Companies deploying big data analytics to gain competitive advantage can quickly sour their successes by lacking a big data governance strategy. Which turns their data assets into data liabilities. In this article, we dive into the field of information governance and information management and explore how to set up and automate a big data governance program for success. Big data governance is a set of processes and principles that ensure the high value of data throughout its lifecycle.

With the steep rise of data, smart businesses have started capitalizing on this new oil to build a new type of products and services: data applications. Admittedly, the engineering and business development of data apps overlaps with their cousins - the trusty desktop app and the well-known web app, … But there is a core difference that sets data applications apart: they are first and foremost about the data they use to deliver value.

In their quest to find out if Keboola could be of wider benefit to a company that has so far been using the platform only as their ETL solution, Michal Hruska, a senior data consultant at Keboola, and Pavel Dolezal, Keboola’s CEO, met with Tim, the company’s data analyst. They sat down to talk about the beaten tracks of working with data and its challenges and the ways Keboola can help solve them.

It’s been almost a year since I wrote about using Keboola Connection in Keboola. A lot of things have happened since then: my Bloodborne board game finally arrived, I'm a double uncle… oh, and I got engaged. I also celebrated another anniversary this month - seven years of working at Keboola! And I believe that we’ve made some great progress yet again. Last time, I gave somewhat of an intro to our internal reporting.

In the last year, businesses have lost $756 billion because of poor eCommerce personalization. Customers have become accustomed to a tailored digital service. Unless you start gathering data about your customers and improving their online shopping experience, customers will walk their wallets to your competitor, who will offer them a better service. We will look at 7 use cases of how to better collect customer data in your eCommerce and put it to good use. Don’t worry.

Running an eCommerce without analytics is like driving a car blindfolded. Sure, you will go places, but one wrong stir and you might crash. In this article, we look at the best eCommerce analytic tools that help you run your online shop in 2021. This will not be your usual listicle.

According to the European Banking Authority report on Advanced Analytics and Big Data in banking, the implementation of data technologies, infrastructure, and practices is still at “an early stage”. The game is on for early contenders in this winner-takes-most market. Banks that move quickly are likely to get ahead of the curve, grabbing more of the market pie before others rise to the challenge.

Supply chain 4.0 is reshaping the global value chains. Advanced technologies like the Internet of Things (IoT), big data analytics, and autonomous robotics are transforming the model of supply chain management. Data is being produced, collected, analyzed, and productized at speeds and scales which were unimaginable a decade ago. The race to capitalize on the value of supply chain 4.0 is on.

There are over 300 SaaS applications that help you automate your data operations. In this crowd of potential solutions, how do you narrow it down to the winning horse? Here we compare three top contenders for your data operations: from data ingestion, via ETL to full data management, we compare side-by-side Fivetran, Stitch, and Keboola to shed light on their respective strengths and weaknesses. Fivetran, Stitch, and Keboola are all cloud-based data platforms.

Fraud comes in different forms, from client-facing credit card fraud to internal fraudsters twisting the loan portfolio. Banks (and other financial institutions) need to stay vigilant and act fast to prevent the loss of both money and reputation that follows each fraudulent incident. Fraud is expensive, but fraud prevention, detection and remediation can also be costly.