Empower everyone to explore data

A self service business intelligence and analytics solution built for your cloud data warehouse. Empower everyone to explore data.

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Sigma Computing

Time: 2016 - present     

Role: Product designer, researcher      

Link: www.sigmacomputing.com

Sigma is the only self service BI and data analytics tool that is made specifically for business users. Build dashboards, charts, tables, and reports in a familiar spreadsheet interface - all without any programming involved.  

Understanding the Problem

Until relatively recently data was structured and small in size. It was able to be analyzed either manually or with the use of simple tools and algorithms. With the advancement of technology and the internet, large amounts of data is accumulating everyday. Every click, purchase, and customer interaction generates data. This is often semi-structured, or completely unstructured.

 

To help us make sense of this growing mass of unstructured data we need more advanced analytical tools and services to transform data into actionable insights that inform an organization's business decisions. This is business intelligence. 

The Problems

Limited to data teams 

Today data scientists are the professionals who knows how to tease actionable insights out of gigabytes of data. Without these experts who turn cutting-edge technology into actionable insights, Big Data is nothing. They spend a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills.

Requires SQL

Structured Query Language (SQL) is the standard programming language used to communicate with a database. It is primarily used to find and pull information from large databases (relational database management systems).

SQL has become a very important tool in a data scientist’s toolbox since it is critical in accessing, updating, inserting, manipulating and modifying data. It helps in communicating with relational databases to be able to understand the dataset and use it appropriately.

Bottleneck = Frustration

With data informing just about every business decision, business users currently require the help of data teams/data scientists to get answers to their data questions. This could take anywhere from 1 week to 3 months. 

 

Frustration goes hand-in-hand with data teams as they are professionals who are qualified to create complex models and write scripts are instead being tasked with manually pulling basic reports for their managers.

Nothing Truly Helps

Needs SQL Knowledge or proprietary language

Steep learning curve

Not for the business user

Not flexible

Only does certain things well (not end-to-end)

Requires multiple products

Very expensive

The Goal

The following are some of the problems of the tools and situations that exist today.

Self Service

This necessitates a self-service model that empowers the domain experts, aka business users, to be able to explore and answer their own data questions. 

 

The Process

Research, research, research.

The Beginning

I was recruited by Sutter Hill Ventures to join a portfolio company that was at Series A as the 8th employee. Six months later, everything got scratched and I became the only designer. I started a journey with the co-founders and engineers to design a product from scratch.

Research interviews

First, we needed to understand the problem and what users were currently using today. 

We didn't have UX researchers or product managers, so I started by interviewing users in our own network from small, medium, and large sized companies to understand how users were accessing data, what they were doing with it, and what the final output was. 

Research findings

After meeting with a number of users from data teams, analysts, to those who work with data, we discovered the following things:

  • Users are used to a tabular UI (like Excel, Google Sheets)

  • 2 sides: person making data available for others vs the person consuming the data

  • Business users (non technical) wouldn’t want to view dashboards in Excel/G-sheets, they wanted something simple and accessible on the web

  • They also didn’t want to download anything to add to desktop. Wanted it on the web/cloud

  • The #1 most commonly used tool amongst all business users was Excel (spreadsheet)

Business Goal: Data Science for everyone

We placed our bet on making a tool that empowers the business user without having to write a line of SQL. At this time, we didn't know what the product would look like or what it would entail. 

All we knew were:

1) There's a huge market (BI & Analytics market is expected to reach $29 billion by 2022)

2) More and more companies are moving to the cloud

3) There's a big problem no one has solved yet – no tool for business users

My Goal: Simplifying the complex

How do I create an interface to something users usually create writing SQL?

How do I address what users want?

BI Architecture

I also studied the overall framework for organizing the data, information management and technology components that are used to build BI systems for reporting and data analysis. As well as familiarized myself with other phases and products of the big data architecture. 

Competitive analysis

I identified all the major competitors and researched their products to analyze their user journeys, task flows, target audience, strengths/weaknesses, market positioning, and to identify any gaps in the product.  

Market analysis

Shifts in the market

Acceptance of a cloud based solution

Design Process

The following are some of the problems of the tools and situations that exist today.

Personas

My team and I interviewed over 30+ users at different organizations to understand workflow, goals, pain points, etc.

 

4 main user types

  • Admin / BI Engineer / Data Team (knows SQL, models data for business users)

  • Power user / Modeler (familiar with SQL, but doesn't want to write it, domain experts)

  • Analyst / Business User (doesn't know SQL, needs data to do their job, ex: PM)

  • Consumer / Business User (limited on time, wants dashboards & reports, C-level)

Ideation & Wireframes

I accessed the pros and cons of each product, borrowed concepts of what I liked, but improved them with my own recommendations.

Build, build, build

Most BI and analytics tools have common features. We accessed what were the minimum viable products (MVP) for our product and designed and built out features as fast as we could.

Version 1: Data Wrangling

Worksheet

  • Data analysis - filters, groupings/levels, formula bar, sorting

  • Ad hoc analysis

  • Ad hoc query tools

Data Sources - Tables, Joins

Navigation 

File management

Sign up/onboarding

Settings - profile, teams, invite

Design System - GUI

Version 2: Visualizations

Visualizations

Dashboards

Version 3: Modeling

Trials

Modeling

Version 4: (Launched Q1 2020)

Improved Dashboards

Embed

User Test, gather feedback, iterate

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Sell, sell, sell

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Final Designs

The following are some of the problems of the tools and situations that exist today.

Worksheet

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Worksheet

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Worksheet

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Worksheet

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Worksheet

Most BI and analytics tools had somewhat similiar features. We accessed what made sense for our product and designed and built out as fast as we could.

Review Summary

The following are some of the problems of the tools and situations that exist today.

Broader data access to business users

Broke down data barriers

Powered a 90% reduction in engineering time required to report insights to users

Empowers nonSQL users

Main adopters/biggest fans are the data scientists/data teams at organizations wanting to empower their business users

 

 0 customers to X customers

Raised round C

X number of customers

$ of sales

60+ employees

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