Digital Transformation has been a buzzword for years, with AI and advanced analytics playing a key role in enabling Communications Service Providers (CSPs) to improve their customer experience, get new services and products to the market much faster, and reduce costs through automation. Skills Transformation, on the other hand, is another crucial aspect in this transformation yet is rarely discussed in-depth. One of the biggest challenges isn’t the implementation of technology but the skills gap - upskilling and training employees, especially given the shortage of AI specialists.
What specific skills are needed, in which areas of your company, and what steps can be taken now to address the gap? I’ll discuss this further in this article.
CSPs at present mode
The Network organization within a CSP is often the center of discussion. It’s where technology evolved from 1G to the current LTE/4G, and in the future to 5G. And this group tends to be one of the biggest, from a headcount and budget perspective. The illustration below shows daily collaborations internally within a particular network domain, cross-functionally/between domains, and externally with other organizations.
Figure 1: Network organization and its interactions internally and externally. Image credit: Guavus.
Some examples we’ve seen in terms of real practices and collaborations within CSP organizations include the following:
The RAN Capacity Plan, led by Planning, is jointly reviewed with the Optimization Team. This is important to not over-estimate expansion which could result in unnecessary CAPEX spending. Understanding temporary and/or seasonal traffic patterns from subscribers in particular areas is very important. Some of the capacity overload problems are still manageable by performing some physical changes on the sites, enabling RAN’s load balance features, and/or parameters tuning. Those actions are less expensive than buying more hardware/software and licenses for capacity expansion.
One root cause of the VoLTE muting call issue is non-optimal end-to-end timers setting across different network elements. A collaborative discussion between cross-domain experts (RAN, EPC, and IMS) to improve these timers is very important, taking into account several different scenarios with a steps approach and the least impact on subscribers.
The Customer Service Center often receives thousands of customer complaints daily. The customer service officer needs to quickly identify the problems (network or handset related) to take further actions with customers. The typical workflow starts with a generic query to find out if there was a service disruption within a location described by the customer during the period of time reported. If nothing is found here, the customer service officer then follows up on this issue by raising a ticket to the Network Operation Center (NOC) Team to perform further investigation which is typically around alarms and minor troubleshooting efforts. If it’s still not solved, the ticket is transferred to the next level, either the Tier-2/3 Advanced Technical Support Team or the Triage Team from Performance if it’s more on KPIs-related investigation.
These processes can easily take hours and days to sometimes weeks to resolve. For more complicated issues, another level of collaboration between the local and national team, and/or even cross-domain experts, is sometimes required. And this requires much longer time to resolve.
These practices can easily consume up to 90 percent of employees’ time, with huge numbers of people involved; long cycle times; costly hardware and software upgrades, licenses, third-party fees; etc. They’re not able to spend enough time to learn new technologies or innovate ways to improve cycle times since these workflows require intensive data readout analysis and trials (i.e., what-if scenarios).
Many of the CSPs we work with are introducing AI-based analytics and automation to make a drastic shift from this present mode of operation. However, they’re not just looking to us to “fish for them but to help teach them to fish.” Their teams are looking for AI-based analytics applications for customer care, network operations, marketing, and security they can put into place very quickly - but they also want to learn how to build their own custom AI-based applications to quickly address the unique needs of each of their business groups in the future.
What’s needed to make this shift? Below are some of the key steps they’re taking to make an AI-based skills and digital transformation in order to better operate and deliver an improved customer experience.
5 key steps to making an AI-powered skills and digital transformation
1. Data Lake Infrastructure with Self-Service Capabilities for Business Owners
Some major CSPs already have this type of data lake up and running, while others are still building it. This data lake has to be properly designed and provides self-service capability that enables business owners (as well as Network groups) to explore and mine the data for insights, any time they require, to make better business decisions. Simple SQL knowledge is optionally required, this can easily be obtained through their internal knowledge base or by searching the Internet. With this capability in place, there are no longer ad hoc and heavy-query requests from business owners to the IT/Data Team to build custom reports, which sometimes can take days.
2. End-2-end (E2E) Domains Knowledge as a Future-Looking Analytics Enabler
CSPs need to view and solve issues based on a cross-services or applications approach rather than a siloed or per domain approach. As an example, solving VoLTE quality problems, Mean Opinion Score (MOS), as seen on Fig. 2 below, is one of the most important metrics. It requires all domain experts to sit together and acknowledge MOS lies in the intersection of all domains. In mature CSPs, an E2E Team is often created that consists of senior-level experts with more than 15 years of cross-domain knowledge and experience. The E2E Team drives the overall Network organization into a better operating, cross-functional/domain collaboration compared to the siloed domain-based approach, hence the cycle time is greatly improved.
Figure 2: Network domains and the intersections. Image credit: Guavus.
3. Data Science Knowledge for Domain Experts
With the high demand for data science expertise and limited supply of data science experts in the market, acquiring the best resources is very challenging. Compensation for this job role is very high as well. It also introduces another level complexity within the CSP’s organization - that is, a new data scientist and AI organization. This does not mean that building a new Data Science Team is not important, but what really matters is justifying the right size of the organization and executing the right use cases based on real pain points found in the field. Thus, enabling Domain Experts to acquire new data science knowledge should be considered a strategic imperative. This can be done by having:
Domain experts develop the skills by participating in learning courses and/or obtaining a formal data science degree. Domain Experts can then practice what they’ve learned from the courses by building the models through various machine learning tools, writing code, performing what-if analysis, etc. However, this can require a big time investment before the CSP sees the value in a production environment.
Pick up an analytics solution that provides domain experts with an ecosystem that enables them to simply turn a new idea (or a new use case) into a production environment. The solution provides the domain experts with various prebuilt analytic algorithms and machine learning models to play with, import-your-own models capability, and simply drop-and-drag UI to build workflows without requiring them to write lines of code. This solution also requires architectural flexibility to interwork with any existing data lake infrastructure owned by CSP. This option is often a lot quicker compared to the previous one.
4. Application-centric Analytics powered by ML/AI as a Revolutionary Way to Plan and Optimize Network Resources
Once the E2E Team has acquired additional data science knowledge, the next step is to build application-centric analytics powered by AI.
As an example is VoLTE Customer Experience Management (CEM) analytics with automated Root Cause Analysis (RCA) and a Recommendation Engine to close the loop. With this solution, an issue can be identified faster with proposed recommended actions to be taken. This implementation requires real-time analytics capability and truly brings efficiency within the CSP’s overall operation workflows - where the issue can be resolved within a few minutes versus days or weeks in the current mode of operation. This type of analytics will evolve towards 5G and IIoT with more stringent requirements.
Another example is 5G Capacity Management. Network Slicing and Dynamic Spectrum Sharing will drastically change the way Domain Experts plan for the 5G resources. Domain Experts can now analyze different capacity scenarios with what-if analysis based on different capacity requirements (e.g., application requirements, layer management and carrier bandwidth, special events, mobility patterns and threshold settings, time of day/week/month, coverage shape, the site’s physical configuration, etc.). This new method of operation will significantly decrease the amount of time consumed by a Domain Expert on analyzing historical data which previously took weeks or months into a matter of hours or days (or even seconds, if real-time action is required (such as in special events capacity management).
5.Innovation at Heart
Last but not least, being an innovative and data-driven company will determine ongoing success for CSPs. The ability to translate market needs, automate repetitive tasks, continuously improve internal processes, and minimize cycle time will sustain their competitiveness and secure their growth in future. As an example, having Exploratory Data Analysis (EDA) process in place with the right use cases derived from real business problems can help them make the right investments. Most strong companies encourage their employees to innovate every day and reward their efforts. An innovative mindset should be owned by everyone, not just a few individuals in the company.
More than just technical skills
Digital Transformation is a must for every CSP whether they like it or not. They can’t provide the improved customer experience and new services to compete and gain new business with the many challenges ahead unless they can make this transformation. Vendors, on the other hand, can take their part by proactively researching CSPs’ main pain points and building solutions with AI capabilities that provide real value on Day 1 in production. With cost pressure on the shoulders of CSP executives, rather than doing everything themselves, creating partnerships with vendors who understand the complexity of the CSP business, organization, services and customer experience and know-how to not just apply AI and analytics technology but train CSP employees on how to use them is key to success. Addressing the skills as well as the digital gap enables CSP executives to truly be transformers of their business.
