We are excited to announce Embedded Analytics Data Lab (EADL), a no-cost collaborative engagement that helps engineering and development teams cut down time required to launch applications with embedded analytics from Amazon QuickSight in production by providing hands-on guidance and architectural best practices.
Embedding rich analytics such as interactive visuals and dashboards directly into applications allows developers to create differentiated, analytics-driven experiences that enables end-users to make more informed decisions. QuickSight is a cloud-native, serverless business intelligence (BI) service that allows developers from enterprises and independent software vendors (ISVs) to incorporate powerful BI capabilities such as interactive visualizations, dashboards, and machine learning (ML)-powered natural language query (NLQ) using Amazon QuickSight Q into their applications and web portals, delivering insights to end-users where they are.
AWS Data Lab is an AWS offering that offers accelerated, joint engineering engagements between customers and AWS technical resources to create tangible deliverables that accelerate data, analytics, AI/ML, serverless, and containers modernization initiatives.
Today, with the new EADL offering, we’re bringing together the breadth of QuickSight’s embedding capabilities with proven expertise from AWS Data Lab. With EADL, AWS customers can request a hands-on session to prototype embedded analytics solutions, build custom architectures, and implement best practices with QuickSight-specialist Data Lab Solutions Architects. The output from this engagement is a customized solution that is specific to customer requirements, built using their data, in their AWS account, while providing hands-on learning to the engineering teams attending the lab. EADL engagements accelerate time from ideation to proof of concept to production by months, through tailored guidance while using resources across AWS teams to accelerate the rollout of embedded analytics features powered by QuickSight.
“We’re excited to announce the launch of the Embedded Analytics Data Lab that enables customers and ISVs to accelerate their embedded analytics offering using Amazon QuickSight. With Amazon QuickSight’s embedded analytics capabilities, AWS customers can integrate rich visuals and dashboards into their applications to scale to 100,000s of end-users, differentiating their user experiences—without any servers or infrastructure management. Embedded Analytics Data Lab helps demonstrate this business value in a matter of days by accelerating the QuickSight embedded journey for development teams.”
– Tracy Daugherty, General Manager, Amazon QuickSight.
Customers in EADL work closely with assigned AWS Data Lab Solutions Architect, solidifying the architecture design for their embedded analytics solution, including designing any data model and data pipeline components. The engagement then proceeds to the lab phase, where builders spend 2–4 days with their Solutions Architect, working backward from end goals and building a solution based on the previously defined architecture and real-time guidance from the Solutions Architect and other AWS service experts. Data Lab Solutions Architects also provide implementation guidance on data modeling, setting up multi-tenancy, enabling single sign-on with customers’ identity providers, enabling row- and column-level security, and tracking the health of the QuickSight environment. At lab completion, customers leave with a working prototype of their embedded analytics solution, built by their own builders in their AWS accounts that meet their requirements and specs.
Over the last year, we have worked closely with customers to help design and build their embedded analytics solutions. Some of these customers include BriteCore, Carbyne, and KRS.io.
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BriteCore is an enterprise-level insurance processing suite that relies on dashboards to provide operational tracking and trend insights to insurance carriers on data points such as insurance claims and losses by agency, policy type, and line of business. To provide a seamless experience for their over 125,000 customers, BriteCore sought to integrate their BI offerings with their core platform and deliver dashboards to customers as embedded visuals. BriteCore’s engineering and reporting and analytics teams engaged the AWS Data Lab to design and validate the best integration approach between QuickSight and their application and to jumpstart building their interactive, embedded QuickSight dashboards. |
“AWS Data Lab was pivotal in helping us build out our embedded analytics solution with the AWS suite of analytics services. Within 4 days, we built a working prototype of our multi-tenant solution with the right identity and security policies in place. Engaging with AWS Data Lab to build our solution definitely helped us reduce our time to production. Our customers now have even better insights into their business, and we will be able to deliver a much richer experience.”
– Supreet Oberoi, Senior Vice President of Engineering, BriteCore.
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Carbyne is the global leader in contact center solutions, enabling emergency contact centers and selected enterprises to connect with callers on any connected devices via highly secure communication channels without downloading a consumer app. Carbyne worked with AWS Data Lab to explore options for building a low-latency, multi-tenant analytical system that would enable them to generate meaningful insights using QuickSight’s interactive dashboards for call center owners who manage 911 calls. Example insights include 911 call duration ranges, peak time of day for callers, and percentage of abandoned vs. answered calls—all data points that help Carbyne customers measure the effectiveness of their emergency response systems and then provision staff and resources accordingly. These insights were then embedded into their application, enabling a seamless experience for the 911 call center managers. |
“This experience with the AWS Data Lab is what it means to be in true partnership. Data Lab’s support and efforts are much appreciated as we push innovative solutions to the public safety industry. I can say confidently that Data Lab’s support will reduce our time to production by weeks, if not months.”
– Alex Dizengof, Founder & CTO, Carbyne, Inc.
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KRS.io is a leader in coalition loyalty marketing connecting thousands of retailers with their customers on an intimate level with rewards programs and loyalty solutions. To truly democratize data, they set out to build a solution that harnesses the power of NQL. In a 1-day workshop with the AWS Data Lab team, KRS.io embedded QuickSight Q into Epiphany and successfully modeled 20 questions for their Profit Central back office accounting system, perpetual inventory, and loyalty datasets. |
“In business, speed matters. Working with AWS Data Lab accelerated our timeframe from proof of concept to deployment. I had zero-tolerance for risk and the Data Lab allowed my team to meet my high bar for security and reliability”
– Brian McManus, CTO, KRS.io.
Get started with EADL
Prerequisites required to qualify for this offering are:
- Valid embedded analytics use case.
- Ready and accessible data to be used with QuickSight.
- Available AWS sandbox or development environment to build the prototype. Data sources for QuickSight must be accessible through this sandbox account.
- Available webpages or assets to be used to embed the QuickSight visuals and dashboards.
- Full-time participation of at least two builders, including a builder that is comfortable and familiar with the web assets to be used for embedding.
| To get started, register now. Once registered, a member of the AWS team will contact you with next steps. |
About the Authors
Romit Girdhar manages Technical Product Management & Software Development teams for AWS Data Lab. He focuses on working backwards from customer outcomes to help accelerate their cloud journey. Romit has over a decade of experience working on engineering solutions for and with customers across two major public cloud companies – Amazon and Microsoft.
Kareem Syed-Mohammed is a Product Manager at Amazon QuickSight. He focuses on embedded analytics, APIs, and developer experience. Prior to QuickSight he has been with AWS Marketplace and Amazon retail as a PM. Kareem started his career as a developer and then PM for call center technologies, Local Expert and Ads for Expedia. He worked as a consultant with McKinsey and Company for a short while.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/big-data/introducing-embedded-analytics-data-lab-to-accelerate-integration-of-amazon-quicksight-analytics-into-applications/