Systems | Development | Analytics | API | Testing

Not sure how to use customer data to improve your marketing? Check out these 7 tips coming straight from the pros.

The job of a modern marketer never stops. In today’s always-on, digital world you can spend forever tinkering with taglines and targeting and still never get to the bottom of why some campaigns perform while others don’t. Is your messaging personalized enough? Are you utilizing the right channels? Are you allocating your budget correctly? To dig into these insights you need data.

How life for data professionals keeps getting better (and more interesting!)

Gift guides come in all shapes and sizes. There are shopper’s guides for sporting goods and wine, aimed at travelers and crafty types, and offering electronics or candy. Since there is no gift guide we’re aware of for data buyers, this is our chance to create the first such guide. Is your wife, best friend, or dad a nerd? No, not that kind of nerd, not an over-the-counter nerd, a data nerd! If so, this stuff will stuff their stocking but good. Remember Sears’ Wish Book?

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way.

With each passing year, companies are becoming increasingly more dependent on APIs. Essentially, any organization looking to take advantage of the latest cloud technologies is dependent on APIs. Therefore, understanding how APIs work and how to best manage APIs is crucial. However, tackling APIs alone can be quite challenging for many companies.

In the ever-changing world of technology, the tools available to citizen integrators are constantly evolving as well. For citizen integrators, access to better low-code integration tools certainly makes their job much easier. However, with the industry landscape constantly changing, it can be difficult to keep up with all the latest trends and find the right low-code integration tools to best meet the needs of the company.

If you are new to creating pivot tables within Google sheets, this step-by-step guide will help you understand the basic concepts.

Machine learning (ML) model serving refers to the series of steps that allow you to create a service out of a trained model that a system can then ping to receive a relevant prediction output for an end user. These steps typically involve required pre-processing of the input, a prediction request to the model, and relevant post-processing of the model output to apply business logic.