We recently sat down with Sandeep Uttamchandani, Chief Product Officer at Unravel, to discuss the top cloud data migration challenges in 2022. No question, the pace of data pipelines moving to the cloud is accelerating. But as we see more enterprises moving to the cloud, we also hear more stories about how migrations went off the rails. One report says that 90% of CIOs experience failure or disruption of data migration projects due to the complexity of moving from on-prem to the cloud. Here are Dr.

Unlimited resyncs, moving from credits to dollars, new pricing plan, and more.

Stuck in a high traffic no sales situation for your online store? Dig into these common reasons for low conversion along with their solutions.

Flow Engineering is the science of creating, visualizing and optimizing the flow of value from your company to the customers. In the end that is the million-dollar (likely more) challenge of most product companies: How to we create value in the form of product and services and ship those to our customers as quickly, sustainably and frictionless as possible.

ETL (Extract, Transform, Load) is an important part of how companies manage their data. Using the ETL framework, it is possible to take the data your business collects and put it into cloud databases. Aggregating data on a massive scale becomes possible since all of the data sent to the data warehouse can be transformed into something that they can store and effectively manage.

Okay, I admit, the title is a little click-batey, but it does hold some truth! I spent the holidays up in the mountains, and if you live in the northern hemisphere like me, you know that means that I spent the holidays either celebrating or cursing the snow. When I was a kid, during this time of year we would always do an art project making snowflakes. We would bust out the scissors, glue, paper, string, and glitter, and go to work.

More than a decade into the Internet of Things (IoT) era, the immense potential of IoT is becoming real. We’re moving from proof of concepts and pilots to projects at scale. What’s become increasingly clear is the vast complexity of deploying IoT solutions at scale and the necessity to do so to become a data-driven business.

The amount of money invested in “Transformation Programs” is staggering. In the past 20 years we have seen Digital and Agile Transformation Programs grow and swell to $1.3 trillion dollars in 2020 alone. According to the HBR, 70% of that spend is wasted. Many companies miss a key component in their Transformation Program. How will a Product-Led Transformation be different?

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?