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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.

The cloud is all the rage right now. The tech giants like Amazon and Google are almost exclusively focusing on cloud technologies. The small companies migrate to the cloud faster than birds fly south for the winter. So, what is so wrong with keeping your app and data on an on-premise server, safely locked in one of your offices?

Productboard is a customer-driven product management system, which enables companies to leverage customer feedback and data insights to fuel innovation, and ultimately, deliver products that customers will love. For a few years, the company worked with data consulting agencies, but things weren't working out. Productboard was using Keboola, but they weren't sure how to get the most out of it.

Transportation and logistics companies generate and consume more data than almost every other industry. Despite this, they still find themselves lagging behind other B2B verticals in their ability to turn a profit from data. With thinning profit margins and new contenders entering the logistics industry, the only way to outperform other companies is through brain, not brawn. Logistics analytics offers the edge over the competition.

The world produces more data than it can consume. Every minute, we watch more than 5 million videos and send over 200 million messages and emails. You read that right. Every. Single. Minute. Companies that want to tap into data-driven decision-making to dominate their competition need to collect the vast amounts of data produced and extract valuable insights using data analysis. But data collection can be extremely challenging.

Data is produced and consumed at volumes and speeds which were unimaginable just a decade ago.Top players have taken advantage of this growth. Tapping into data resources for actionable insights - aptly called the new oil - makes data-driven companies dominate their competition. But the proliferation of data can lead to growing pains. Companies find themselves increasingly incapacitated by the vast and messy nature of their in-house data.

The problem with modern analytics is that it overpromises and underdelivers. We can even quantify the disappointment: This begs the question: why even bother with analytics? Well, when analytics is done right, it pays back $13.01 for every dollar spent. In fact, data-driven companies outperform their competitors in almost every conceivable way. We have broached the topic of extracting value from data before, from how to set up the right data strategy to building a data-driven culture.

Data is the oil that fuels the growth of modern enterprises. But unless you have the tools to unlock the potential of data, you might be left stuck on the tracks as your competitors speed ahead. With the rise of Big Data, the nature of the data that we work with has changed drastically. Data scientists like to refer to the ‘3 Vs’ of Big Data: The 3 Vs of Big Data reshaped the data landscape as we knew it.