Systems | Development | Analytics | API | Testing

The 6 common data mistakes that could be holding your business back-and how to avoid them

Data is everywhere–driving the evolution of technology, changing the way we do business, transforming what it means to be a customer. Yet, too many businesses are still operating in a data-aware state and not truly adapting to a data-driven mentality. According to Deloitte Insights, just 1 in 10 executives believe that their employees can actually use data to make decisions.

How exploratory testing tools benefit QA teams

Exploratory testing is a successful method for locating undiscovered defects in software. However, many companies request that testers use scripted test cases based on predetermined product requirements rather than exploratory testing. Although scripted test cases help find faults in a product's code, exploratory tests assist the QA team in finding difficult-to-predict problems within the features of the finished product.

Distributed Feature Store Ingestion with Iguazio, Snowflake, and Spark

Enterprises who are actively increasing their AI maturity in a bid to achieve business transformations often find that with increased maturity comes increased complexity. For use cases that require very large datasets, the tech stacks required to meet business needs quickly become unwieldy.

What is DataOps Observability?

Data teams like yours face new challenges as they manage an increasing variety of data formats, expanding use cases, and as data volumes double every three years. Organizations increasingly depend on new data products to meet their financial objectives. Join SanjMo Advisory Services Co-Founder Sanjeev Mohan and Unravel Data Vice President of Solutions Engineering Chris Santiago to learn.

The top 6 attributes of a data leader

We’re in the defining decade of data. Data underpins the technologies transforming how we work, communicate, socialize and buy. If you want to take part in the revolution, you need to become—or hire—a data leader. But what does that even mean? What sets data leaders apart from the average data-aware professional? And how can we become data leaders?