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.

Whether you call it self-service analytics or self-service business intelligence (BI), there has been much discussion about the perils, myths, promises, and prospects of successfully building self-service capability. Going forward, I’ll use the phrase “self-service BI” but you are welcome to substitute the words “self-service analytics”.So, is self-service BI actually attainable or just snake oil?

To quote Hemingway: change happens gradually, then suddenly. We see this in the world around us. Think back to 2019. There’s no denying how much the pandemic reshaped our professional and personal lives, with technology driving this change at massive scale. Yet these changes, despite their ubiquity, are really the culmination of trends like cloud and automation that were well underway.

I’ve encountered a thousand different problems with spreadsheets, data importing, and flat files over the last 20 years. While there are new tools that help make the most of this data, it's not always simple. I’ve distilled this list down to the most common issues among all the databases I’ve worked with. I’m giving you my favorite magic fixes here. (Well, okay, they aren’t really “magic” but some of them took me a long time to figure out.)

Businesses have been scaling rapidly in the cloud, driven by the pandemic and lured by the promise of agility and flexibility. But here’s a dirty little secret anyone who works in data knows. Despite the value of the cloud, tons of data hasn’t made it there. So, where is it? Spreadsheets. Still the stalwart workhorse, hero, and bane of the business world. We all love how Google revolutionized this world by bringing spreadsheets to the cloud.

SQL is the universal language of data modeling. While it is not what everyone uses, it is what most analytics engineers use. SQL is found all across the most popular modern data stack tools; ThoughtSpot’s SearchIQ query engine translates natural language query into complex SQL commands on the fly, dbt built the entire premise of their tool around SQL and Jinja. And within your Snowflake data platform, it’s used to query all your tables.

I’ve been following the Streamlit framework for a while, since Snowflake announced that they would acquire it to enable data engineers to quick spin up data apps. I decided to play around with it and see how we could leverage the speed of creating an app along with the benefits that ThoughtSpot provides, especially around the ability to use NLP for search terms. Streamlit is built in Python.

If you haven’t heard of dbt, you’re missing out on one of the hottest technologies in data. dbt has been adopted by more than 15,000 organizations looking for a SQL-friendly workflow to transform the data inside their cloud data platform.

The difference between consuming data and actioning it often comes down to one thing: effective data visualization. Case in point? The John Snow’s famous cholera map. In 1854, John Snow (no, not that one) mapped cholera cases during an outbreak in London. Snow’s simple map uncovered a brand new pattern in the data—the cases all clustered around a shared water pump.