Telco Scaling Strategies: Modernizing Business Support Systems for Flexible Revenue Growth

Laura Delarosa
December 8, 2022
#Telco

Consumers and businesses alike are driving huge demand for innovative telecommunications technology that tests the limits of monolithic, traditional business support system (BSS) architecture.

The competition is fierce within the industry, tipping telcos to differentiate their businesses with fresh digital services like low latency mobile apps, ultra-fast streaming services, virtual reality, and IoT solutions. The worldwide adoption of 5G is driving the change, bringing with it the need to simplify architectures to accommodate the complexity of modern 5G networks, which require multiple assurances, orchestration, provisioning, and charging functions to aggregate data effectively and manage services.

Alongside the development of headline-grabbing technologies, telecommunications enterprises are also busy building increasingly customer-centric experiences. Fast, reliable communication is essential for all people, from the average consumer who expects flawless performance, to the enterprises that need to run mission-critical business processes over telecommunications networks. These interactions matter immensely for customer loyalty.

Partnering together, Tech Mahindra and MongoDB have ushered telcos through their ongoing BSS modernization journeys by enabling business growth and operational efficiencies with solutions ranging from core network functions through to product catalog and customer management systems. Today, we see that the biggest hurdles standing in the way of telco innovation are legacy data architectures that eat up developers’ time with time-consuming maintenance work.

Building a consolidated view: Drilling into customer data

Billing modernization is a big market. The global telecommunications billing and revenue management market size reached $13 billion in 2019, and is forecast to expand at a compound annual growth rate of 11.6% to more than $31 billion between 2020 and 2027, according to research prepared by Forrester Research, Inc.

Core, consolidated customer data act as an enabler for many other related solutions that share the need for a solid, reliable record of customer core data. The common factor for payment processing, customer loyalty programs, service provisioning, service usage, and finally billing generation, is customer core data.

**Figure 1:** Customer centricity in billing

Like many large corporations, CSPs are often made up of siloed application stacks broken out by product area, such as VoIP, mobile, cable, and so on. Since customers use products and services that exist within multiple silos, changes to customer data need to be propagated to multiple systems.

The lack of a single, consolidated view is often a result of historical mergers and acquisitions. Without the ability to analyze data within a single view, opportunities to capitalize on data analytics to uncover cross-selling and up-selling opportunities are lost. Furthermore, enterprises managing multiple parallel billing implementations and the systems’ associated data synchronization infrastructure can incur hefty costs and architectural complexity.

The best way to address these challenges is to modernize core customer data systems to have a single view of customers and their billing-related data. At the heart of these modernization projects is the move to a new platform.

Product catalog simplification and the hybrid cloud approach

Akin to the global-scale retailers with some of the most complex product offerings around, telcos have a complicated array of product offerings that require CPQ processes to be able to combine offerings. Whether a telco is combining handsets, tariffs, warranties, add-ons or promotions, managing data in a streamlined and scalable infrastructure is a crucial modernization strategy. What’s more, telcos must deliver personalized and real-time shopping experiences to customers across web, mobile, phone, and in-store platforms to stay competitive.

The modernization and simplification of a telco’s product catalog architecture can quickly turn into a complicated mess when certain existing legacy systems must stay in place. Making one swift rip-and-replace move can be a big risk with costly implications for long-term transformation projects.

Today, more and more successful modernization strategies are achieving omnichannel implementation and multi-play services goals by migrating incrementally. This enables telcos to set the stage for a successful migration, at the right pace for the company. A key part of a modernization strategy is often a move from a self-managed, on-premise architecture to a cloud-based one. Initially, a hybrid cloud strategy is often more cost-efficient, and acts as an important stepping stone in any enterprise’s digital transformation journey.

The limits of existing legacy systems inhibit telcos’ ability to scale and grow. As the telecommunications industry reimagines how to apply new technology in the digital-first world, heavier reliance on the public cloud is delivering operational and competitive advantages. But for an industry with complex legacy infrastructure and loads of personal data, moving every workload to the cloud isn’t feasible yet.

Through implementation projects with telcos around the world, Tech Mahindra has proven that a central commercial catalog, integrated with the right legacy technical catalogs and BSS stacks, improves time-to-market for launching multiple bundles and offers that are still processed and billed to a single customer.

The benefits of this hybrid approach are immediately apparent:

Faster time-to-market
Increase in sales
Improved customer satisfaction
Reduced handling time
Less fallouts, errors, and training time

Through Tech Mahindra’s BlueMarble Commerce solution, underpinned by MongoDB, telecommunications enterprises are quickly overhauling their omnichannel strategy without overhauling their business or existing systems. A true end-to-end omnichannel multi-play solution for telcos, BlueMarble automates channel sales, order management, CPQ (product configuration, pricing, and quoting) and fulfillment, including reverse logistics.

Combatting the complexity of BSS modernization

By putting the needs of their customers first, telco, cable, and media service providers are building a bridge toward seamless and consistent experiences for both digital and physical user experiences. BlueMarble Commerce was built with this principle in mind.   With its ability to connect with legacy systems using custom-built adaptors and APIs, BlueMarble combines multiple channels in a uniform, seamless manner, helping to simplify telco modernization. MongoDB delivers a multi-cloud database service built for resilience, scale, and the highest levels of data privacy and security. This is critical when building a platform that enables a cohesive, integrated suite of offerings capable of managing modern data requirements for building applications in a microservices framework without sacrificing speed, security, developer experience, and the ability to scale. These were the primary reasons to choose MongoDB for the BlueMarble platform.

With these features of MongoDB Atlas, BlueMarble acts as a federated, overlay solution that masks the complexity of legacy, and enables the creation of new digital functionalities by integrating new digital architectures with existing legacy, eliminating the need to build new bespoke applications.

In conclusion, as telcos compete to provide the exciting new technologies driving change in the industry, they must not lose sight of the customer, or providing customer value. Tech partners like MongoDB and Tech Mahindra are leading the charge in supplying cloud-native, microservices-based architectures in business support systems.

This essay appears in the new TM Forum report: Evolving BSS for future services. Access the full report here.

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Simplifying IoT Connectivity with myDevices and MongoDB

In the highly competitive era of Industry 4.0, companies that are able to adopt emerging Internet of Things (IoT) technologies and shift from traditional offerings to digitally differentiated ones are moving to the forefront of their respective industries. McKinsey & Company estimates that by 2030, IoT could enable $5.5 trillion to $12.6 trillion in value globally, including the value captured by consumers and customers of IoT products and services. From smart thermostats to smart factories, IoT already connects billions of devices worldwide. Figure 1 shows potential areas where IoT solutions make a difference. Figure 1:   IoT applications by industry (non-exhaustive). All of these IoT applications and solutions require technologies that can offer low-power operation, low-cost, and low complexity in setting up and maintaining end devices. End devices that are able to communicate wirelessly over large distances with low-power consumption are key. The data generated by IoT devices is time series and high frequency, placing a unique strain on the underlying data infrastructure. Because of the polymorphic nature of IoT sensor data, the database must support flexible data schemas, making it easy for developers to work with the data. It must also ensure that the IoT applications are resilient to future changes. MongoDB embraces the variety and volume of IoT data without compromising on performance. Through its document model, MongoDB eliminates data movement and blends time series with the rest of the enterprise data in a single developer data platform. In this article, we’ll describe how myDevices leverages the MongoDB developer data platform for IoT. Overview of myDevices myDevices is a U.S.-based IoT solutions company that empowers system integrators, MSPs, ISVs, VARS, and enterprise customers to quickly deploy IoT solutions to their customers. The company has more than 1000 plug and play sensors and multiple Long Range Wide Area Network (LoRaWAN) gateway options to create IoT solutions for a variety of use cases. Over time, myDevices has created the world’s most extensive IoT device catalog from more than 150 hardware manufacturers around the globe. LoRaWAN offers unique IoT benefits, such as long range and coverage, which may reach up to 15 kilometers in line of sight (LOS). It offers ultra-low power consumption for end devices, low-cost infrastructure, and high capacity, which makes it possible to link thousands of devices to one single gateway. myDevices understands that connecting devices from disparate manufacturers can be very challenging; thus, they have created a no-code solution that includes plug-and-play templates to connect sensors to the gateway just by scanning a QR code. After the sensor is connected to the gateway, users can perform remote monitoring and device management from a single-view interface. They can also get alerts through text and email and set up charts for visualization of sensor data. The alert rules can be configured as time based or threshold based in the myDevices platform. The myDevices IoT platform is secure from the edge to the application layer through the cloud. The security is composed of LoRaWAN network security at the edge, TLS to the cloud, and SAML at the application layer. Figure 2 shows the architecture of the myDevices platform and how it connects to the sensors. Figure 2:   MyDevices architecture. myDevices also has multiple ready-to-go solutions for a variety of IoT use cases and applications. From machine health predictive maintenance to soil moisture detection, there are sensors that just work with the IoT in a box application. It takes only minutes to set up connectivity between the sensor and myDevices cloud, and myDevices enhances productivity because you don’t have to worry about writing code to extract data from the sensors and establishing secure connectivity with the gateway. As LoRaWAN enables hundreds, if not thousands, of sensors sending data to a single gateway, it requires a database that can easily and automatically scale. When it comes to publishing data out of myDevices cloud to MongoDB Atlas, myDevices provides a webhook integration functionality that can be set up in minutes to establish connectivity between the two systems. Database requirements for IoT and MongoDB Atlas MongoDB and MongoDB Atlas are ideal partners for any IoT deployment, offering: Deployment flexibility (on-premises, in-field, cloud) Multi-cloud flexibility (AWS, Azure, GCP) Schema flexibility (frequent changes and additions) The ability to blend different data (time series, operational) Real-time analytics readiness Automated data tiering As a result, IoT data platforms and service providers, such as Bosch and Software AG, as well as some of the world’s most intensive IoT users, including Toyota, Mercedes-Benz, and Vodafone, choose MongoDB for their IoT platforms and services. MongoDB’s developer data platform supports the entire IoT data life cycle, from ingestion, storage, querying, real-time analytics, and visualization to online archiving (Figure 3). MongoDB Atlas brings the core components of real-time analytics into one developer data platform. Figure 3:   MongoDB Developer Data Platform for IoT. Let's talk about a few features that directly support IoT applications: Native time series platform: MongoDB supports native time series collections with hands-free schema optimization supporting high-efficiency storage and low-latency queries. This is an extremely important feature for IoT applications. Change streams: MongoDB change streams allow applications to access real-time data changes in the database without any complexity or risk. IoT applications can use change streams to subscribe to all data changes on a single collection, a database or an entire deployment and immediately react to them. This approach enables quick response time and fast decision making. Aggregation framework: By using the built-in aggregation framework in MongoDB, users are able to do real-time analytics without having to move the data to another platform. By using the aggregation framework, the work is done inside MongoDB, and the final results can be sent to the application, typically resulting in a smaller amount of data being moved around. For IoT applications, this can be a powerful tool to only transmit the filtered data to the Cloud or central storage resulting in improved security and reduced cost. Data Lake: As data is ingested, Atlas Data Lake automatically optimizes and partitions the data in a format and structure best for analytical queries. This capability significantly reduces the complexity of transforming data for the data scientist tasked with building machine learning models for analytical use cases and applications Data Federation: Atlas Data Federation provides the ability to federate queries across data stored in various supported storage formats, including Atlas Clusters, Data Lake Datasets, AWS S3 buckets, and HTTP stores. This feature reduces complexity of bringing data together for analytical model testing purposes. Data API: Companies can use Atlas Data API to integrate Atlas into any apps and services that support HTTPS requests. Leveraging this feature, the data from the myDevices cloud can be sent to Atlas and then used for storage and for analytical purposes using the aggregation framework or via the Atlas ecosystem connectors with third-party analytical software. Ecosystem integration: MongoDB Spark Connector opens up access to all Spark libraries for use with MongoDB datasets: Datasets for analysis with SQL (benefiting from automatic schema inference), streaming, machine learning, and graph APIs. Charts: MongoDB Charts is the best way to visualize IoT data stored in MongoDB. Charts is built specifically for the document model, no ETL, no time loss to data manipulation or duplication required to visualize rich JSON data. Using Charts, powerful engaging data experiences can be created for the use case stakeholders in no time. Integrating Atlas and myDevices using Webhooks and Data API myDevices offers a variety of no-code integrations for its clients to quickly get started by sending data to the platform of their choice. For MongoDB Atlas clients, this is great news because, by using myDevices Webhook integrator and payload transformation feature, MongoDB Atlas clients can receive and store LoRa sensor data into the specified collection. Let’s run through the methodology to perform this integration: Step 1: Log into your Atlas Cluster and set up Data API and API key. The MongoDB Atlas Data API lets you read and write data in Atlas with standard HTTPS requests. To use the Data API, all you need is an HTTPS client and a valid API key. It is important to understand that the Data API is not a direct connection to the MongoDB database. Instead, it routes requests through a fully managed middleware layer, called Atlas App Services, that sits between your cluster and client apps. This layer handles user authentication and enforces data access rules to ensure that the data is secure. The Data API supports two types of endpoints: Data API endpoints are automatically generated endpoints that each represent a MongoDB operation. You can use the endpoints to create, read, update, delete, and aggregate documents in a MongoDB data source. Custom endpoints are app-specific API routes handled by functions that you write. You can use custom endpoints to run your app's backend logic or as webhooks that integrate with external services. In this example, we are using a data API endpoint. You can follow these easy steps to enable Data API and create a Data API Key. Step 2: Log in your myDevices Console and set up integrations After you log in, click on new webhook creation through the INTEGRATIONS option on the right-hand panel (Figure 4). For the purpose of this article, we are assuming that you have already created an organization in myDevices and added sensors and gateways to it. If you have not, please refer to myDevices API docs to get started. Figure 4:   Set up integrations in myDevices. Step 3: Click on Webhook integration to open up the new Webhook creation panel. In this step, choose Webhook as the desired integration option, as shown in Figure 5. Figure 5:   Choose Webhook as the integration option. Step 4: Add key information. In this step, you’ll want to include key information, such as Url, which is your Data API endpoint, Webhook Header, which will include the api-key at the very minimum, and the payload transform script, where you can specify the cluster, database, and collection where this sensor data needs to be stored (Figure 6). Figure 6:   Paste the endpoint generated by Data API in Atlas. An example payload transformation script looks like the following. This is according to Data API requirements where you have to specify the cluster, database and collection name in the raw body data. function Transform(event, metadata) { return { dataSource: "my_cluster", database: "my_database", collection: "current_sensor", document: event, }; } Step 5: Save your webhook. Once you save your webhook, you can observe sensor data flowing into your MongoDB Atlas collection from the actual device using MongoDB Compass or Atlas Charts (Figure 7). For more details on how to create Charts, please visit the Atlas Charts documentation . Figure 7: Visualize sensor data using Atlas Charts. Conclusion We have shown how easy it is to connect myDevices IoT platform with MongoDB using the Data API . The overall architecture is shown in Figure 8. Figure 8: End-to-end architecture of myDevices and MongoDB Atlas integration. Simplifying IoT connectivity is of paramount importance for any organization looking to embark on a digital transformation journey. Fortunately, both myDevices and MongoDB Atlas provide platforms that simplify management of the full life cycle of an IoT device from provisioning to connectivity to data storage and archival. To learn more about how MongoDB enables IoT for our customers, please visit our IoT use cases page .

December 6, 2022

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Transforming Industries with MongoDB and AI: Healthcare

This is the sixth in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. In healthcare, transforming data into actionable insights is vital for enhancing clinical outcomes and advancing patient care. From medical professionals improving care delivery to administrators optimizing workflows and researchers advancing knowledge, data is the lifeblood of the healthcare ecosystem. Today, AI emerges as a pivotal technology, with the potential to enhance decision-making, improve patient experiences, and streamline operations — and to do so more efficiently than traditional systems. Patient experience and engagement While they may not expect it based on past experiences, patients crave a seamless experience with healthcare providers. Ideally, patient data from healthcare services, including telehealth platforms, patient portals, wearable devices, and EHR, can be shared – securely – across interoperable channels. Unfortunately, disparate data sources, burdensome and time-consuming administrative work for providers, and overly complex and bloated solution stacks at the health system level all stand in the way of that friction-free experience. AI can synthesize vast amounts of data and provide actionable insights, leading to personalized and proactive patient care, automated administrative processes, and real-time health insights. AI technologies, such as machine learning algorithms, natural language processing, and chatbots, are being used to enhance and quantify interactions. Additionally, AI-powered systems can automatically schedule appointments, send notifications, and optimize clinic schedules, all reducing wait times for patients. AI-enabled chatbots and virtual health assistants provide 24/7 support, offering instant responses, medication reminders, and personalized health education. AI can even identify trends and predict health events, allowing for early intervention and reduction in adverse outcomes. MongoDB’s flexible data model can unify disparate data sources, providing a single view of the patient that integrates EHRs, wearable data, and patient-generated health data for personalized care and better patient outcomes. For wearables and medical devices, MongoDB is the ideal underlying data platform to house time series data, significantly cutting down on storage costs while enhancing performance. With Atlas for the Edge, synchronization with edge applications, including hospital-at-home setups, becomes seamless. On the patient care front, MongoDB can support AI-driven recommendations for personalized patient education and engagement based on the analysis of individual health records and engagement patterns, and Vector Search can power search functionalities within patient portals, allowing patients to easily find relevant information and resources, thereby improving the self-service experience. Enhanced clinical decision making Healthcare decision-making is critically dependent on the ability to aggregate, analyze, and act on an exponentially growing volume of data. From EHRs and imaging studies to genomic data and wearable device data, the challenge is not just the sheer volume but the diversity and complexity of data. Healthcare professionals need to synthesize information across various dimensions to make informed, real-time, accurate decisions. Interoperability issues, data silos, lack of data quality, and the manual effort required to integrate and interpret this data all stand in the way of better decision-making processes. The advent of AI technologies, particularly NLP and LLMs, offers transformative potential for healthcare decision-making by automating the extraction and analysis of data from disparate sources, including structured data in EHRs and unstructured text in medical literature or patient notes. By enabling the querying of databases using natural language, clinicians can access and integrate patient information more rapidly and accurately, enhancing diagnostic precision and personalizing treatment approaches. Moreover, AI can support real-time decision-making by analyzing streaming data from wearable devices, alerting healthcare providers to changes in patient conditions that require immediate attention. MongoDB, with its flexible data model and powerful data development platform, is uniquely positioned to support the complex data needs of healthcare decision-making applications. It can seamlessly integrate diverse data types, from FHIR-formatted clinical data to unstructured text and real-time sensor data, in a single platform. By integrating MongoDB with Large Language Models (LLMs), healthcare organizations can create intuitive, AI-enhanced interfaces for data retrieval and analysis. This integration not only reduces the cognitive load on clinicians but also enables them to access and interpret patient data more efficiently, focusing their efforts on patient care rather than navigating complex data systems. MongoDB's scalability ensures that healthcare organizations can manage growing data volumes efficiently, supporting the implementation of AI-driven decision support systems. These systems analyze patient data in real-time against extensive medical knowledge bases, providing clinicians with actionable insights and recommendations, thereby enhancing the quality and timeliness of care provided. MongoDB's Vector Search further enriches decision-making processes by enabling semantic search across vast datasets directly within the database. This integrated approach enables the application of pre-filters based on extensive metadata, enhancing the efficiency and relevance of search results without the need to synchronize with dedicated search engines or vector stores, meaning healthcare professionals can utilize previously undiscoverable insights, streamlining the identification of relevant information and patterns. Clinical trials and precision medicine The need for innovation and transformation isn’t just limited to the patient-provider-healthcare system experience. The challenges of conducting clinical trials and advancing precision medicine are significant, from identifying and enrolling suitable participants to data management practices are fraught with the potential for errors, compromising the accuracy and reliability of trial outcomes. Moreover, the traditional one-size-fits-all approach to treatment development fails to address the unique genetic makeup of individual patients, limiting the effectiveness of therapeutic interventions. AI can make clinical trials faster and treatments more personalized. It's like having a super-smart assistant that can quickly find the right people for studies, keep track of all the data without making mistakes, and even predict which medicines will work best for different people. This means doctors can create safe, efficient treatments that fit you perfectly, just like a tailor-made suit. Plus, with AI's help, these custom treatments can be developed quicker and be more affordable, bringing us closer to a future where everyone gets the care they need, designed just for them. It's a big step towards making medicine not just about treating sickness but about creating health plans that are as unique as patients are. MongoDB plays a pivotal role in modernizing clinical trials and advancing precision medicine by addressing complex data challenges. Its flexible data model excels in integrating diverse data types, from EHRs and genomic data to real-time patient monitoring streams. This capability is crucial for clinical trials and precision medicine, where combining various data sources is necessary, sometimes through a project purpose ODL, to develop a comprehensive understanding of patient health and treatment responses. For clinical trials, MongoDB can streamline participant selection by efficiently managing and querying vast datasets to identify candidates who meet specific criteria, significantly reducing the recruitment time. Its ability to handle large-scale, complex datasets in real-time also facilitates the dynamic monitoring of trial participants, enhancing the safety and accuracy of trials. Other notable use cases Patient Flow Optimization and Emergency Department Efficiency: AI algorithms can process historical and real-time data to forecast patient volumes, predict bed availability, and identify optimal staffing levels, enabling proactive resource allocation and patient routing. Virtual Health Assistants for Chronic Disease Management: Utilizing AI-powered virtual assistants to monitor patients' health status, provide personalized advice, and support medication adherence for chronic conditions such as diabetes and hypertension. AI-Enhanced Digital Pathology and Medical Imaging: Build modern VNA (Vendor Neutral Archive and Digital pathology solutions with innovative approaches, dealing with interoperable data, and manage extensive metadata associated with all your resources enabling fast findings and automated annotations. Operational Efficiency in Hospital Resource Management: Implementing AI to optimize hospital operations, from staff scheduling to inventory management, ensuring resources are used efficiently and patient care is prioritized. Learn more about AI use cases for top industries in our new ebook, How Leading Industries are Transforming with AI and MongoDB Atlas .

April 22, 2024