The Growing Need for Advanced Analytics to Fuel 5G and Edge Solutions

Organizations have been focused on enhancing customer experiences to enable quicker responses to services and to provide localized behavior for many years now. However, with the Internet of Things (IoT), Smart Cities, Gaming technologies and Self-Driving Cars going more mainstream, there is an even greater need for organizations to react faster to customer behavior and bring solutions closer to the customers. Two areas where analytics are impacting these efforts are in 5G and edge solutions, which are key enablers for these modern consumer demands.

5G and Edge - What is Hype All About?

A concept that has been gaining incredible attention ever since its inception is 5G. There has been a lot of buzz within telecom circles ever since its launch, primarily due to its low latency, high efficiency and faster data speeds. While 3G an 4G focused on higher data speeds and customer interactions, 5G has focused on lower latencies, improving accessibility, and ensuring more coverage to resources. This naturally aligns 5G with Edge deployments.

Edge networks provide faster links in a localized area, providing flexibility for organizations to run applications on distributed infrastructure. Edge deployments provide an opportunity for businesses to get closer to their consumers, offering faster response to requests, service flexibility, and an even better localized experience. And those experiences will be driven by data.

According to Gartner estimates, by 2025, 75% of the Enterprise data will be handled in Edge sites, and one of the biggest Edge applications is expected to be 5G.

Edge Solutions Come with Real Challenges

Edge data centers are expected to be as critical as cloud and traditional data centers soon since they provide decentralized, localized, and flexible footprints as appropriately needed. However, with all the promises that 5G and Edge provide, the adoption has been slower than expected. Here are a couple of key considerations that organizations are factoring in that add to these delays:

• Many organizations believe that the technology associated in building Edge-based applications is complicated and requires refactoring due to distributed computing. This is compounded by the need for technically skilled people to do the work, driving the overall costs associated with these rollouts much higher than traditional projects.

• For an effective and successful roll out and operation of Edge deployments, it is critical to select the right geographical locations. With a shifting customer base and new solutions, picking the right location for today and to scale in the future is dauting given the importance of location to delivering personalized experiences.

There are also setup costs, and security concerns associate with any data center project.

How Can Data Help?

For organizations to be successful on Edge strategies, they must have a strong data strategy in place. Embarking on an Edge journey means reckoning with the fact that there will be disjointed deployments resulting in data silos that need to be resolved. It’s important to have a data strategy that can remove redundancies, eliminate blind spots, and most importantly have a unifying data integration strategy to bring together disparate data that ensures a complete picture of the customer experience. Here are some of the key considerations to building a data strategy that matches Edge needs:

1. Optimization and Governance: Deploying solutions in the Edge is a multi-departmental activity in most organizations, and a data governance framework will be crucial to align people, deployments and policies and eliminate duplicates, manual activity, and misrepresentation. A solid understanding of various sources of data, its structure and frequency will be critical to deriving the strategy.
   
   Teams will also want to make sure the data strategy that’s deployed supports and maintains both overall business strategies and the Edge solution needs.

2. Data Modeling: Data Modeling is the most critical step in defining data strategy specifically for Edge deployments. This is the process of creating a visual representation of various sources of data and their dependencies with each other. This process also involves defining integration from various data sources, and processing disparate data to ensure integration happens in a seamless manner.
   
   Additionally, if real time analytics is necessary, data modeling needs to be appropriately updated to capture a constant flow from the data pipeline. In an Edge deployment model where large amounts of data are generated from various geographic locations and from various network elements, care must be taken to consider each source, the structure of data from each source, and then appropriately eliminate duplicates to avoid double counting. Additionally, every source should be evaluated to avoid blind spots.

3. Business View of Data: With visualization techniques provided by business analytics tools, data can be represented in an easy-to-understand way and can easily be mapped to overall KPIs. And with “What If” analysis, predictive analysis can enable business owners with a view that enables appropriate decisions based on current data.
   
   Edge deployments are expected to be the backbone for all next generational technologies. However, there are many complexities associated with roll outs and if best practices are not followed, organizations could suffer from overwhelming costs and poor customer satisfaction. With business and data analytics methodologies, combined with data integration technologies, organizations can align edge solutions with core business functions to deliver consistent and incredible customer experiences wherever they are in the world.

To learn more about Qlik’s work in supporting telecom leaders like BT and Charter, visit Media Analytics | Telecom Analytics | Qlik.

5G and Edge deployments have incredible upside potential for telecom customers, but without a strong data strategy, they won't deliver.