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Driving Adoption of Tableau and Amazon Web Services

RevGen helped a large manufacturing and distribution client improve their data and analytics capabilities by stabilizing and enhancing their AWS data lake, data warehouse, and Tableau visualization environments.

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Project Overview

A large manufacturer and distributor of aftermarket automotive products had recently implemented Tableau and a number of Amazon Web Services (AWS) including Amazon Glue, Amazon S3, and Amazon Athena for their data and analytics environments. However, due to several challenges with the new environments, many users continued to utilize legacy reporting applications, creating additional overhead and cost for the maintenance of multiple technologies as well as data discrepancies between the disparate environments. 

Client Challenge

The slow performance of the Tableau implementation and fragility of the AWS environment coupled with data quality issues caused business users to revert to older reporting tools and data stores that were perceived to be more reliable.

Delayed reporting

The client was experiencing delays in distributing reporting to their business users. The large amount of data loaded daily into Tableau and the inability to load data incrementally meant that important reports were not available when the business needed them, which was critical for a multi-national organization working across many time zones.

Unreliable data

The client struggled with inaccurate data in their Tableau reports, data lake, and data warehouse model on Amazon S3 and Amazon Athena. This undermined confidence in the data and reporting.

Legacy reporting applications

Also, they had several legacy .NET reporting applications, each of which was custom coded and costly to maintain or modify. These applications were on-premises and sourced their data from Microsoft SQL Server.

Approach

RevGen partnered with key stakeholders in Sales and Finance to identify the greatest pain points that were hindering adoption of the Tableau implementation. From there, RevGen worked closely with IT to improve performance and stabilize the client’s data and analytics environments on Tableau and AWS.

Solution

The solution consisted of identifying and addressing the most pressing issues within the environment that limited user adoption.

Analyze

RevGen investigated the challenges facing the client to identify and prioritize fixes, best practices, and enhancements in Tableau, the data lake, and data warehouse model on Amazon S3 and Amazon Athena, and in the data pipelines in AWS Glue.

Stabilize

RevGen immediately began fixing the Python code in the AWS Glue jobs to address data quality issues by implementing best practices. We also built alerts utilizing Amazon EventBridge and AWS Lambda to quickly notify the client when processes failed or were delayed.

Enhance

After stabilizing the environment, RevGen began developing a “Version 2” of the data model in Tableau that utilized Tableau Data Extracts. This would address additional data quality issues, reduce load times, and lay the foundation for incremental data loads.

“The RevGen team is by far the most intelligent and capable team I have ever worked with. I trust you and your team 500% and look forward to working with you long term.”

Client Data Architect

Results

RevGen helped the client lay a foundation that would enable them to leverage more advanced data and analytic capabilities to support future growth.

Reduced load times

The execution time for the daily data load into Tableau from Amazon S3 was reduced by almost 50%, which put data in the hands of business users when they needed it.

Increased confidence

With the data quality fixes and alerting, the company has renewed confidence in their newly implemented (and stabilized) data and visualization tools, which has driven increased user adoption.

Ready for the future

The organization is ready to embrace the future with a solid data and analytics foundation and to retire their legacy .NET on-prem reporting applications. Together, RevGen and the client will continue to build the sustainable technology capability that will support higher advanced analytics maturity necessary to drive accelerated future growth.

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