Organisations across industries are under constant pressure to cut costs and operate efficiently at scale. To navigate this landscape effectively, enterprises are increasingly recognising the inherent value within their data ecosystems. Leveraging this data potential presents an opportunity for real-time situation detection, empowering swift and precise analyses, facilitating informed decision-making, and supporting automated resolution processes.
However, despite the widespread collection of massive amounts of data, many organisations face a common hurdle – they lack the necessary tools and expertise to transform raw data into actionable insights that drive business success.
Consequently, companies are unable to harness the full potential of data to reduce expenses, raise service standards, and increase the overall perceived customer experience, ultimately missing out on interesting business prospects that could increase their revenues. The gap between the accumulation of data and its effective use represents a fundamental challenge, which requires innovative solutions to bridge the divide between the abundance of information and actionable intelligence.
Celfocus’s Cognitive Operations Offer empowers organisations to maximise the potential of their data, transforming it into a strategic asset.
Our comprehensive end-to-end solutions and services are helping our customers solve several problems, such as:
To positively transform day-to-day operations through advanced analytics, Celfocus believes that heterogeneous teams with a mix of Data Engineers, Data Scientists, and Domain Subject Matter Experts are imperative. This provides the team with a thorough understanding of the technology and data and/or business processes, which are key to following our transformation approach:
Evaluating existing data landscape, tooling, and data platforms while considering the organisational strategy to identify areas for improvement and develop a feasible roadmap.
Designing/implementing or enhancing the existing platforms and capabilities that support seamless data collection, storage, and processing, ensuring data is readily available for actionable insights.
Implementing data lineage processes and anomaly detection mechanisms to ensure the accuracy, consistency, and reliability of data, providing a solid foundation capable of producing insights.
Data scientists and SMEs collaborate closely with business stakeholders to design and implement use cases that address operational and engineering challenges, with tangible business outcomes.
Leveraging our experience in developing and applying Machine Learning Models to optimise processes, troubleshoot issues, predict equipment failures, streamline field services, and more, driving efficiency and cost savings across your organisation.
Additionally, depending on the nature of the challenge, Celfocus may also require roles that focus on the transformation aspects of the organisation to help with the smooth transition to the future mode of operations.
By leveraging Celfocus’s data approach, we can achieve several tangible benefits for our clients’ organisations:
Process data from Telco ecosystems to perform network root-cause-analysis using data sources such as faults, inventory, incidents, and changes, and more, to automatically flag incidents and problems to operational dashboards and automation frameworks for follow up.
Process and report substantial amounts of service usage records, keeping KQI and KPIs under control while detecting service usage patterns, flagging potential customer service issues to operational dashboards and automation frameworks for follow up.
Vertically monitor core services from NFV applications to the Telco Cloud in use by applying Machine Learning Models that proactively detect anomalies, prevent degradation, reduce downtime, and improve performance, providing insights to operations that can also be automated for further efficiency.
Using Large Language Models to elevate customer experience and automate customer interactions, providing more natural and accurate answers and resolving issues efficiently (sentiment analysis, intent recognition, call summarisation, real-time troubleshooting, and suggestion and deep detractors’ prevention).
Provide engineers with organisation-contextual answers to resolve and/or automate problems by answering natural language prompts with actions (automation scripts, service SLAs, etc.), aligned with the organisation’s blueprint, while ensuring that all data remains private.
