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.
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.
