Systems | Development | Analytics | API | Testing

Latest Posts

Build data apps with Streamlit + ThoughtSpot APIs

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.

6 most useful data visualization principles for analysts

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.

3 types of data models and when to use them

Data modeling is the process of organizing your data into a structure, to make it more accessible and useful. Essentially, you’re deciding how the data will move in and out of your database, and mapping the data so that it remains clean and consistent. ThoughtSpot can take advantage of many kinds of data models, as well as modeling languages. Since you know your data best, it’s usually a good idea to spend some time customizing the modeling settings.

How to build a self-service BI strategy

Think about the times you've wished you had more insight into your business data. Or all of the times you wished you could answer questions about your business performance without waiting for someone else to get back to you. Gone are the days when businesses rely solely on IT staff to provide reports and analytics. With self-service business intelligence (BI), users can create their own reports, dashboards, and data visualizations without relying on IT help.

How ThoughtSpot Uses ThoughtSpot for Field Marketing

As ThoughtSpot’s SVP of Corporate Marketing I oversee a field marketing team that acts as the glue between our Marketing and Field Sales teams. When people talk about field marketing, they’re often just thinking of events — but we have a far broader remit than that. Each member of the Field Marketing team sits within a specific sales region, acting as a kind of regional CMO.

The case for a query modification language and why dashboards are dead

In 1895, a German physicist was trying to determine if he could observe cathode rays escaping from a glass tube and noticed an unexpected glow on a fluorescent screen several feet away. On further examination, it turned out to be a different kind of radiation that we now know as X-ray. Fast forward to today and you can’t even imagine diagnosing many medical problems without the X-ray.

Activate your data: How to get started with ThoughtSpot Sync

Every data team wants to make insights more actionable for frontline business users. The only question is how. You know they spend the majority of their time in business-critical tools like HubSpot, Slack, and Microsoft Teams. So why not bring the data-driven insights created in ThoughtSpot to the apps they use most? With ThoughtSpot Sync, you can. Starting today, ThoughtSpot customers will be able to send insights directly from ThoughtSpot to Google Sheets, Slack, and Microsoft Teams.

9 best practices and tips to follow for effective data visualization

Visualizing data is an important aspect of presenting insights clearly. But it's not always easy to create an effective visualization that people will understand on their first glance, or even second. So how do you create the kinds of graphs and tables that leave key stakeholders thinking, " Wow! I need this information!" In this post, we will discuss the top nine best practices for data visualization.

Three dbt data modeling mistakes and how to fix them

When I first started my role as an analytics engineer, I was tasked with rewriting a bunch of data models that were written in the past by contractors. These models were taking over 24 hours to run and often failed to run at all. They were poorly thought out and contained a bunch of “quick fix” code rather than being designed with the entire flow of the model in mind.