Systems | Development | Analytics | API | Testing

Analytics engineer is the latest role that combines the technical skills of a data engineer with the business knowledge of a data analyst. They are typically coding in SQL, building dbt data models, and automating data pipelines. You could say they own the steps between data ingestion and orchestration. Whether you are a seasoned analytics engineer or new to the field, it’s important to continually learn new things and improve the work you’ve already done.

Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Data plays a profound role in finance. In fact, some might argue that finance professionals are some of the most data-driven individuals in an organization. That’s because finance data, and the insights you draw from it, can literally make or break a company. This is especially true in times of economic uncertainty, when businesses are trying to make data-driven decisions about where to invest and cut resource allocation.

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.

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?

The FIFA World Cup 2022 is nearing its end, and the final game promises to be a nail biter. What started with 32 countries battling it out for close to a month, will now culminate in a play-off between Argentina and France. FIFA projects more than 5 billion people to tune in for the tournament, perhaps making the World Cup Final 2022 the most watched event of the year!

Late last June, I had the opportunity to attend a ThoughtSpot User Group session in London and share how ThoughtSpot has impacted global procurement at Roche—notably how it’s helped my team not become a dashboard factory for the rest of our business.

When setting up a modern data stack, data warehouse modeling is often the very first step. It is important to create an architecture that supports the data models that you wish to build. I often see people going straight to writing complex transformations before thinking about how they want to organize the databases, schemas, and tables within their warehouse. To succeed, it is key to design your data warehouse with your models in mind before starting the modeling process.

Business monitoring is essential to a company’s success. Whether you’re improving efficiency, saving costs, planning inventory, or tracking goals, you need to define metrics and monitor them regularly to make progress. With ThoughtSpot, business monitoring is an intuitive experience that starts with visualizing your KPIs in real-time so you can take action when there’s movement.

In 1987, economist Robert Solow declared, “You can see the computer age everywhere but in the productivity statistics.” He noted that despite massive investments in computer hardware and software, companies saw a decrease in fundamental productivity measures.