Data mesh: Helping telco companies build data ecosystems
13 MINS READ
Why an ‘ecosystems ready’ data operating model is the next big thing for the telecom sector.
Getting a data strategy right sometimes requires different perspectives and lenses. For example, CSPs haven’t always been known for their highest-ranking consumer satisfaction scores when compared to other industries. Yet, they have done well in building resilience and adaptability, while providing mission-critical and even life-saving connectivity.
This gives industry leaders a unique opportunity to build greater trust with customers and to thrive in a connected digital future. Investments in data and security will be the foundation of this journey.
Introducing the data mesh
Emerging from the traditional domains of telecommunications business models, a data mesh approach embraces the natural organization of a business across its functions.
The data mesh approach represents a fundamental shift in how data can drive transformation. Over the last few years, CSPs have invested in three key domains to drive project-based outcomes: digital engagement and empowerment, network platform agility, and enterprise ecosystems. Most have started viewing data as a tactical requirement to boost a project’s outcome.
Building on this investment model, these CSPs are also partnering with cloud hyperscalers to innovate platform business models and grow new revenue streams. These partnerships are forged by organizing data in the cloud for accelerated value. This has influenced data strategy. As the shift to cloud is underway, new regulatory requirements such as the Telecommunications Security Act (UK) will secure networks and protect consumers against fraud.
CSPs need to build robust data management across public and hybrid clouds. Data governance capabilities need to be upgraded to handle the deployment of data, analytics, and machine learning across multiple clouds and hybrid (on premise) deployments. Cost-effective governance with a centralized model that supports federated approaches must be considered by CSPs. This will enable the adoption of AI/ML within the business domains of customers, networks, and the enterprise. The data mesh approach will help CSPs conceptualize, categorize, and simplify creating outcomes from data by closely aligning to how the business views itself. This would apply regardless of whether data science, analytics, or predictive modeling is used.
Enhancing net promoter score (NPS) and customer satisfaction with a data mesh changes how data is provisioned by intelligently supporting data consumers to address business outcomes.
For example, this can be through a typical access path using a database to query the data. Alternatively, this can also be through an API to pull the required data into a CRM application in real time to create a next best action recommendation. The data product should be able to handle the multitude of access demands, but importantly, use the same data sources regardless of the consumption pattern.
Many CSPs are conglomerates formed through mergers and acquisitions that may have taken years to close. This makes it challenging and expensive to build a singular customer view, connect experiences, and create outcomes such as enhanced NPS across brands. Often, data assets are deployed into business units and operate in silos and therefore few economies of scale exist through asset reuse. The impact is that many different solutions exist, and this impedes the value that the data assets could add. An example of this is witnessed in the limitation imposed around customer network experience data. It requires a real time combination of customer contact and profile data sources with network and session data.
Data technologies can help boost consumer engagement. But they can also hinder it if data platforms are not modernized to reflect the latest real time digital capabilities deployed into sales channels. Take, for instance, secure data access platforms, contact agent analytics and optimization, and customer insights analytics and predictive models. To get maximum mileage out of these data solutions, companies need to build the capabilities that support them. These include active data governance, master data management, and activated metadata.
Network platform agility
Networks are traditionally vendor-managed where black box systems manage data and optimize network equipment using proprietary insights.
This is changing. To unlock insights, typically, licenses must be shared with a customer contact center or the group corporate function. Recently, some efforts were made to create open standards in network telemetry. Network initiatives for full service CSPs (whether fixed wireline, wireless, or satellite) must ultimately integrate back into the converged network experience. Real-time insights across the network can be integrated into an intelligent orchestration engine that makes decisions using orchestrated automation and AI.
Decisions typically reconfigure software-defined network elements on the fly after modeling near-future scenarios quicker than a human operator. If necessary, operators can redeploy or reconfigure software-based network functions to meet SLAs and optimize customer experience.
Having the ability to self-drive the network using customer experience insights is the next stage of networking’s evolutionary journey, but many challenges exist. Networks can also be empowered to drive SLAs around energy optimization and sustainability, all while reducing mean time to resolve (MTTR) and cost of operations.
Enterprise transformation is mandatory
The time is now for the industry to re-platform to a digital enabler, by leveraging data-driven insights with AI to create new digital products and services.
CSPs must adopt a new operating model, which enables products and services innovation across business domains, and within an ecosystem of hyperconnected partners. CSPs are adopting aspects of operating models from technology giants. Building trust around personally identifiable information (PII) is a mandatory requirement, and sovereign clouds are being deployed behind the CSP firewalls to safeguard this critical data in context.
It starts with the modeling and consolidation of user journeys (consumers, business partners, employees) and communications channels using analytics and eventually predictive models using AI/ML. This new data insights capability can expand into an emerging area of technical communications. Where telco-owned platforms can combine data and insights around connectivity and machine intelligence to drive new industry use cases. For example, use cases for smart cities, smart factories, digital healthcare, and intelligent transportation.
CEOs of B2B and wholesale divisions can repurpose legacy connectivity services. This will drive new outcomes within partner firms such as enhanced sustainability for smart cities and support design and development of new digital services. All this while growing monthly recurring revenue (MRR) and achieving higher ROI from capex investments.
Data experience is an enabler that the industry is adopting from the user experience domain to derive value from data.
New architectural patterns in data are emerging in the adoption of microservices for data meshes. These patterns help improve security and data access across business domains. They also make day-to-day wrangling with data much more fluid now than in the last five years. Having a deep understanding of consumer and user preferences is a must to create highly contextualized and personalized services. However, this alone isn’t enough to fully accelerate business value from data. Having the right data security patterns can help drive a better data experience for different user groups across business domains.
Telcos looking to begin their data journey must start with assessing and identifying data operating models and understanding the critical role they play in making business and operational support systems transformation a success. Next generation communication networks are removing constraints in society, the economy, and in businesses through increased bandwidth, reduced latency, and high definition and interactive digital services.
The next thing to keep in mind is that having the right skills and a capable team can accelerate the journey. A purposeful business-led team can help identify the optimal technology, people, and process roadmap, in a technology-agnostic manner. Having the right data mesh can ensure that the maximum value from data can be realized. This delivers on the promise of 5G and fast fiber services by supporting the near-to-real time business that consumers and businesses demand today. If we can’t move data and solve problems in real time, the large investments going into networks toward transformation will be held back.
If you can arrive at truth faster than your competition, this insight will feed your algorithms to give your business the real time edge needed today. This is what we call managing business at the speed of experience.