4 Ways Telcos Deliver Mission-Critical Network Performance and Reliability

Tech leaders like Google, Apple, and Netflix set a new standard for customer service. Today’s customers expect intuitive, always-on, seamless service that challenges telecommunications companies’ network performance and reliability. This article examines several ways that companies can meet these challenges through an automated, data-driven approach.

How a modern data platform can help

A fully integrated, customer-centric, and data-driven approach to service delivery and assurance is needed to remain competitive. Modern telecommunications enterprises are tackling this problem by investing in areas like AI and machine learning, for example, which can help them identify correlations between disparate, diverse sources of data and automate end-to-end network operations, including:

  • Network security

  • Fraud mitigation

  • Network optimization

  • Customer experience

Furthermore, by adopting a modern data platform, companies can easily answer questions, such as the following, that are nearly impossible to resolve when relying on legacy technology:

  • Is an event likely to have a customer impact?

  • Are customer-facing service SLAs being met?

  • Where should cell sites be placed for maximum ROI?

  • Is new equipment deployed and configured correctly?

MongoDB’s developer data platform can help companies provide the necessary performance and reliability to meet customers’ expectations in four key areas: reducing data complexity, service assurance automation, network intelligence and automation, and TM Forum Open APIs.

Reducing data complexity

One recent study found that data scientists spend about 45% of their time loading and cleansing data. For a true impact to your organization, you need to free up that time to enable data scientists to focus on mission-critical projects and innovation. Additionally, architectural complexity, with bolted-on solutions and legacy technology, prevents you from harnessing your data and having a true impact on network performance and reliability.

MongoDB’s developer data platform solves the great complexity problem by supporting a diverse range of workloads from a single data platform. Reducing the channels for data flow allows companies to establish a single source of truth, achieve a customer-centric approach that is critical for competitive advantage, and increase service assurance.

Diagram of MongoDB's Developer Data Platform. The platform includes (from top to bottom) the Document Model with features such as Key Value Pairs, Graphs, Relationships, Geospatial, Time Series, and Objects. This is followed by Unified interface with features such as Serverless Applications, Mobile Applications, Distributed Applications, Analytical, Search, and Transactional. At the base, our platform is built upon being secure, resilient & elastic, global, and multi-cloud.
Figure 1. MongoDB’s developer data platform reduces complexity in telecommunications workloads, resulting in more reliable network service for customers.

With continuous uptime and advanced automation, MongoDB’s developer data platform ensures performance, no matter the scale.

Service assurance automation

In telecommunications, always-on, always-available service both for the end user and internal IT teams is critical. While outdated service assurance processes may have been viable decades ago, the volume of data and number of users have grown exponentially, making manually intensive processes of the past no longer possible. This volume increase will continue to stress existing business support systems, and without modernization, it will hamper the development of new revenue streams.

Moving from a reactive to proactive and then predictive model, as shown in Figure 2, will enhance service assurance and enable organizations to meet the expectations of the digital-native customer.

Graphic of the transition from Reactive to Proactive to predictive data model. Reactive is defined as responding to alerts generated by network components or service complaints raised by end customers. A Reactive data model is associated with Legacy systems, which are defined as being highly structured, normalized data. Analytical functions performed via batch-base systems on EDW data. The KPI for reactive is time to resolve fault and the outcome is reestablish QoS. The Proactive data model is defined as predicting spikes, congestion and equipment failure which has the ability to impact end customers before it happens. A Proactive data model is associated with Interactive data science, which is defined as supervised data science tools and techniques used to derive predictive models based on historical data. The KPI for the Proactive model is reduced uptime challenges with the outcome being to prevent degradation in QoS. The Predictive data model is defined as Automatically take preventative action to prevent customer service interruption or to limit the impact of any interruption. The Predictive data model is associated with Industrial AI Ops, which is defined as Using advanced AI techniques over diverse, flexible data sets to autonomously act on real-time events. The KPI for the Predictive model is optimized service availability with the outcome being cost reduction and infrastructure optimization.
Figure 2. The transition from a reactive to proactive to predictive data model opens up new opportunities to use innovative technologies like artificial intelligence.

Network intelligence and automation

Consider the essential task of configuration and management of radio access networks. On a daily basis, engineers change the angles of antenna towers, the configuration of the radio, the nearest neighbor relations, and other events your system tracks and manages.

With an intuitive developer data platform, any change in the configuration is saved in the data mediation layer (DML) for anyone to see and track, making it easy for engineers to go to the DML to check the configuration for a particular tower. Information that was previously captured in one snapshot per day is now propagated in real time.

Another example — intent-based automation — abstracts the complexity of underlying software-defined networking components by allowing intent to be specified and by providing automatic translation. This type of automation allows teams to process intent generated either by end user activity or via service assurance processes, and that intent is translated into the underlying network state.

Network events determine whether the network is in the desired, stable state, and that unintended states are addressed via automation, potentially using TM Forum Network-as-a-Service APIs.

TM Forum Open APIs

The TM Forum (TMF) is an alliance of more than 850 companies that accelerates digital innovation through its TMF Open APIs, which provide a standard interface for the exchange of different telco data models. The use of TMF Open APIs ranges from providers of off-the-shelf software to proprietary developments of the largest telecommunications providers.

In working with many of the world’s largest communication service providers (CSPs) and their related software provider ecosystems, MongoDB has seen a significant number of organizations leverage these APIs to develop new microservices in days, rather than weeks or months. Through exposing common interfaces, CSPs are able to adopt a modular architecture made up of best-of-breed components (either internally or externally developed) while minimizing the time, effort, and cost required to integrate them.

The TMF Network-as-a-Service APIs, in particular, hold significant potential for network automation. This API component suite supports a set of operational domains exposing and managing network services. The abstraction layer between network automation tooling and the underlying network infrastructure provides a flexible, modular architecture.

Network optimization is vital to the survival of telcos in today’s competitive market. However, with a modern developer data platform underpinning your network, you’ll be equipped to meet and exceed customer expectations.

Read our ebook to learn more about implementing TM Forum Open APIs with MongoDB.

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7 Ways to Reduce Your Database's Carbon Footprint

In his technology predictions for 2022 , Werner Vogels, VP and CTO of Amazon, reminded the industry that sustainability is no longer a secondary consideration — nor something someone else is responsible for. “As developers, we are trained to think about how to optimize our architectures for factors like security, performance, reliability, and cost,” he wrote. “In 2022, you can add sustainability to that list. What we will begin to see in the coming years is developers taking an active role in building sustainability-conscious architectures that take into account not just the problems they are solving, but the planet as well.” Vogels’s vision makes it clear that there is a difference between how we measure the efficiency of databases and, perhaps, how we should. In this article, we’ll look at several steps organizations can take to reduce the carbon footprint of their databases. It all starts with the cloud. Move off premises Migrating workloads to a public cloud creates an instant return on sustainability efforts, with an average 84% immediate reduction in carbon emissions as a result. This reduction results from a combination of factors, including the fact that cloud data centers use less physical space and resources than those on-premises; the use of more efficient servers and higher server usage; and the fact that public cloud providers are themselves committed to sustainable infrastructure and a reduced carbon footprint, which enhances and supports their customers’ efforts as well. For example, the three major public cloud providers have made the following commitments: Amazon Web Services (AWS) is committed to being powered with 100% renewable energy by 2025 and has pledged to be carbon neutral by 2040 across all Amazon properties. Microsoft Azure has also pledged to be completely powered by renewable energy by 2025 and is committed to being water-positive and zero-waste by 2030. Google Cloud already boasts the use of 100% renewable energy in all regions, with a goal to use only carbon-free energy by 2030. Consider serverless Businesses can amplify that carbon reduction by selecting a database platform that aligns with their energy goals. MongoDB is currently in 80-plus regions, with 12 of those regions being powered by renewable energy as well as being low-carbon and/or zero-waste. Furthermore, MongoDB Atlas Serverless instances are optimized to use the underlying infrastructure — RAM, CPU, disks, network bandwidth — to its maximum before consuming additional cloud resources. Currently, 60% of organizations globally have yet to adopt any serverless technology; however, pushing to serverless not only helps to reduce carbon footprint but also can provide a competitive advantage. Consolidate your platform Databases require management on many fronts, from transactional data and relational data to search, analytics, graph use cases, time series, and more. Each of these facets is frequently handled in its own separate database, necessitating management, monitoring, and resources. Not only is this a technical pain point, but it can also affect the environmental goals of an organization and its carbon footprint overall. To start, fewer workloads result in a lower carbon emission total. Thus, simply paring down the number of databases used is an easy way to reduce the consumption of resources and associated carbon emissions. MongoDB Atlas brings all of those requirements into a single, consolidated database platform that solves for disparate and siloed databases and reduces resources and consumption. Strategize around prevention The greenest energy is the energy that is never used in the first place. Toward that end, organizations can work to shift their architectural strategy to be more mindful of carbon and energy considerations, and adjustments can be made to reduce usage without affecting performance. For example, about 40% of instances are at least one size larger than needed . Businesses can reduce such excess consumption by avoiding over-provisioned databases. This change can be done easily through automatic scaling with MongoDB Atlas cluster and storage auto-scaling, as well as with Atlas Triggers , which allows users to schedule scaling events. Another consideration is the consumption of non-production environments. Currently, more than 44% of total compute resources are being used in non-production environments like dev, testing, staging, and pre-production. This consumption typically does not occur outside of work hours, and it’s easy to pause these resources outside of peak hours in Atlas, either on-demand or with an Atlas Trigger. Continue to iterate These major adjustments to the structure of your database architecture represent impactful steps toward creating a sustainable database, but there are many, more granular ways to improve database technology and better align with environmental goals. Implementing smaller changes will chip away at carbon emissions and create significant impact over time. Such easy-to-execute suggestions include the following: Consider the impact of data transfer over longer distances and find new regions to house data. Determine the proper location of data and leverage the flexibility to deploy in any region, which grants more control over replication of data. Use Atlas Global Clusters to ensure that data stays within a defined geography, thereby minimizing the infrastructure required to deploy a globally distributed application. Employ Atlas Data Federation to minimize the need to replicate data between cloud object storage and Atlas by allowing users to query both sources from a single interface. Leverage Atlas Analytics Nodes to offload workloads to separate nodes. And note that, with MongoDB 6.0 , you can manually size those nodes to trim even more resources. Use analytic nodes that are deployed in more environmentally sustainable regions. Leverage Atlas’s many dashboards and alerts to be proactively notified when system resources are rising beyond acceptable thresholds. Introduce an “eco mode” in your apps, giving users the ability to agree to potentially lower response times in exchange for a positive environmental impact. Avoid peak times for predictable workloads; schedule these during a time that will not drive over-consumption. Use data tiering; users can automate data lifecycles using Atlas Online Archive and reduce exhaustion of resources. Don't wait The time to act is now. Studies estimate that between 2% and 4% of global emissions come from the information and communications technology industry — about the same as from aviation. Much of that impact is concentrated in data centers, which consume 10 times to 50 times the energy per square foot compared to a typical office building. With every day that businesses do not improve their carbon footprint, the environmental problem gets worse. For example, IDC found that the number of data centers worldwide has grown from 500,000 in 2012 to more than 8 million today. And data itself is exploding, with 2.5 quintillion new bytes of data created daily . Because of this, the amount of energy used by data centers will continue to double every four years, meaning data centers have the fastest-growing carbon footprint of any area within the technology industry sector. Organizations need a proper database framework to combat these negative environmental trends without creating additional and unnecessary carbon emissions that add to the global warming crisis. As a developer data platform, MongoDB provides businesses with the database architecture to reduce consumption of resources, and MongoDB Atlas offers additional features to enhance carbon reduction efforts. Get inspired Businesses are a strong lever for change in the world. When it comes to carbon reduction, MongoDB is committed to creating sustainable and lean data architectures, both internally and for customers. Check out these three stories of companies that have taken incredible strides to curtail their carbon impact. Business leaders can all be inspired by and learn from each other to make changes and collectively benefit the Earth and future generations. Learn about this and more from MongoDB Solutions Architect, Snehal Bhatia’s, presentation on Designing a Sustainable Architecture from MongoDB World 2022 . .

August 24, 2022
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The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

July 18, 2024