GoDaddy uses Amazon QuickSight and Amazon Q to compress business intelligence analytics from weeks to minutes

This is a guest post written by Mayur Patel from GoDaddy.

Founded in 1997, GoDaddy’s global solutions help 21 million customers seamlessly connect entrepreneurs’ identity and presence with commerce, leading to profitable growth. GoDaddy’s Data and Analytics (DNA) team is responsible for GoDaddy’s data platform, business analytics, machine learning and AI initiatives. In this post, GoDaddy shares why it partnered with Amazon QuickSight to shorten its multi-week, manual data insight process and leveraged QuickSight for Generative BI.

At GoDaddy, our heritage is domain registration and web hosting. It’s still a cornerstone of our strategy, but over time that’s evolved to include powerful tools and AI for marketing, branding, and commerce for our customers.

To support this evolution, we needed to analyze data to make decisions faster. Our team of business analysts works tirelessly to instrument the business, helping internal stakeholders make sense of our data and use it to make data-driven decisions. We wanted to overhaul our manual processes to accelerate time-to-insight and improve governance.

Manual processes and data preparation

We realized that many of our challenges stemmed from manual data preparation and the inflexibility of our current legacy business intelligence (BI) tool. Over time, we grew to more than 5,000 dashboards using this tool, making governance and support onerous.

Our business analysts took an average of 3-4 weeks to create a dashboard, with much of that time spent manually translating reporting needs into dashboards and normalizing data for the dashboards. Because of this high-touch process, self-service analytics were difficult and required engineering support.

We wanted to improve this workflow to achieve four key goals:

• Accelerate insight generation from our data
• Consolidate vendors and tools to reduce operational overhead
• Enhance our data governance and tooling capabilities
• Most importantly, drive a paradigm shift – move business analysts away from dashboard/report building and manual data prep, towards addressing high-impact business challenges and business outcomes

Why GoDaddy chose Amazon QuickSight

To achieve our four key goals, we explored alternatives to our current legacy BI tool and evaluated improvements we could make to our traditional BI use cases. These included:

• Anomaly detection in gross cash receipts
• Customer care agent performance
• In-depth, 360 views of customers and their web activity
• Scorecards used to surface problems for executive leaders

With such diverse use cases, we needed a tool to collate and quickly analyze various data. And we required a tool that would scale self-service access to our more than 3,000 internal users.

Amazon QuickSight stood out as the natural choice for these requirements. Because most of our infrastructure was in AWS, we knew QuickSight would integrate seamlessly with our data platform. No custom development or integration would be required.

The easy-to-use user interface and connectors for QuickSight were also important. We needed an intuitive tool for our business analysts that wouldn’t require data engineers’ assistance in preparing and extracting data.

Finally, we are particularly excited about the new Generative BI capabilities of Amazon Q in QuickSight, which will shift GoDaddy from a dashboard-driven culture to one using natural language for data and insight discovery. Amazon Q in QuickSight combines the power of generative AI with BI to allow data discovery and decision-making through natural language queries, removing the need for specialized training.

Fast deployment on complex data

We collaborated with AWS and rolled out QuickSight in two months. By April of 2024, we onboarded 176 authors and 200 unique users.

Users access QuickSight via Okta, which is integrated with AWS IAM Identity Center for identity federation. This ensures a seamless experience for users while bolstering data security and governance.

QuickSight pulls data from several different systems, including Amazon Redshift, Amazon Athena, and ServiceNow. It delivers full visibility across our disparate data sets: data marts, data lakes, and applications are all connected through QuickSight. Creating SPICE (Super-fast, Parallel, In-memory Calculation Engine) datasets provides consistently fast performance, no matter how many concurrent users are connected to the application. The datasets are refreshed periodically ensuring users access to recent data.

The following figure depicts GoDaddy’s information architecture, with data marts, data lakes, and applications all feeding into QuickSight.

Business intelligence: Supercharged with Q Topics

We’re excited about how Generative BI capabilities of Amazon Q in QuickSight can transform our approach to data analytics. QuickSight’s Q Topics feature enables us to slice our data in a way that empowers our business users to explore data through natural language queries, rather than pre-built dashboards. This allows us to set clear parameters and divisions throughout our data, enabling business stakeholders to answer questions of the data instead of building another dashboard. By binding Amazon Q in QuickSight to any data source, business analysts can avoid having to create dashboards for bespoke needs and instead connect business stakeholders to the data they need using natural language. This makes it easier for our users to explore data and get insights.

This also means we no longer need to build and populate new dashboards or reports for a specific insight – users can use natural language to probe and explore data in a fraction of the time that they did with our old tool. QuickSight’s Data Stories capability allows users to create rich, data-driven narratives that can surface new insights and tell compelling stories about the underlying data.

QuickSight dramatically reduced time-to-insight. A survey of business analysts showed they took over a week to prepare a dataset and a full day to explore that data using our old tool. That time was reduced to less than four days for data preparation and less than 10 minutes for exploring the information using QuickSight.

Amazon Q and automation provided by QuickSight has unburdened BAs from dashboard creation and freed up time for them to spend on strategic initiatives and innovation.

QuickSight’s Future at GoDaddy

It’s been only a few months since we started working with QuickSight, but we are already planning scaling initiatives and feature enhancements.

We aim to have 1,000 QuickSight users by the end of 2024 and to expand to the entire user base of over 3,000 users by the end of 2025.

From a technical perspective, we will use IAM Identity Center to augment security, providing a single place to manage user access across the AWS products that we use.

In addition, we plan to connect Salesforce, Confluence, and Jira as data sources for QuickSight to draw from, expanding the pool of data our teams can use to analyze vital information.

And finally, we’re excited to explore the capabilities of QuickSight within our internal experimentation platform, HiveMind. We will be testing QuickSight as an embedded feature for both HiveMind and our care insights dashboards for customer care center agents.

To explore QuickSight and learn how its innovative features can deliver new applications, reduce in-house development time, and make data insights faster, smarter, and more accessible, visit the Amazon QuickSight page.

About the author

Mayur Patel is a Director of Software Development at GoDaddy. He leads a team of engineers responsible for GoDaddy’s insights platform and is leading the transition at GoDaddy from traditional BI to generative AI, reducing time-to-insight for GoDaddy’s BA (business analyst) community and business users. Mayur is also a founding member and speaker at the Amazon QuickSight User Group – Phoenix. Prior to GoDaddy, Mayur held engineering leadership positions at AMEX and Atos Syntel.