Contact centre cloud migration data layering.
Layering in data lakes determines whether cloud migrations sink or swim.
By Tim North, Global Strategy Director at Connect.
According to Gartner predictions, more than 50% of enterprises will use industry cloud platforms by 2028, as greater agility and the ability to access new digital capabilities become a business necessity to maintain relevance and competitiveness.
This is particularly relevant in the contact centre industry, where rapidly evolving consumer preferences and shifting engagement patterns necessitate broader digital capabilities to streamline customer experiences (CX) and facilitate seamless omnichannel engagements.

However, a critical enabler of a contact centre operator’s ability to embrace the advanced digital capabilities that become available in the cloud is how they design their data utilisation, which is best done during the initial stages of the migration.
The pitfalls of data lakes in cloud migrations
When migrating to a Contact Centre as a Service (CCaaS) model, many companies make a common and often costly mistake – pouring all their data into a data lake.
Mixing structured and unstructured data, such as critical, historical, raw, and reference data, takes key data away from those who best understand it. This erodes an operator’s ability to apply data intelligence tools that support advanced search, intelligence, and optimisation capabilities.
Losing data quality by pooling it all into a lake is, at times, driven by weakness in reporting from CCaaS systems, an issue highlighted by Gartner.
The power of layered data for contact centre success
Addressing this issue requires a skilled and experienced systems integrator to layer data. This process entails strategically organising and migrating data in phases to minimise disruption, reduce risks, ensure data integrity, enhance flexibility and optimise performance.
- The first data layer should aim to close any reporting gaps in the CCaaS solution. This step allows operators to generate insights from raw data by producing the reports and dashboards that the management team requires or has become accustomed to using to continue making decisions on forecasting.
- The next data layer aims to bolster the reporting functionality in the CCaaS platform by integrating data analytics tools, particularly to transcribe unstructured call recordings and merge them into a single data set with the first layer.
- Data layer three seeks to integrate an operator’s system of interaction – its CCaaS – and its system of action, like a CSM or CRM solution. While many consider merging these data sources in the data lake, it seldom delivers the tight data integration needed to produce consistent reports and dashboards that reveal the customer journey.
- Layer four leverages GenAI, providing context and intent for better data visualisation.
- Lastly, layer five optimises all data sources, using human-in-the-loop intelligence to look deeper into data sets and find ways to improve or deal with intent through system or engagement optimisation, which is critical to improving customer journey pathways.
Layering data in this manner makes it more accessible, keeping data closer to the coalface where operators can use it to transform and optimise operations.
For instance, layered data improves discoverability and searchability, simplifying data analysis. These features provide management teams with the tools needed to manipulate multiple data sources for granular and insightful instant reporting.
Unlocking AI-powered insights with data layering
With access to the right data, in the right format and the right place, operators can also start to use AI to understand customer intent more easily, supporting agents through optimisation.
This was a stumbling block to enhanced CX in the past because people had to manually determine intent when reviewing contact centre interactions that were transcribed from voice to text. This process was time-consuming and the need for human input made it less accurate due to inherent human biases.
When operators started using AI tools to transcribe and analyse calls, the AI interpretation was even less accurate but more productive, leaving operators with a balance of volume vs. relative accuracy.
Now, system integrators are able to leverage generative AI (GenAI) to mine and understand intent at scale, getting the data and insights needed to understand customer journey mapping and optimise interactions across every channel, including voice.
Furthermore, the extracted insights ultimately provide the knowledge sources needed to fully embrace AI-based automation in the business, which supports solutions like chatbots and virtual agents, eventually leading to AI agents.
Uncovering intent in this way is how Connect helps our contact centre customers understand their customers’ needs.
Uncovering intent in this way is how Connect helps our contact centre customers understand their customers’ needs.
With insights generated from GenAI, our Conversational Services team feeds this data into AI engines and designs new ways to interact and optimise conversation flows across digital and voice channels that start to save you costs and improve customer service.
We also leverage our skills, expertise and tools to uncover additional ways to optimise operations by streamlining engagements and delivering exceptional CX that sets our customers apart from their competitors.
About‘Connect.
Connect combines global contact centre and customer experience (CX) expertise, deep domain knowledge, and unparalleled industry skills to make the complex, simple. Since 1990, we have leveraged our vendor-independent managed services approach to digitally transform how organisations communicate, both internally and externally. We specialise in combining the most relevant technologies and services from leading vendors and platform providers to create opti-channel engagement solutions, orchestrating frictionless experiences and simplifying complex communication challenges.
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