging.

Analysts and data scientists use SQL queries to pull data from the data storage underbelly of an enterprise. They mold the data, reshape it, and analyze it, so it can offer revenue-generating business insights to the company. But analytics is only as good as the material it works with. That is, if the underlying data is missing, compromised, incomplete, or wrong, so will the data analysis and inferences derived from it.