How Telcos Drive Mission-Critical Innovation and Cost Savings Through Automation
Modern telecommunications customers don’t only expect flawless network performance, they demand it. For global enterprises that hold the key to human connection, the industry standard is nothing less than a fully integrated, customer-centric approach to service delivery. How are telecommunications firms tackling these customer expectations today? By embracing AI and machine learning capabilities, developing new decisioning models, and ensuring network security and optimization with a 99.995% uptime SLA. Why automation is vital in the telco modernization journey AI adoption is growing at a break-neck speed, and the telecommunications industry has a close eye on the way that automated decision-making can improve operational efficiency and reduce costs. In the latest TM Forum Digital Transformation Tracker 7 survey , 73% of respondents agreed that operational efficiency and cost reduction were very important drivers of CSPs’ digital transformation journeys, compared with 69% in 2022 and 65% in 2018. By eliminating manual tasks, and reducing errors introduced by manual intervention, automation is improving end-to-end performance and reducing handoffs and touchpoints. In the case of automated decision-making, it’s possible to leverage large volumes of data that already exist within telco organizations, alongside data science and machine learning techniques, to generate data-driven insights and inferences to better serve customers and develop cost savings. Three automation use cases in the telecommunications industry Here are three ways that MongoDB’s customers in the telecommunications industry can innovate with automation: Service assurance processes: Telcos can proactively identify issues impacting customers, or even predict them before they occur, utilizing automated processing of large amounts of diverse data. Network automation techniques can then step in to automatically remediate the situation, and output intent that can be processed by intent-based network automation tooling. Network automation: While service assurance processes can make decisions around what needs to be done, network automation tooling takes responsibility for effecting change. What’s more, the demands of 5G networks will force operators to open up traditional closed systems to third parties via network APIs. For example, these APIs can allow automated provisioning and configuration of 5G network slices. Customer expectations here will be that these operations are self-serve, and will happen in real time, making automation key. Customer issue management: We’ve all come across chatbots used in B2C customer service experiences. As chatbots become more sophisticated, potentially leveraging modern generative AI techniques, many more customer care issues will be automated. This change will not only reduce the cost of call centers, but speed up mean time to resolution for customer care issues. The future of AI/ML-based automated decision-making in telecommunications We’ve established the importance of automation for service assurance, network automation, and customer experiences. By utilizing the power of AI and data science, telcos have the opportunity to take these technologies further into the realm of network security and fraud mitigation. However, getting to automation with unstructured data is no simple task. Studies show that more than 50% of data scientists’ time is spent wrangling data, and more than 80% of all essential data is unstructured. Figure 1: Data processing operations for machine learning models. To build out a new AI/ML-based system, both data processing and ML capabilities must be in place. These two solutions are typically provided by different systems with clear integration points. Any AI/ML-based solution requires large amounts of historical data to train the model. The storage and feeding of this data is usually the job of a traditional data warehouse system. This gets complicated when decisions need to be made in real time with “live” data, such as in anti-fraud use cases. To achieve real-time results, it’s required to integrate an operational database into the architecture to stream real-time data and requests into the model and to persist the model output. In this hybrid system example, we have both operational and analytics data requirements co-existing; this interaction adds to the overall architectural complexity of the system. It’s important to note that raw data cannot be used by AI models “as it.” First the data must be cleaned, potentially deduplicated and turned into features. Standardized techniques are required to do this, including binning, normalization, standardization, and one-hot encoding. The aggregation pipeline provides powerful data processing capabilities that assist with this process. MongoDB Atlas, the developer data platform, is capable of handling each of the above requirements from a single platform. Its analytical nodes and Data Lake allow for massive amounts of historical data storage, and service to the model for training purposes. Figure 2: MongoDB's role in machine learning pipeline. Real-time data can also be ingested and served through MongoDB Atlas via change streams, triggers, and integrations. The powerful aggregation framework of MongoDB is capable of transforming raw data into usable features. Lastly, integration patterns based on Spark, Kafka, and HTTP are supported out of the box, which greatly reduces overall architectural complexity. Once decision-making models produce the new data output, it can be persisted back into the transactional database automatically actioned by additional automation tooling. Decision-making models will constantly evolve and will require new facets of data to be used. MongoDB’s document model and dynamic schema naturally supports the ingestion of new types and formats of data without the need for complex schema changes. > Join MongoDB, Hansen Technology and TM Forum for a live discussion on the future of AI in the telecommunications industry.
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. 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. 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 .
Mobile Edge Computing, Part 1: Delivering Data Faster with Verizon 5G Edge and MongoDB
As you’ve probably heard, 5G is changing everything, and it’s unlocking new opportunities for innovators in one sector after another. By pairing the power of 5G networks with intelligent software, customers are beginning to embrace the next generation of industry, such as powering the IoT boom, enhancing smart factory operations, and more. But how can companies that are leveraging data for daily operations start using data for innovation? In this article series, we’ll explore how the speed, throughput, reliability, and responsiveness of the Verizon network, paired with the sophistication of the next generation MongoDB developer data platform, are poised to transform industries including manufacturing, agriculture, and automotive. Mobile edge computing: The basics Companies everywhere are facing a new cloud computing paradigm that combines the best experiences of hyperscaler compute and storage with the topological proximity of 5G networks. Mobile edge computing , or MEC, introduces a new mode of cloud deployments whereby enterprises can run applications — through virtual machines, containers, or Kubernetes clusters — within the 5G network itself, across both public and private networks. Before we dive in, let’s define a few key terms: What is mobile edge computing? The ability to deploy compute and storage closer to the end user What is public mobile edge computing? Compute and storage deployed with the carrier data centers What is private mobile edge computing? On-premise provisioned compute and storage Verizon 5G Edge , Verizon’s mobile edge compute portfolio, takes these concepts from theoretical to practical. By creating a unified compute mesh across both public and private networks, Verizon 5G Edge produces a seamless exchange of data and stateful workloads — a simultaneous deployment of both public and private MEC best characterized as a hybrid MEC. In this article, we’ll primarily focus on public MEC deployment. Although MEC vastly increases the flexibility of data usage by both practitioners and end users, the technology is not without its challenges, including: Deployment: Given a dynamic fleet of devices, in an environment with 20-plus edge zones across both public and private MEC, to which edge(s) should the application be deployed? Orchestration: For Day 2 operations and beyond, what set of environmental changes, — be it on the cloud, network, or on device(s) — should trigger a change to my edge environment? Edge discovery: Throughout the application lifecycle, for a given connected device, which edge(s) is the optimal endpoint for connection? Fortunately for developers, Verizon has developed a suite of network APIs tailored to answer these questions. From edge discovery and network performance to workload orchestration and network management, Verizon has drastically simplified the level of effort required to build resilient, highly available applications at the network edge without the undifferentiated heavy lifting previously required. Edge discovery API workflow Using the Verizon edge discovery API, customers can let Verizon manage the complexity of maintaining the service registry as well as identifying the optimal endpoint for a given mobile device. In other words, with the edge discovery API workflow in place of the self-implemented latency tests, a single request-response would be needed to identify the optimal endpoint, as shown in Figure 1. Figure 1. A single request-response is used to identify the optimal endpoint Although this API addresses challenges of service discovery, routing, and some advanced deployment scenarios, other challenges exist outside of the scope of the underlying network APIs. In the case of stateful workloads, for example, how might you manage the underlying data generated from your device fleet? Should all of the data live at the edge, or should it be replicated to the cloud? What about replication to the other edge endpoints? Using the suite of MongoDB services coupled with Verizon 5G Edge and its network APIs, we will describe popular reference architectures for data across the hybrid edge. Delivering data with MongoDB Through Verizon 5G Edge, developers can now deploy parts of their application that require low latency at the edge of 4G and 5G networks using the same APIs, tools, and functionality they use today, while seamlessly connecting back to the rest of their application and the full range of cloud services running in a cloud region. However, for many of these use cases, a persistent storage layer is required that extends beyond the native storage and database capabilities of the hyperscalers at the edge. Given the number of different edge locations where an application can be deployed and consumers can connect, ensuring that appropriate data is available at the edge is critical. It is also important to note that where consumers are mobile (e.g., vehicles), the optimal edge location can vary. At the same time, having a complete copy of the entire dataset at every edge location to cater for this scenario is neither desirable nor practical due to the potentially large volumes of data being managed and the associated multi-edge data synchronization challenges that would be introduced. The Atlas solution The solution requires having an instantaneous and comprehensive overview of the dataset stored in the cloud while synchronizing only required data to dedicated edge data stores on demand. For many cases, such as digital twin, this synchronization needs to be bi-directional and may potentially include conflict resolution logic. For others, a simpler unidirectional data sync would suffice. These requirements mean you need a next-gen data platform, equipped with all the power to simplify data management while also delivering data in an instant. MongoDB Atlas is the ideal solution for the central, cloud-based datastore. Atlas provides organizations with a fully managed, elastically scalable developer data platform upon which to build modern applications. MongoDB Atlas can be simultaneously deployed across any of the three major cloud providers (Amazon Web Services, Microsoft Azure, and Google Cloud Platform) and is a natural choice to act as the central data hub in an edge or multi-edge based architecture, because it enables diverse data to be ingested, persisted, and served in ways that support a growing variety of use cases. Central to MongoDB Atlas is the MongoDB database, which combines a flexible document-based model with advanced querying and indexing capabilities. Atlas is, however, more than just the MongoDB database and includes many other components to power advanced applications with diverse data requirements, like native search capabilities, real-time analytics, BI integration, and more. Read the next post in this blog series to explore the real-world applications and innovations being powered by mobile edge computing.
Mobile Edge Computing, Part 2: Computing in the Real World
It would be easy to conceptualize mobile edge computing (MEC) as a telecommunications-specific technology ; but, in fact, edge computing has far-reaching implications for real-world use cases across many different industries. Any organization that requires a solution to common data usage challenges, such as low-latency data processing, cloud-to-network traffic management, Internet of Things (IoT) application development, data sovereignty, and more, can benefit from edge-based architectures. In our previous article , we discussed what mobile edge computing is, how it helps developers increase data usage flexibility, and how Verizon 5G Edge and MongoDB work in conjunction to enable data computing at the edge, as shown in Figure 1. Figure 1. Verizon and MongoDB work in conjunction to deliver data to consumers and producers faster than ever with mobile edge computing. In this article, we’ll look at real-world examples of how mobile edge computing is transforming the manufacturing, agriculture, and automotive industries. Smart manufacturing Modern industrial manufacturing processes are making greater use of connected devices to optimize production while controlling costs. Connected IoT devices exist throughout the process, from sensors on manufacturing equipment to mobile devices used by employees on the factory floor to connected vehicles transporting goods — all generating large amounts of data. For companies to realize the benefits of all this data, it is critical that the data be processed and analyzed in real time to enable rapid action. Moving this data from the devices to the cloud for processing introduces unnecessary latency and data transmission that can be avoided by processing at the edge. As seen in Figure 2, for example, sensors, devices, and other data sources in the smart factory use the Verizon 5G Edge Discovery Service to determine the optimal edge location. After that, data is sent to the edge where it is processed before being persisted and synchronized with MongoDB Atlas — all in an instant. Figure 2. Data sources in smart factories use the Verizon 5G Edge Discovery Service to determine the optimal edge location. Process optimization Through real-time processing of telemetry data, it’s possible to make automated, near-instantaneous changes to the configuration of industrial machinery in response to data relayed from a production line. Potential benefits of such a process include improved product quality, increased yield, optimization of raw material use, and ability to track standard key performance indicators (KPIs), such as overall equipment efficiency (OEE). Preventative maintenance Similar to process optimization, real-time processing of telemetry data can enable the identification of potential impending machinery malfunctions before they occur and result in production downtime. More critically, however, if a situation has the potential either to damage equipment or pose a danger to those working in the vicinity, the ability to automatically perform shut downs as soon as the condition is detected is vital. Agriculture One of the most powerful uses of data analytics at scale can be seen in the agriculture sector . For decades, researchers have grappled with challenges such as optimal plant breeding and seed design, which to date have been largely manual processes. Through purpose-built drones and ground robotics, new ways to conduct in-field inspection using computer vision have been used to collect information on height, biomass, and early vigor, and to detect anomalies. However, these robots are often purpose-built with large data systems on-device, requiring manual labor to upload the data to the cloud for post-processing. Using the edge, this entire workflow can be optimized. Starting with the ground robotics fleet, the device can be retrofitted with a 5G modem to disintermediate much of the persistent data collection. Instead, the device can collect data locally, extract relevant metadata, and immediately push data to the edge for real-time analytics and anomaly detection. In this way, field operators can collect insights about the entirety of their operations — across a given crop field or nationwide — without waiting for the completion of a given task. Automotive Modern vehicles are more connected than ever before, with almost all models produced today containing embedded SIM cards that enable even more connected experiences. Additionally, parallel advances are being made to enable roadside infrastructure connectivity. Together, these advances will power not just increased data sharing between vehicles but also between vehicles and the surrounding environment (V2V2X). In the shorter term, edge-based data processing has the potential to yield many benefits both to road users and to vehicle manufacturers . Data quality and bandwidth optimization Modern vehicles have the ability to transmit large amounts of data not only in terms of telemetry relating to the status of the vehicle but also in regard to the observed status of the roads. If a vehicle detects that it is in a traffic jam, for example, then it might relay this information so that updates can be made available to other vehicles in the area to alert drivers or replan programmed routes, as shown in Figure 3. Figure 3. Mobile edge computing enables data generated from multiple sources within a vehicle to be shared instantly. Although this is a useful feature, many vehicles may be reporting the same information. By default, all of this information will be relayed to the cloud for processing, which can result in large amounts of redundant data. Instead, through edge-based processing: Data is shared more quickly between vehicles in a given area using only local resources. Costs relating to cloud-based data transfer are better controlled. Network bandwidth usage is optimized. While improving control of network usage is clearly beneficial, arguably a more compelling use of edge-based processing in the automotive industry relates to aggregating data received from many vehicles to improve the quality of data sent to the cloud-based data store. In the example of a traffic jam, all of the vehicles transmitting information about the road conditions will do so based on their understanding gained through GPS as well as internal sensors. Some vehicles will send more complete or accurate data than others, but, by aggregating the many different data feeds at the edge, this process results in a more accurate, complete representation of the situation. Read Part 1 of this blog series . Download our latest book on computing at the edge .
How Telcos Are Transforming to Digital Services Providers
The telecommunications industry is in the midst of a digital revolution, shifting from a traditional service delivery model to one that is increasingly customer-centric and that extends beyond the provision of traditional connectivity services to include diverse digital services. Telcos undergoing this modernization journey are digital services–focused first, offering apps, streaming services, retail platforms, peer-to-peer payment platforms, and more. As telcos delve into the complex 5G, IoT, and AI technologies powering personalized and real-time user experiences, pressure is increasing on aging networks and business support system (BSS) infrastructures. MongoDB customers like TIM and Telefónica are using the MongoDB Atlas developer data platform to deliver a robust platform-focused experience that complements existing technologies. Through an integrated modernization approach, telcos are improving both customer and developer experiences, building innovative new applications. In a recent roundtable discussion , Boris Bialek , MongoDB global head of industry & solutions, sat down with telco IT leaders Paolo Bazzica , head of digital solutions at Italy’s TIM, and Carlos Carazo , global CTO of Spain’s Telefónica Tech IoT and Big Data division. This article provides an overview of the discussion and insights into how platform thinking is invigorating telco IT teams. From communications services providers to digital services providers The shifting value chain in telecommunications. Source: Kearney The shift and expansion from traditional communications services to a comprehensive digital services suite requires global telecommunications companies to rethink their monetization strategies. Even before the pandemic, an evolution was well underway for telecommunications providers. From 2010 to 2020, overall revenue coming from connectivity services grew by only 2%, according to research compiled by Kearney. During the same period, digital services experienced a five-fold increase. Although telecommunications providers successfully sparked a revolution that grew into a $6.3 trillion digital economy, only those capitalizing on digital services reaped the benefits. In 2020, digital services like e-commerce and online advertising surged, capturing nearly 80% of growth. Leveraging platform thinking As network operators evolve to digital service providers, the idea of platform thinking is rippling across the industry. Network connectivity was tested with the hardships of the March 2020 COVID-19 lockdown in Italy, but TIM’s digital platform project Fly Together , which was initiated in 2018, helped bridge the divide. “People went from their normal lives to a full lockdown in one day. People realized that telco was a key point, because you need to stay at home, but you still need to communicate to work and go to school,” said Bazzica in the virtual roundtable discussion hosted by MongoDB. “Our digital platform was the way to refill or top up your account, and access ebooks and so on, so I think it’s more than just an evolution for the business; it's a different positioning.” Today, customer trust is a key differentiator and essential focus for TIM. People rely on TIM’s services to keep the country going. And TIM continues to modernize the digital experiences of its customers through the Fly Together platform. “From my perspective, this is definitely a trend, and I think it’s the evolutionary stalwart of the digital life of the people to be relevant and continue to be their trusted partner,” Bazzica said. A similar dynamic led to the creation of Telefónica Tech two years ago, a division of Spain’s Telefónica SA, according to Carazo. The new business is split into two units: one dedicated to offering cloud or cybersecurity solutions and the other offering IoT or big data digital services, which are the services customers need to pursue their own digital transformations. “We are strongly convinced that connectivity is the basis for any new digital economy, so we are really proud to offer connectivity for these customers,” Carazo said. At the center of Telefónica Tech’s transformation is its Kite Platform , run on MongoDB, which is a managed connectivity platform running close to 30 million IoT devices all over the world. The platform provides connectivity, but it goes beyond IoT connectivity and provides multidimensional benefits across all IoT environments from the devices to the product connecting the clouds. This is the foundational component of Telefónica Tech’s portfolio, which delivers new business use cases across industries. Modernizing applications and evolving to microservices and APIs How can a telco simplify this complex journey to modernization? For TIM, the change was driven by a desire to modernize 700 different applications before effectively going into the digital business. TIM launched Fly Together to build a digital layer that serves the scalability and latency needed to transform customers’ digital service experiences. Before, a customer could be querying up to 14 systems, depending on which apps were open. Without the digital experience layer, you can’t express an SLA or determine how long it takes to open an app, according to Bazzica. The first task of Fly Together was to build the layer that decoupled the backend systems from the model that helps run TIM’s digital channels. Through its work with MongoDB over the past four years, TIM launched a resilient platform that doesn’t require exotic hardware to run efficiently. Because the platform was developed in a cloud-native environment, it comprises containerized microservices and RESTful APIs, setting a new standard for the company’s development of applications. “We are able to modernize, but gradually. We still have our mainframe running,” Bazzica said. “The real experience is seeing the company learning and experimenting. That’s another value with this type of technology; we can try a lot of different things with minimum effort and make big discoveries.” Four digital services trends to watch IoT is driving many exciting use cases for Telefónica Tech’s new business division. Within the B2B sector, there is healthy growth across four key industry use cases, according to Telefónica’s Carazo. Connected Industry and IoT — Telefónica starts with providing private network solutions. These technologies are expected to evolve to more complex use cases like robotics and predictive maintenance in small and medium factories within the next five years. Smart metering — Massive growth is expected in smart metering, which uses electronic devices to measure energy consumption. The implementation of this trend could spur demand for millions of connected devices. Connected cars — This sector is expected to grow significantly in the next five to 10 years as operators deploy new digital services like infotainment, security, and safety applications. Smart cities — Cities around the world are seeking services for their digital citizens looking to live in more sustainable and flexible communities. These use cases are critical to building modern cities, societies, and industries. Platform thinking and an integrated approach to modernization will help telcos create modern applications, extending their businesses beyond conventional services to include novel digital services. Watch our webinar to learn more about TIM and Telefónica’s transformation to digital services providers.
Content Discovery: How to Win the Battle for Attention
Think of the last app you used today. For me, it was searching for the latest episode of Sesame Street on HBOMax for my toddler. For someone else, it was finding a YouTube video on how to bake a cake. Or listening to a song recommended by Spotify. All of these instances, steps we barely put any thought into, are examples of content discovery , the bidirectional process by which users and applications interact, ensuring users’ known and unknown content consumption needs are fulfilled. As content is being generated at a nearly unfathomable and exponential pace ( think 500 hours of videos uploaded to YouTube every single minute ), catching and holding consumers’ attention with content is only going to become more difficult. Delivering great content discovery experiences that meet evolving customer expectations will be the only way to keep up. Content discovery happens in two ways, resembling push and pull forces: Push (recommendation engines): Content is suggested to the user. This can look like personalized landing pages or content recommendations. Pull (search): The customer searches for content, typically via a search bar. The user leads the action, and a new opportunity for suggesting relevant content is created. Consider how you consume content. Maybe you’re searching for a show you want to watch, or once that show is completed, the app you’re using recommends another similar show you might like. If media providers can master both of these processes – accurate search and intuitive recommendations – you can expect to fuel user engagement and decrease churn. Simple enough, right? Unfortunately, developing and deploying cutting-edge search and recommendation engines is easier said than done. A few major challenges stand in the way, like integrating data from multiple sources with excruciating extract, transform, load (ETL) pipelines, adding and maintaining a separate search engine solution, reduction in both time-to-value and developer productivity, and more. Having a unified data platform that can handle analytics at scale and search natively is a massive advantage for effective content discovery. Let’s look at how an advanced data platform like MongoDB Atlas makes the push and pull of content discovery possible. The push: Real-time, relevant recommendations Hitting users with the content they want when they want it (whether they know it's the content they want or not) is the aim of any recommendation engine. It’s particularly important in the media content consumption game, since there are so many competing platforms vying for user attention. As the volume and variety of user data increases by the second — generated from what they’re watching, what they stopped watching, what devices they’re using to interact with content — recommendations engines need to move beyond simple if-then-else statement based on historical data to advanced machine learning model that learns with data captured in real time, such as a causal inference models that predict what people might want to watch based on what other users with similar profiles and viewing habits are currently watching. MongoDB integrates natively with machine learning and artificial intelligence engines, using change streams to update the ML models to provide recommendations. The consumer profile is updated and saved in MongoDB, acting as the persistence layer and effectively becoming the single-view consumer data platform, a critical component in the pursuit of real-time analytics informing recommendations. Developers now have a single view of data, and machine learning models use that unsoiled data to make lightning-fast, accurate recommendations to keep users engaged with content. MongoDB acts as the catalyst for real-time recommendations informed by customer behavior triggers. The pull: Solving the search bar Virtually every application today has a search function — but it is also challenging to get right. Unlike database queries, where the user knows exactly what they are querying for, search has to give fast and relevant results to open ended, natural language inputs, tolerating typos and partial search terms, and essentially inferring users intent. Ultimately, consumers expect a Google-level search experience, and if they don’t get it, they’ll move to the next content platform. Building your own search engine, that will meet user expectations even as those expectations evolve, is costly in terms of time and resources spent developing and maintaining the engine. Many more database indexes need to be added to support search queries, and the search workload will start to contend for system resources with the core data persistence and processing demands of the application. To avoid resource contention between these two workloads, the database needs to be carefully sized and closely monitored and scaled, driving up operational overhead and cost. Also developing a database search solution won’t offer you any advantage over the competition, since there are dedicated search engines in the market that can do that heavy lifting for you. This reality has led companies to bolt-on a specialized search engine to their database – not that this is a simple solution either. Bolting on a search cluster to your database requires adding a new query language to integrate your application with the search engine, which increases the operational and architectural complexity of your current environment. This results in an elongated time for market for what could be a suboptimal search engine. Atlas Search solves the architecture and operational challenges of adding a separate search engine, since it’s fully integrated with the MongoDB Atlas Data Platform. Powered by the market-leading search engine Apache Lucene, it provides advanced search capabilities, while reducing architectural sprawl. Customers have reported improved development velocities of 30% to 50% after adopting Atlas Search. Atlas manages the required search infrastructure and automatically keeps the search indexes in sync with data mastered in the MongoDB database. Developers interact with search using the same universal interface that they are accustomed to using when interacting with other data in the platform, which means no new solutions to learn or decrease in developer productivity. Maintaining two separate systems adds complexity and lowers productivity, compared to the unified platform offered by MongoDB Atlas. With MongoDB Atlas, you can deliver the right recommendations at the most opportune time, and provide a best-in-class search experience to keep users engaged. No secondary solutions. No months of wasted development. Just a single, simplified process for game-changing content discovery. Take a deeper look into content discovery powered by MongoDB in our recent guide, Simplifying Content Discovery .