Breaking down silos in the multichannel contact center
One of the most confounding challenges for modern contact center leaders is reporting on any performance metric that requires information from more than one system or application, each of which is a self-contained silo of data. As brands follow their customers’ lead and embrace new channels, the contact center data landscape is only getting more complex. Savvy contact center leaders are seeking modern solutions to help eliminate data silos and create a comprehensive view of the customer journey.
Struggles of the past
Traditionally contact centers have been dealing with two main data challenges.
Real-time data is handled by a separate, dedicated real-time engine that feeds a set of separate reports. While this dedicated process is fast and reliable for its purpose, most real-time data simply “disappears” after briefly appearing in a single second in a report. Contact centers struggle to integrate this information with data from other systems to understand performance over time and across channels.
Historic data—data that is aggregated and summarized, with a variety of calculated metrics for trend reporting based on longer time intervals—is typically handled in batch processes that only run periodically. These “extract, transform and load” batch processes feed data into a secondary relational database optimized for reporting—usually a data mart or data warehouse. While this data is often richer and more complete than real-time data feeds, batch-loading is relatively inefficient for handling very large amounts of real-time data, and performance can suffer when the “refresh” intervals get shortened to minutes, or even seconds.
The birth of Big Data
Today there are technologies that are capable of handling large quantities of data. These technologies underpin many of the largest applications on the internet. Google, Facebook, Twitter and others helped to develop many of these technologies that reinforce what we now call Big Data, capable of handling hundreds of millions of data elements per second, and almost infinitely scalable. They first entered the contact center sphere when social media became a significant channel for customers to interact with brands.
While these same technologies can be used for handling real-time reporting, they aren’t necessarily getting all the value-added richness that’s possible with integrating more conventional contact center technologies. Social media monitoring and reporting applications often don’t connect with other contact center systems, leaving contact center leaders unable to report on social media interactions as part of the entire spectrum of contact center activity—including calls, emails, chats, and other interactions.
Contact center reporting technology of the future
What was needed was a new architecture that somehow married the best of both worlds—the scalability and instantaneousness of Big Data technologies, and the richer, business-rules driven context of the data warehouse.
And so was born the Lambda architecture, which was designed for robust processing of massive quantities of data across numerous contact center systems and channels. The architecture aims to strike a balance between data latency (the time between an event in real-life and its data showing up in a report) and data volume.
This Lambda architecture, which is the foundation of Calabrio Advanced Reporting powered by Symmetrics, is designed to handle the entire spectrum of contact center reporting needs, from collecting and processing large quantities of real-time data to highly specific, context rich reports that contact center managers need for performance management. Contact center leaders and analysts now have access to real-time data, granular contact detail data, and aggregated, summarized data on any time interval they want to measure—from any source or channel. Silos are eliminated and brand can better understand the entire customer journey.
Find your path to comprehensive reporting. Download our white paper, Better Contact Center Reporting: A Roadmap to Improving Analytics to Drive Key Business Objectives.