Case Studies

Case Study: Transforming Contact Center Data with Azure Synapse Implementation

Client Profile:

• Industry: Telecommunications  
• Client: Fortune 50 Telecom  
• Provider Solution: Modern Data

Background:

Contact Centers represent a significant driver of operating costs for businesses. While some costs are inherent to the nature of these operations, others result from suboptimal call handling, or lack of data-driven decisions, and can be mitigated with a well-designed analytics architecture. Timely and accurate analytics play a pivotal role in addressing these challenges. For instance, one avoidable cost comes from transferring calls between agents. Transfers become necessary when a caller is not directed to the most suitable agent for their specific inquiry. However, by leveraging advanced analytics and implementing refined routing logic, businesses can substantially reduce transfer costs.  

A proactive analytics approach ensures that callers are accurately routed on the first attempt, minimizing unnecessary transfers, and providing a more cost-effective operation. Precise analytics offer valuable insights into contact center operations, empowering businesses to make informed decisions that enhance efficiency and optimize overall performance.

The Problem:

Our client, a forward-thinking enterprise heavily reliant on processing extensive real-time contact center data, is grappling with critical challenges rooted in their legacy technology stack. The legacy infrastructure is ill-equipped to handle the sheer volume of data generated, resulting in significant impediments to their Business Intelligence (BI) processes.

1. Delayed Reports

The existing technology stack hampers the client’s ability to generate timely and actionable BI reports. The volume of contact center data overwhelms the legacy system, causing delays in extracting meaningful insights. This delay not only impacts decision-making but also diminishes the agility required to respond promptly to evolving customer needs.

2. Complex Workarounds

The limitations of the legacy technology stack require complex workarounds to support BI reporting. These workarounds contribute to inefficiencies, complicating the entire data processing and reporting workflow. The complexity not only hinders operational efficiency but also elevates the risk of errors in the reporting process.

3. Data Sharing Challenges

The outdated infrastructure presents challenges in sharing crucial data across teams seamlessly. Siloed data storage and retrieval mechanisms impede collaboration, hindering the organization’s ability to harness collective intelligence. This lack of cohesion negatively impacts the overall performance and strategic alignment of different teams within the organization.

4. Inefficient Handling of Data Model Changes

The inability of the current technology stack to streamline the delivery and handling of new requests and changes to the data model poses a significant operational hurdle. This translates into extended timelines to implement necessary adjustments, limiting the organization’s ability to adapt swiftly to evolving business requirements.

5. Limited Experimentation with Data

The legacy system restricts the client’s ability to experiment with their data. Innovation and exploration of new data-driven strategies stifles the organization’s ability to uncover novel insights and optimize their operational processes effectively. 

 The client’s existing technology infrastructure falls short in accommodating the demands of processing vast real-time contact center data. This manifests in delayed reports, intricate workarounds, data-sharing challenges, inefficient handling of model changes, and a constrained ability to experiment with data. Recognizing the urgency of modernizing their technology stack, the client needed a robust solution that ensures scalability, agility, and the ability to extract actionable insights promptly.

The Solution:

In response to the technology challenges, Kenway took a proactive approach by implementing a Modern Data solution that harnessed the capabilities of the Azure Cloud. Recognizing the need for a centralized and adaptable processing platform, Kenway strategically advocated for the integration of Azure Synapse Analytics. This decision allowed the seamless orchestration of data ingestion, preparation, and serving, providing a comprehensive solution to the existing challenges.

In the subsequent sections, we delve into each of these components, showcasing their roles in transforming the client’s contact center analytics landscape.

1. Azure Synapse Modern Data Platform

Kenway Consulting recommended the use of Azure Synapse Analytics as the central data platform. This choice was driven by the platform’s integrated capabilities, which combine big data and data warehousing, allowing for seamless ingestion, preparation, and serving of data. The platform’s architecture supports both on-demand and provisioned resources, offering flexibility in managing diverse workloads efficiently.

2. Custom Python Kafka Consumers

The Kenway team implemented a robust Python Kafka Consumer to capture and ingest real-time IVR data to an Azure Data Lake Storage Container. A custom Python consumer was meticulously developed to ensure compatibility between Kafka and the Azure Data Lake including record batching and compression. This consumer not only efficiently processed and transformed incoming data but also handled potential data schema changes, ensuring adaptability to evolving Contact Center reporting requirements.

3. Spark Synapse Notebooks for Transformation

Leveraging the power of Apache Spark within Azure Synapse Notebooks, Kenway executed sophisticated ETL (Extract, Transform, Load) processes. These Spark-based notebooks facilitated data cleansing and transformation operations at scale leveraging data contracts when possible to ensure data consistency and technology scalability. The notebooks also allowed for iterative development, making it easy to adjust transformations as needed while maintaining high performance during the entire data processing lifecycle.

4. Serverless SQL for Data Modeling

With the transformed data residing in Azure Blob Storage, Kenway implemented serverless SQL queries within Azure Synapse. This serverless approach allowed for on-the-fly querying and modeling without the need for dedicated infrastructure and the movement of data. The serverless SQL capabilities seamlessly integrated with the existing Azure Synapse environment, providing a cost-effective solution for modeling, and preparing data for analysis.

5. Power BI for Data Visualization

To empower the client with compelling data visualizations, Kenway utilized Power BI. The team developed interactive dashboards and reports that offered a comprehensive view of contact center performance metrics. Power BI’s integration with Azure Synapse allowed for real-time data exploration, enabling stakeholders to make informed decisions based on dynamic and up-to-date insights.

The Result:

The new modern data solution utilizing Azure Cloud and Azure Synapse Data Platform provided a centralized and flexible processing platform. These changes enabled the client to strategically retire costly pieces of legacy infrastructure, enabling significant cost savings, resulting in a 90% cost reduction in overall infrastructure spend, while also providing critical new benefits to the organization including:

Improved Decision-Making:

Power BI visualizations, enriched by data contained within the Azure Synapse platform, empowered stakeholders to gain actionable insights. Additionally, the Modern Data Architecture enabled significantly faster and more frequent Power BI Data Source refreshes, enabling more frequent analysis of key KPIs. Decision-makers could easily interpret data trends, monitor key performance indicators, and make informed decisions to enhance contact center operations and customer satisfaction.

Scalability and Agility:

The modular architecture of Azure Synapse and the Azure Cloud provided the scalability and agility required to handle varying workloads, ensuring the system could adapt seamlessly to spikes in data volume or processing demands.

Cost Efficiency:

The serverless SQL approach significantly contributed to cost efficiency by eliminating the need for continuously provisioned infrastructure. The pay-as-you-go model ensured cost optimization while meeting the client’s performance requirements.  

 Ready to optimize your Contact Center or Data Architecture? Connect with our contact center consulting experts to discuss a streamlined IVR Analytics Solution for your business.

FAQs:

What is contact center data?

Contact center data refers to all the information generated from a customer’s interaction with a company. This can include voice calls, chats, emails, tickets, account data, surveys, and agent performance data.

How do you analyze data in a call center?

To analyze data in a call center, an organization must ingest, store, transform, and publish the data in a way for users to derive value from it. End user reports can live in a CCaaS platform tool or using a Business Intelligence tool like Power BI or Tableau.