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Scalable ILI Dashboard with AWS Lambda and Tableau Integration

A leading DEI consultancy was struggling with slow, outdated reporting systems, scattered data sources, and a lack of scalability. Their legacy database caused delays in processing survey data, leading to stale insights and a heavy manual reporting burden. NeenOpal’s Implementation Division stepped in to address these challenges by developing an Inclusive Leadership Index (ILI) dashboard using Tableau and AWS.

Scalable ILI Dashboard with AWS Lambda and Tableau Integration
98.6%KPI Arrow
Data Processing Time Reduction
24 HoursKPI Arrow
Reporting Time Improvement
30-40%KPI Arrow
Reduction in Infrastructure & Ops Costs
100+KPI Arrow
concurrent complex report generations
70%KPI Arrow
faster feature deployment
60%KPI Arrow
less time on manual tasks

Customer Challenges

The client’s legacy systems were unable to keep up with the growing complexity of their Inclusive Leadership Index (ILI) data. Inefficient data flows, slow reporting, and scalability issues made it difficult to deliver timely, actionable insights to their customers:

Integration & Insight Gaps:

Integration & Insight Gaps:

Organizations struggled with disjointed data flows, legacy reporting structures, and inefficient integration of survey data from multiple sources. Customers required a unified, real-time solution that combined detailed demographic analyses with a transparent ETL process, enabling quick decision-making and actionable insights.

Data Latency and Stale Insights

Data Latency and Stale Insights:

The client's previous data infrastructure suffered from significant latency, meaning that newly collected survey data and assessment results were not immediately available for analysis. This delay led to insights that were often outdated by the time they reached decision-makers, hindering agile responses to evolving client needs and market trends.

Manual Reporting Burden:

Manual Reporting Burden:

Prior to our intervention, the generation of complex reports on inclusive scores, ratings, and competencies was an arduous, manual process. This required significant human effort to extract, consolidate, and transform data from various disparate sources, leading to high operational costs and a substantial drain on consultant time. The labor-intensive nature of their reporting limited the volume and sophistication of analyses they could offer, directly affecting their service delivery capacity.

Scalability Limitations & Growth Hindrance:

Scalability Limitations & Growth Hindrance:

The existing infrastructure was inherently rigid and lacked the elasticity required to scale with the client's rapidly expanding global operations and increasing data volumes. This limitation manifested in frequent system slowdowns and an inability to onboard new clients or integrate additional data sources without extensive, costly, and time-consuming manual interventions. This bottleneck actively impeded their strategic growth initiatives and competitive positioning.

Solutions

To overcome these challenges, NeenOpal implemented a robust, cloud-based data infrastructure designed for scalability, automation, and real-time insights. The solution combined advanced AWS services with Tableau integration to create a seamless reporting and analytics ecosystem.

We meticulously planned and executed a zero-downtime migration of the client's outdated database to a modern, high-performance AWS RDS (Relational Database Service) instance. By employing a blue-green deployment strategy, we created an identical "green" environment on AWS while the "blue" legacy system continued to operate. This approach allowed us to test the new database and applications in a live environment without disrupting ongoing operations. Additionally, we implemented robust data validation and reconciliation processes to ensure 100% data integrity, minimizing the risk of corruption or service interruptions and ensuring smooth business continuity.

01

We developed robust automated data pipelines leveraging AWS Lambda to handle large volumes of complex survey data efficiently. These pipelines seamlessly ingested raw data from multiple external survey APIs and the ILI assessment platform. We established a scalable data lake architecture to store raw and processed datasets, offering a flexible and cost-effective foundation for current and future analytics needs. Using serverless ETL (Extract, Transform, Load) processes with Lambda, we cleaned, transformed, and enriched the disparate datasets into a unified, analysis-ready format, ensuring the data was accurate, consistent, and easily accessible across all systems.

02

We designed and implemented a reporting layer to generate multi-dimensional reports with deep analytical insights. These included breakdowns of inclusive scores by demographics, organizational units, and leadership levels, along with benchmarking against industry standards and historical data. Competency mapping reports highlighted strengths and improvement areas at both the individual and team levels. We integrated the processed data with Tableau to enable interactive dashboards and visualizations. Finally, we equipped the client’s team with self-service analytics tools and practical training, empowering them to perform ad-hoc analysis and build custom reports without depending on manual support.

03

Why choose NeenOpal?

NeenOpal specializes in integrating complex data ecosystems. By combining advanced Tableau analytics with robust AWS cloud infrastructure, NeenOpal delivers scalable, secure, and insight-driven solutions. With deep industry experience and an innovative approach, they help organizations turn raw data into actionable intelligence.

Services Used

Tableau
Tableau
AWS Lambda
AWS Lambda
Amazon S3
Amazon S3
CloudFront
CloudFront
Amazon RDS
Amazon RDS
API Gateway
API Gateway
SQS
SQS
SES
SES
Custom SQL
Custom SQL
PostgreSQL
PostgreSQL

Benefits

The implementation of the new AWS-based infrastructure and Tableau-powered analytics platform delivered significant improvements across data management, reporting, and scalability. These enhancements not only streamlined operations but also helped the client to deliver deeper insights and greater value to their customers.

Conclusion

The client solution seamlessly combines advanced data visualization with a robust AWS-based data pipeline. It enables dynamic, real-time reporting on inclusive leadership while ensuring high data quality and efficient operations. This integrated setup helps organizations use data effectively for continuous improvement and strategic planning.

Authors

Monish Mohanty

Senior Associate Consultant

LinkedIn

Madiha Khan

Content Writer

LinkedIn
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