Amazon Connect Data Lake Best Practices - Amazon Connect Data Lake Best Practices

Amazon Connect Data Lake Best Practices

Publication date: May 13, 2021

Abstract

Customer service is a crucial element of brand reputation and business success. Contact centers are vital to enabling a two-way agent-customer interaction and essential to delivering a superior customer service experience. Conversely, a poor experience can lead to customer churn. Organizations invest in omnichannel contact centers for a competitive edge in enhancing customer experience.

According to an Aberdeen survey, organizations manage an average of 33 unique data sources for analytics and experience 50% year-over-year data volume growth. Rapid data volume growth creates challenges in data management and storage capacity. Today, organizations are developing data lake strategies to harness intelligence from the diverse and ever-growing data. The survey indicates a 9% increase in organic revenue growth for organizations that implemented a data lake.

To get the most advanced analytics benefits, organizations need a robust platform and cost-effective solution to run a thriving contact center. Amazon Web Services (AWS) provides customers with a comprehensive set of services and a scalable platform to ensure high availability, security, and resiliency of a data lake in the cloud.

This whitepaper outlines the best practices for architecting a contact center data lake with Amazon Connect.

Are you Well-Architected?

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems on AWS. Using the Framework allows you to learn architectural best practices for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud.

In the Machine Learning Lens, we focus on how to design, deploy, and architect your machine learning workloads in the AWS Cloud. This lens adds to the best practices described in the Well-Architected Framework.

Introduction

Traditional on-premises contact centers often involve multiple proprietary systems, resulting in disparate data sources containing data in various formats. Challenges in standardizing and consolidating information slow down the discovery of new business insights or possible operational issues.

The following figure shows the architecture of a traditional on-premises contact center.


        A diagram showing Amazon Connect contact center data lake with AWS analytics and AI /
          ML services

On-premises contact center architecture

Data plays a crucial role in driving the success of a contact center. Managing data silos is inefficient and may lead to a poor customer experience. Customers expect a frictionless experience when connecting with a contact center. Having a streamlined data lake solution helps agents deliver a personalized experience to customers. The following figure shows a strategic approach to simplifying complex traditional contact center data spans across infrastructure, licensing, and maintenance environments into Amazon Connect.


        A diagram showing Amazon Connect contact center data lake with AWS analytics and AI /
          ML services

A strategic approach to simplifying complex traditional contact center data into Amazon Connect

A data lake is a centralized, curated, and secured repository that stores and governs all your structured and unstructured data in its native or transformed formats for analysis. AWS delivers the breadth and depth of services to build a secure, scalable, comprehensive, and cost-effective data lake solution. You can use the AWS services to ingest, store, find, process, and analyze data from a wide variety of sources.

This whitepaper provides architectural best practices to technology roles, such as chief technology officers (CTOs), architects, developers, and operations professionals when building a contact center data lake with Amazon Connect.