Transforming communications with analytics and 5G

5G has been a hot topic in the telecommunications (telco) sector lately, especially as working from home is becoming the new normal due to COVID-19. It holds the potential to help networks become more resilient, especially amidst such uncertain times, and support the Internet of Things for industries to truly transform in the long run.

In the region, 5G is already unlocking business interest for telcos and consumers. But to effectively support the surge of incoming data from 5G-enabled devices and benefit from 5G, more telcos will need to leverage analytics effectively.

Reinventing telcos with analytics

With a wealth of information, telcos are well-suited to harness data for actionable insights that can improve operations and seize new revenue opportunities.

For instance, 5G requires a much higher density of base stations than 4G to deliver high-speed connectivity. Telcos should, therefore, use analytics to identify the best location for network sites to support new coverage and optimize existing telecom tower operations, as well as the technologies that should be deployed at each site to maximize ROI.

Analytics can also enable telcos to enhance customer service. One way of doing so is by using descriptive and predictive analytics to forecast demand and map it to the supply of network services every few hundred meters. Another way is to automate network complaint reports and enable visual analysis of network incidents. This move has been proven beneficial as it helped reduce incident handling time for our operations.

Additionally, analytics can empower telcos to build new revenue streams with data. For example, telcos can develop a predictive model that gives credit scores to subscribers. By mapping those models to loan offerings from banking partners, it enables banking partners to sell their financial products to relevant customers while preserving customers’ privacy and confidentiality.

Making analytics work

To maximize the potential of analytics, telcos need to design an organizational structure and encourage a new way of work that will enable its employees to use analytics to significantly change the business. Here are five tips to consider:

1. Building a cross-functional team and centralizing data

Instead of creating teams by disciplines (i.e., data scientists, data engineers, business consultants), it’ll be more effective to have multi-disciplinary analytics teams structured by vertical (i.e. marketing analytics, network analytics, customer service analytics, etc). Since technical and business teams usually focus on different parts of the problem, co-locating data scientists with the business teams they support will ensure that the two groups of employees talk to one another and work collaboratively to achieve business goals using analytics.

Telcos also need to scale analytics across the company to become data-driven. To do so, they should centralize data to have an integrated view of the business when it comes to using analytics.

2. Putting analytics under functions which drive the business daily

Analytic initiatives are systemic refinements of an organization’s core operational decision-making processes. Analytics should, therefore, be coupled organizationally with daily operation teams such as marketing instead of strategy or IT. This will empower employees to make insights-driven decisions as part of their daily activities, which could lead to rapid iteration of new products.

3. Defining the business problems worth solving

Analytics is not about solutions looking for problems, but problems looking for solutions. Telcos should, therefore, have a clear view and understanding of their business challenges and opportunities before embarking on analytics initiatives.

4. Promoting a culture of experimentation

Analytics projects should not be managed in waterfall but following the agile methodology. They should be iterative as the finetuning that data science and analytics models require cannot be planned.

What telcos need to do is to identify the business problem to solve; find out whether they have the data required and, if not, whether that data can be acquired; create a prototype/model quickly; and enter into an experimentation cycle. The experimentation cycle will require multiple reiterations, with the model becoming better and more accurate in each cycle.

Telcos can also promote a culture of experimentation (or agile culture) across the organization by providing employees time during their workhours to experiment. In some of our companies, employees are given several hours to work on experiments that are related to analytics and they are passionate about but may not be necessarily related to their projects at work.

5. Measuring the ROI of their analytics initiatives

Since analytics need to drive continuous improvements for the business, telcos need to regularly measure the economic impact of the analytical models they have deployed to make the necessary changes or reprioritize their analytics efforts.

Setting the stage for telcos to digitally transform with analytics

Having a solid data foundation enabled by an enterprise data cloud is key to supporting this new organizational structure and new way of work to effectively harness analytics.

In Axiata, we have built such a foundation using Cloudera’s solutions, and it has helped us overcome common big data issues such as data inconsistency, data loss, data formatting issues, and data silos. It also allowed us to offer ready-to-use analytical products that not only provide predictive and prescriptive capabilities, but can also be easily used by multiple business units to improve the efficiency of businesses and provide stakeholders with sources of data-driven actionable intelligence to take the business forward.

In general, an effective enterprise data cloud should help telcos to support hybrid and multi-cloud to provide the flexibility to manage, analyze, and experiment with data in an environment (i.e., on-premises, public or private clouds) that best suits the telco’s needs.

It also needs to provide a standardized architecture for analytics across the entire company to support analytics across departments or even subsidiaries. This will help unify analytics functions to make it easier and faster for lines of business to ingest, transform, query, optimize, and make predictions from data to solve demanding business issues.

In addition, the enterprise data cloud should offer unified security and governance to diverse enterprise data regardless of where the data resides. It should also be open to prevent vendor lock-in and enable integration with the broad ecosystem, which will, in turn, lead to rapid innovation.   

Although 5G can support the next normal, telcos need to make analytics as part of the fabric of their daily operations to fully benefit from 5G. This calls for telcos to radically transform instead of focusing on run-of-the-mill changes. They can do so by designing the right organizational structure to support analytics, make experimentation as part of their company’s DNA, and having a solid data foundation to support those efforts.