Prashant Juttukonda

7 results

Enabling Commerce Innovation with the Power of MongoDB and Google Cloud

Across all industries, business leaders are grappling with economic uncertainty, cost concerns, disruption to supply chains, and pressure to embrace new technologies like generative AI. In this dynamic landscape, having a performant and future-proofed technology foundation is critical to your business’s success. Kin + Carta, a Premier Google Cloud Partner and MongoDB Systems Integrator Partner, recently launched the Integrated Commerce Network . The Integrated Commerce Network is an Accelerator that enables clients to modernize to a composable commerce platform and create value with their commerce data on Google Cloud with a pre-integrated solution in as little as six weeks. This article explains the concept of composable commerce and explores how MongoDB and Google Cloud form a powerful combination that enables innovation in commerce. Finally, it explains how Kin + Carta can help you navigate the complexity facing businesses today with their approach to digital decoupling. Unraveling the complexity: What is composable commerce? Why microservices and APIs? The evolution of commerce architecture Traditional monolithic architectures, once the cornerstone of commerce platforms, are facing challenges in meeting the demands of today's fast-paced digital environment. Microservices, a paradigm that breaks down applications into small, independent services, offer a solution to the limitations of monoliths. This architectural shift allows for improved agility, scalability, and maintainability. Defining composable commerce Composable commerce is a component-based, API-driven design approach that gives businesses the flexibility to build and run outstanding buying experiences free of constraints found in legacy platforms. To be truly composable, the platform must support key tenets: Support continuous delivery without downtime at the component level Have API as the contract of implementation between services, with open, industry-standard protocols providing the glue between components Be SaaS based, or portable to run on any modern public cloud environment Allow the open egress and ingress of data — no black-boxes of vendor data ownership Defining APIs and microservices APIs play a pivotal role in connecting microservices, enabling seamless communication and data exchange. This modular approach empowers businesses to adapt quickly to market changes, launch new features efficiently, and scale resources as needed. Enhanced scalability, resilience, and agility Taking a microservices approach provides businesses with options and now represents a mature and battle-tested approach with commoditized architectures, infrastructure-as-code, and open-source design patterns to enable robust, resilient, and scalable commerce workloads at lower cost and risk. Additionally, the decoupled nature of microservices facilitates faster development cycles. Development teams can work on isolated components, allowing for parallel development and quicker releases. This agility is a game-changer in the competitive e-commerce landscape, where rapid innovation is essential for staying ahead. Microservices and API-based commerce solutions (like commercetools, which is powered by MongoDB) have begun to dominate the market with their composable approach, and for good reason. These solutions remove the dead-end of legacy commerce suite software and enable a brand to pick and choose to enhance its environment on its own terms and schedule. MongoDB Atlas: The backbone of intelligent, generative AI-driven experiences As e-commerce has developed, customers are expecting more from their interactions — flat, unsophisticated experiences just don’t cut it anymore and brands need to deliver on the expectation of immediacy and contextual relevance. Taking a microservices approach enables richer and more granular data to be surfaced, analyzed, and fed back into the loop, perhaps leveraging generative AI to synthesize information that previously would have been difficult or impossible without huge computing capabilities. However, to do this well you need core data infrastructure that underpins the platform and provides the performance, resilience, and advanced features required. MongoDB Atlas on Google Cloud can play a pivotal role in this enablement. Flexible data models: Microservices often require diverse data models. MongoDB Atlas, a fully managed database service, accommodates these varying needs with its flexible schema design, which allows businesses to adapt their data structures without compromising performance. Horizontal scalability: Modern commerce moves a lot of data. MongoDB Atlas excels in distributing data across multiple nodes, ensuring that the database can handle increased loads effortlessly. Real-time data access: Delivering on expectations relies on real-time data access. MongoDB Atlas supports real-time, event-driven data updates, ensuring you are using the most up-to-date information about your customers. Serverless deployment: Rather than spend time and money managing complex database infrastructure, MongoDB Atlas can leverage serverless deployment, allowing developers to focus on building features that delight customers and impact the bottom line. Unleashing generative AI with MongoDB and Google Cloud Generative AI applications thrive on massive datasets and require robust data management. MongoDB effortlessly handles the complex and ever-evolving nature of gen AI data. This includes text, code, images, and more, allowing you to train your models on a richer data tapestry. MongoDB Atlas: Streamlined gen AI development on Google Cloud MongoDB Atlas, the cloud-based deployment option for MongoDB, integrates seamlessly with Google Cloud. Atlas offers scalability and manageability, letting you focus on building groundbreaking gen AI applications. Here's how this powerful duo functions together: Data ingestion and storage: Effortlessly ingest your training data, regardless of format, into MongoDB Atlas on Google Cloud. This data can include text for natural language processing, code for programming tasks, or images for creative generation. AI model training: Leverage Google Cloud's AI services like Vertex AI to train your gen AI models using the data stored in MongoDB Atlas. Vertex AI provides pre-built algorithms and tools to streamline model development. Operationalization and serving: Once trained, deploy your gen AI model seamlessly within your application. MongoDB Atlas ensures the smooth data flow to and from your model, enabling real-time generation. Vector search with MongoDB Atlas: MongoDB Atlas Vector Search allows for efficient retrieval of similar data points within your gen AI dataset. This is crucial for tasks like image generation or recommendation systems. Advantages of this open approach By leveraging a microservices architecture, APIs, and the scalability and flexibility of Atlas, businesses can build agile and adaptable composable platforms. Atlas seamlessly integrates with Google Cloud, providing a streamlined environment for developing and deploying generative AI models. This integrated approach offers several benefits: Simplified development: The combined power of MongoDB Atlas and Google Cloud streamlines the development process, allowing you to focus on core gen AI functionalities. Scalability and flexibility: Both MongoDB Atlas and Google Cloud offer on-demand scalability, ensuring your infrastructure adapts to your gen AI application's growing needs. Faster time to market: The ease of integration and development offered by this combination helps you get your gen AI applications to market quickly. Cost-effectiveness: Both MongoDB Atlas and Google Cloud offer flexible pricing models, allowing you to optimize costs based on your specific gen AI project requirements. Digital decoupling, a legacy modernization approach With so much digital disruption, technology leaders are constantly being challenged. Existing legacy architectures and infrastructure can be extremely rigid and hard to unravel. Over 94% of senior leaders reported experiencing tech anxiety . So how do you manage this noise, meet the needs of the business, stay relevant, and evolve your technology so that you can deliver the kinds of experiences audiences expect? Digital decoupling is a legacy modernization approach that enables large, often well-established organizations to present a unified online experience to their users, take full advantage of their data, innovate safely, and compete effectively with digital natives. Technology evolves rapidly, and an effective microservices solution should be designed with future scalability and adaptability in mind. Kin + Carta helps to ensure that your solution is not only robust for current requirements but also capable of evolving with emerging technologies and business needs. It all starts with a clear modernization strategy that allows you to iteratively untangle from legacy systems, while also meeting the needs of business stakeholders seeking innovation. Navigating commerce complexity with Kin + Carta on Google Cloud Commerce is undergoing a significant transformation, and businesses need a future-proof technology foundation to handle the demands of complex models and massive datasets. That’s why Kin + Carta launched their Integrated Commerce Network , the first commerce-related solution that’s part of Google’s Industry Value Network . With the right tools and partners, your business can be at the forefront of innovation with generative AI, through automating tasks in revolutionary new ways, creating entirely new content formats, and delivering more personalized customer experiences. The complexities of commerce transformation can be daunting. But you can master the art of digital decoupling and leverage the strengths of the Integrated Commerce Network to unlock limitless possibilities and gain an edge over your competition. Check out Kin + Carta’s guide: Flipping the script — A new vision of legacy modernization enabled by digital decoupling . Get started with MongoDB Atlas on Google Cloud today.

April 9, 2024

Transforming Industries with MongoDB and AI: Retail

This is the third 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. With generative AI, retailers can create new products and offerings, define and implement upsell strategies, generate marketing materials based on market conditions, and enhance customer experiences. One of the most creative uses of gen AI help retailers understand customer needs and choices that change continually with seasons, trends, and socio-economic shifts. By analyzing customer data and behavior, gen AI can also create personalized product recommendations, customized marketing materials, and unique shopping experiences that are tailored to individual preferences. AI plays a critical role in decision-making at retail enterprises; product decisions such as design, pricing, demand forecasting, and distribution strategies require a complex understanding of a vast array of information from across the organization. To ensure that the right products in the right quantities are in the right place at the right time, back-office teams leverage machine learning arithmetic algorithms. As technology has advanced and the barrier to adopting AI has lowered, retailers are moving towards data-driven decision-making where AI is leveraged in real-time. generative AI is used to consolidate information and provide dramatic insights that could be immediately utilized across the enterprise. AI-augmented search and vector search Modern retail is a customer-centric business, and customers have more choice than ever in where they purchase a product. To retain and grow their customer base, retailers are working to offer compelling, personalized experiences to customers. To do this, it is necessary to capture a large amount of data on the customers themselves—like their buying patterns, interests, and interactions—and to quickly use that data to make complex decisions. One of the key interactions in an ecommerce experience is search. With full-text search engines, customers can easily find items that match their search, and retailers can rank those results in a way that will give the customer the best option. In previous iterations of personalization, decisions on how to rank search results in a personalized way were made by segmentation of customers through data acquisition from various operational systems, moving it all into a data warehouse, and then running machine learning algorithms on the data. Typically, this would run every 24 hours or a few days, in batches, so that the next time a customer logged in, they’d have a personalized experience. This did not, however, capture the customer intent in real-time, as intent evolves as the customer gathers more information. These days, modern retailers augment search ranking with data from real-time responses and analytics from AI algorithms. It's also now possible to incorporate factors like the current shopping cart/basket and customer clickstream or trending purchases across shoppers. The first step in truly understanding the customer is to build a customer data platform that combines data from disparate systems and silos across an organization: support, ecommerce transactions, in-store interactions, wish lists, reviews, and more. MongoDB’s flexible document model allows for the easy combination of data of different types and formats with the ability to embed sub-documents to get a clear view of the customer in one place. As the retailer captures more data points about the customer, they can easily add fields without the need for downtime in schema change. Next, the capability to run analytics in real-time rather than retroactively in another separate system is built. MongoDB’s architecture allows for workload isolation, meaning the operational workload (the customer's actions on the ecommerce site) and the analytical or AI workload (calculating what the next best offer should be) can be run simultaneously without interrupting the other. Then using MognoDB’s aggregation framework for advanced analytical queries or triggering an AI model in real time to give an answer that can be embedded into the search ranking in real time. Then comes the ability to easily update the search indexing to incorporate your AI augmentation. As MongoDB has Search built in, this whole flow can be completed in one data platform- as your data is being augmented with AI results, the search indexing will sync to match. MongoDB Atlas Vector Search brings the next generation of search capability. By using LLMs to create vector embeddings for each product and then turning on a vector index, retailers can offer semantic search to their customers. AI will calculate the complex similarities between items in vector space and give the customer a unique set of results matched to their true desire. Figure 1: The architecture of an AI-enhanced search engine explaining the different MongoDB Atlas components and Databricks notebooks and workflows used for data cleaning and preparation, product scoring, dynamic pricing, and vector search Figure 2: The architecture of a vector search solution showcasing how the data flows through the different integrated components of MongoDB Atlas and Databricks Demand forecasting and predictive analytics Retailers either develop homegrown applications for demand prediction using traditional machine learning models or buy specialized products designed to provide these insights across the segments for demand prediction and forecasting. The homegrown systems require significant infrastructure for data and machine learning implementation and dedicated technical expertise to develop, manage, and maintain them. More often than not, these systems require constant care to ensure optimal performance and provide value to the businesses. Generative AI already delivers several solutions for demand prediction for retailers by enhancing the accuracy and granularity of forecasts. The application of retrieval augmented generation utilizing large language models (LLMs) enables retailers to generate specific product demand and dig deeper to go to product categories and individual store levels. This not only streamlines distribution but also contributes to a more tailored fulfillment at a store level. The integration of gen AI in demand forecasting not only optimizes inventory management but also fosters a more dynamic and customer-centric approach in the retail industry. Generative AI can be used to enhance supply chain efficiency by accurately predicting demand for products, optimizing/coordinating with production schedules, and ensuring adequate inventory levels in warehouses or distribution centers. Data requirements for such endeavors include historical sales data, customer orders, and current multichannel sales data and trends. This information can be integrated with external datasets, such as weather patterns and events that could impact demand. This data must be consolidated in an operational data layer that is cleansed for obvious reasons of avoiding wrong predictions. Subsequently, feature engineering to extract seasonality, promotions impact, and general economic indicators. A retrieval augmented generation model can be incorporated to improve demand forecasting predictions and avoid hallucinations. The same datasets could be utilized from historical data to train and fine-tune the model for improved accuracy. Such efforts lead to the following business benefits: Precision in demand forecasting Optimized product and supply planning Efficiency improvement Enhanced customer satisfaction Across the retail industry, AI has captured the imaginations of executives and consumers alike. Whether you’re a customer of a grocer, ecommerce site, or retail conglomerate, AI has and will continue to transform and enhance how you do business with corporations. For the retailers that matter most globally, AI has created opportunities to minimize risk and fraud, perfect user experiences, and save companies from wasting labor and resources. From creation to launch, MongoDB Atlas guarantees that AI applications are cemented in accurate operational data and that they deliver the scalability, security, and performance demanded by developers and consumers alike. Learn more about AI use cases for top industries in our new ebook, Enhancing Retail Operations with AI and Vector Search: The Business Case for Adoption .

March 29, 2024

Better Inventory Management With MongoDB Atlas, Atlas Device Sync, and WeKan

Mobile technologies can be powerful enablers of an improved retail experience, both for customers and for employees. Using mobile devices, staffers can check in on an app rather than physically signing into their workplace. Order management and order tracking can be greatly improved with mobile technologies, offering customers increased assurance and predictability. One of the largest challenges faced by retailers — and one where mobile technologies are ideally suited to help — is inventory management. Poor inventory management leads directly to lost revenues, because retailers cannot fulfill orders if they don’t know what they have. Some 70% of store associates are unable to fulfill an order that is out of stock at their location. In this article, we’ll look at how mobile technology, connected to a central database, can improve inventory management and provide key functionality throughout an organization. Inventory challenges Inventory management is exceptionally complicated, especially for retailers that have roots in brick and mortar. Even a medium-size retailer may have dozens of physical locations and multiple warehouses. When an order comes in, the information must be sent to the appropriate warehouses, and the warehouse managers need to know which items should go to which stores. When e-commerce is layered on top of that, workers pick and pack items directly from the warehouse, in addition to managing returns. Worse, more than 16% of online orders are typically canceled . If a store manager orders a particular style of shoe, for example, they want to be able to see when those shoes are going to arrive. However, this information is typically compiled in batch mode at the end of the day, which can result in lags and inaccuracies. Additionally, if the various inventory systems aren’t communicating well, a store manager may not see that an order has been returned or canceled. They may think the item is out of stock — even though it could be sitting on a shelf. Such difficulties can be the result of fragmented technologies. Warehouses may have their own systems, point-of-sale systems may be a separate technology, and inventory management may be completely independent. This fragmentation doesn’t even begin to take into account the systems that help the store managers with operations and the delivery infrastructure. Some of these systems may be modern, some may be legacy, but without being connected to a central data layer, the information contained within them remains siloed and therefore of limited value. Mobile technology, connected to a central database, can unlock that information and provide important new functionality to employees throughout an organization. It can tell the shop manager that their shoes will arrive in the next hour. And it can tell them that, based on data from the point-of-sale system, they’re running out of women’s socks in size small, for example, and need to replenish their stock. Central data layer Conceptually, the solution is simple: A central data layer that is able to talk to all these different components on a real-time basis and then cascade the information to different applications using mobile devices to feed off of the database. The data layer and the local clients must be interoperable and available regardless of network availability and other challenges. The data required for the mobile app should be on-device and should be synced with the database server. The data layer is the backbone of this entire system. It needs to be flexible, because it will be asked to ingest wildly different forms of data. A retailer may have millions of SKUs that need to be managed, along with prices and special promotions. This information changes frequently, so the data layer must be able to manage a very high volume of data, and the data must be easily transformed to work in one central system that other devices can easily access. According to research from Incisiv, 70% of workers do not sit at a desk, and 79% of the retail workforce are digital natives, meaning that the data layer must be interoperable with any number and type of mobile devices. MongoDB and WeKan , working together, use MongoDB Atlas as that central data layer with Atlas Device Sync to connect data between mobile devices and the cloud. WeKan — an IT consulting company offering end-to-end transformation and technology innovation services — has been building mobile solutions since 2015, and has deep experience with Atlas and Atlas Device Sync. MongoDB has partnered with WeKan because of its consistent success in helping customers in industries such as retail, manufacturing, and automotive modernize their tech stacks and their business ecosystems, paving the way for valuable innovations. The combination of MongoDB Atlas and Atlas Device Sync provides significant advantages to joint customers, including: Out-of-the-box, robust sync protocol: When a sale is made, data from the point-of-sale system is synchronized to the cloud and then sent to all the other devices that need the information in real time. No matter how an employee is accessing the data, they can be assured that it is the most up-to-date. Efficient data consumption from the mobile client: It’s important that any solution be specifically tailored for mobile devices. A generic cloud-based solution is not necessarily mobile-friendly, which means that it may not be efficient in terms of data consumption, battery-life or storage optimization. Flexible schema: A flexible data structure allows businesses to ingest data quickly, innovate, and be free from the rigidity of relational systems. Warehouse teams may need one view of the data, while delivery teams need a completely different view. A flexible schema allows for fluid processes and helps each team get the data access it needs. GraphQL and auto-generation of endpoints: Endpoints talk to the data layer so that an API can fetch data from it. Most developers have to build those endpoints, but with Atlas Device Sync clusters, the endpoints are generated automatically. Triggers and functions: Certain aspects of MongoDB Atlas and Atlas Device Sync can be set up to be serverless. If you want to move data between Atlas and another system, you can use triggers to send the data and then send notifications based on the changes. Data access permissions and user authentication: This functionality is available out of the box with Atlas and Atlas Device Sync, which saves developers valuable time. Because MongoDB Atlas and Atlas Device Sync provide a fully managed, out-of-the-box implementation of device-to-cloud data synchronization, creating and maintaining a high-quality, reliable application fit for business-critical use cases is simple. Below, we illustrate how updates from client and server are handled automatically: Enabling e-commerce at a leading retail chain In one successful use case, a massively complex retail organization that operates, franchises, and licenses thousands of stores globally used MongoDB Atlas with Atlas Device Sync to build innovative functionality for its e-commerce application. The application allows customers to browse a product catalog connected to their local store’s inventory, make purchases on their mobile devices, and schedule in-store pickup or delivery. To provide the best customer experience, the mobile app obviously requires accurate, timely, and granular inventory data. As part of their technology modernization initiative, this retailer also brought custom mobile devices into 10,000 stores across the United States and Canada to better scan and manage inventory. With the new functionality, store employees can now call customers directly about order statuses and clarifications. The result is a better customer experience, increased mobile sales, and extensive data analytics capabilities that can foster further improvements. Inventory management is exceptionally complicated and frequently made worse with separate, fragmented technologies. Could improved inventory management bring in more revenue for your business? Learn more about WeKan or contact MongoDB at .

August 16, 2022

MACH Aligned for Retail: Microservices

MACH is an approach to architecting modern applications through open tech ecosystems. It is an acronym representing Microservices, API-first, Cloud-native SaaS, and Headless. With the accelerating digitalization of retail experiences requiring new technology stacks that provide agility, flexibility, and performance at scale, MACH is especially relevant for retail and ecommerce , a far cry from current legacy, monolithic architectures. The MACH Alliance is an organization, of which MongoDB is a member, dedicated to educating and driving the adoption of the MACH framework and to “future proof enterprise technology and propel current and future digital experiences.” This is the first of a series of blog posts dedicated to MACH and how retail organizations are leveraging this framework to gain a competitive advantage. Let us begin with the first letter of MACH: microservices. Read the next post in this series, "MACH Aligned for Retail: API-First." What are microservices and why should I care? In simplest terms, microservices are an approach to building applications in which business functions are broken down into smaller, self-contained components called services. These services function autonomously and are usually developed and deployed independently. This independence means the failure or outage of one microservice will not affect another. Each service serves a particular business function or objective. The benefits of a microservices-based architecture are clear. The modular approach of microservices provides companies with quicker time to market and value, ultimately leading to a better customer experience. Development teams can work independently on different app functionalities, consequently shortening development cycles to get more features deployed in less time, which means the reaction to changing customer demands improves dramatically. Also, since services are deployed in independent environments, scalability concerns are managed in a much more convenient (and efficient) way, and resilience is strengthened significantly because there is no single point of failure, as there would be with monolithic applications. Microservices provide a modern architecture for app development, which ultimately delivers the best experience for customers. Learn how Boots modernized its stack with MongoDB and Microservices . Applying microservices for retail What does a microservice-based application look like in a real-world scenario? Let’s say an ecommerce application is being built. Microservices will greatly optimize the following challenges: Dynamic product catalog: An ecommerce app might involve a large number of products (maybe from different suppliers) with changing availability. With each supplier and/or product category as a part of a microservice, it becomes easier and more efficient to manage and provide an always up-to-date product catalog for users. Changing customer needs: A microservice-based architecture increases speed of development and testing, ultimately allowing new features to be deployed faster and enabling developers to quickly pivot to new customer needs. Different teams can work in parallel and independently, with little to no dependencies, rolling out or rolling back features as needed without risk. Scale flexibly: Independently scale app functionalities up during peaks or down for valleys with on-demand cloud-based microservices. The world before microservices Before microservices were an option, the typical data infrastructure would look like a data access layer on top of a database in order to get all the datasets containing information needed for running the application, as seen in Figure 1. There would be many databases to pull data from and various information silos, making for a painful process. Business logic had to be generated to transform these datasets to perform specific functions, namely a product catalog, cart, checkout, payments, and the like. Before building any application, the relational data objects would need to be mapped out to match an object-oriented programming paradigm. Figure 1: The monolithic application diagram before microservices This is not easily scalable or flexible for modern applications because every change in a dataset needs to be pushed upstream, and every new feature request for the app implies a data schema change downstream. This complicates life for developers and makes adaptation to customer needs a nightmare. For a deeper look into technical details about microservices, check out MongoDB’s specific guides dedicated to this topic. Decoupled app functionality with microservices With microservices, business functions are decoupled as much as possible in order to create a bounded context that is clearly independent of the others, meaning a failure or outage in one does not affect the others. This often means having a separate database per service, as seen in Figure 2. Figure 2: A first approach to microservices In this first approach to microservices, decoupled application functionalities can be developed, maintained, and scaled independently. However, having a separate database for each business functionality is not the ideal. It adds operational complexity, defeating the purpose of a microservices approach since maintaining and scaling a distributed system is not a simple task. But there is light in all of this: a middle ground between strong decoupling and operational efficiency can be found with MongoDB. MongoDB and microservices MongoDB is built under a number of fundamental technology principles that ensure companies can reap the advantages of microservices, specifically around a flexible data model, redundancy, automation, and scalability. MongoDB can be deployed in any environment (on-premises or cloud for example), but all industries are moving or have already moved toward the cloud, with its lower cost of ownership and flexibility. Retail is no exception. The cloud is again the natural next step for microservices. It provides dynamic scalability and high availability, freeing teams of the operational burden of maintaining a distributed system in-house. This is why MongoDB clients are choosing MongoDB Atlas as their cloud database-as-a-service to deploy applications based on microservices. As a step to modernization , MongoDB can be used as an operational data store, as seen in Figure 3. Legacy data silos are needfully connected via change data capture (CDC) and/or ETL processes in order to have an up-to-date copy of the data, stored as JSON documents. This is a first major advantage, since now applications can be developed against a data model that fits how developers think and code, therefore providing unprecedented agility to the development cycle. Figure 3: Microservices with MongoDB, acting as an operational data Store. Applications can be developed taking advantage of its flexible data model and scalability MongoDB Atlas allows for all of the benefits and flexibility of a fully managed, API-driven database. It allows for environment automation without dealing with every detail of database operation and scalability. This makes development teams more agile so that they can evolve applications at the pace customers expect and require today. Read the next post in this series, "MACH Aligned for Retail: API-First."

April 28, 2022

How to Build the Right App For Your Mobile Workforce

The average turnover rate in the retail industry is slightly above 60%. This high turnover rate translates into more than 230 million days of lost productivity and $19 billion in costs associated with recruiting, hiring, and training, according to Human Resources Today . When surveyed by Harvard Business Review , 86% of the organizations polled said frontline workers need better technology-enabled insights to be able to make good decisions in the moment. The survey also pointed out that leading retailers are starting to consider the impact tech can have on productivity. Combined, the data points to a growing chorus of evidence that suggests a mobile workforce — where employees are empowered with the digital tools needed to not only provide a great customer experience but also make their own jobs easier — is less likely to feel burnout and be dissatisfied with their jobs. What a mobile workforce can do for your organization With an intuitive, modern app, you can accomplish key business objectives. Improve the customer buying experience: Frontline staff equipped with mobile-first technologies can better match the fluency of the customers. It enables them to serve the customer better by providing accurate, real-time information, such as what items are in stock, or make suggestions based on customer buying history. Increase employee productivity: According to Deloitte , workers spend as much as three hours each week looking for the information they need. Imagine the impact regaining those hours could have on worker productivity! Track and improve performance, sales, and buying experience through data analysis: The potential of workforce enablement apps extends beyond just identifying what items are in stock at which stores. They can also gather valuable data that can reveal key patterns in everything from customer purchase habits and target peak shopping times to individual worker metrics such as number of successful sales. With those data insights, you can better allocate workers, assign workers based on strengths, stock items based on buying trends, and more. Challenges when building a retail worker app An always-connected and innovative retail workforce enablement app sounds great, but building this kind of intuitive app from the ground up presents a lot of challenges for already strained IT teams. Many retailers still rely heavily on relational databases that require additional support from a sprawl of supporting databases and technologies. As shown in this typical retail tech stack, legacy architectures are often made up of specialist NoSQL and relational databases, and additional mobile data and analytics platforms — all resulting in siloed data, slow data processing, and unnecessary complexity. This “spaghetti” architecture has several drawbacks when it comes to building a mobile app that truly empowers developers. The data from all these systems ends up siloed, requiring time-consuming ETL maneuvers to bring it together into a single view. Real-time access to data and insights, required to know what’s out of stock, who made a purchase for pickup, and more becomes harder to orchestrate. It’s hard to ensure data synchronization between a worker’s app and the backend database when they’re moving in and out of connectivity (when workers walk to the back of a warehouse or stockroom, for instance). It’s even harder with a sprawling data architecture to account for. The added complexity managing multiple databases, analytics suites, and the connections between them slows down your development teams, burdening them with additional complexity and maintenance issues to manage. As a result, IT teams will spend more time managing data silos and supporting old systems and applications than enabling mobile platforms to support new applications and empower frontline staff. To learn more about these issues — and overcome them — read our latest whitepaper, Why It’s So Hard for Retailers to Build a Workforce Enablement App (and How to Do It Right) .

March 10, 2022

Retail Tech in 2022: Predictions for What's on the Horizon

If 2020 and 2021 were all about adjusting to the Covid-19 pandemic, 2022 will be about finding a way to be successful in this “new normal”. So what should retailers expect in the upcoming year, and where should you consider making new retail technology investments? Omnichannel is still going strong Who would have anticipated the Covid-19 pandemic would still be disrupting lives after two years? For the retail industry this means more of the same - omnichannel shopping. Despite the hope many of us had for the end of the pandemic and the gradual increase of in-person shopping, retail workers can expect to continue accommodating all kinds of shopping experiences – online shopping, brick and mortar shopping, buy online and pick up in store, reserve online and pick up in store. Even beyond the pandemic, the face of shopping is likely forever changed. This means retailers need to start considering the long-term tech investments required to meet transforming customer expectations. Adopting solutions that offer a single view of the consumer gives you the unique opportunity to personalize offerings, products and loyalty programs to their demand. With a superior consumer experience, you can achieve repeat business and increased customer loyalty. While many retailers may have thought they could “get by” with their current solutions until the pandemic ends, it’s time to rethink that approach and start exploring more long-term solutions to improve omnichannel shopping experiences. Leaner tech stacks over many specialized solutions In 2022, you should explore solutions that allow your IT teams to do more with less. The typical retail tech stack looks something like the diagram below. Legacy, relational databases are supplemented by other specialist NoSQL and relational databases, and additional mobile data and analytics platforms. As a result, retailers looking to respond quickly to changing consumer preferences and improve the customer experience face an uphill battle against siloed data, slow data processing, and unnecessary complexity. Your development teams are so busy cobbling solutions together and maintaining different technologies at once that they fail to innovate to their full potential, so you’re never quite able to pull ahead of the competition. This is the data innovation recurring tax (or DIRT) . Think of this as the ongoing tax on innovation that spaghetti architectures, like the example above, legacy architecture costs your business. As technology grows more sophisticated and data grows more complex, companies are expected to react almost instantaneously to signals from their data. Legacy technologies, like relational databases, are rigid, inefficient, and hard to adapt, making it difficult to deliver true innovation to your customers and employees in a timely manner. Your development teams are so busy cobbling solutions together that they fail to innovate to their full potential, so you’re never quite able to pull ahead of the competition. It’s time to rethink your legacy systems, and adopt solutions that streamline operations and seamlessly share data to ensure you’re working with a single source of data truth. Many retailers recognize the need to upgrade legacy solutions and get away from multiple different database technologies, but you may not know where to start. Look for modern data applications that simplify data collection from disparate sources and include automated conflict resolution for added data reliability. Also, consider what you could do with fully managed data platforms, like MongoDB Atlas . With someone else doing the admin work, your developers are free to focus on critical work or turn their talents to innovation. Digital worker enablement will increase retention For employees, 2022 looks set to continue last year’s trend of the “ Great Resignation ”. To combat worker fatigue, and retain your workforce you need to prioritize worker engagement. One way to better engage your employees is through mobile workforce enablement. While many companies consider how to engage their customers with a more digital-friendly work environment, you shouldn’t forget about your workers in the process. Global companies like Walmart are starting to invest in mobile apps to enable their workforce. A modern, always-on retail workforce enablement app could transform the way your employees do their jobs. Features like real-time view of stock, cross-departmental collaboration, detailed product information, instant communication with other stores can simplify your workers’ experiences and help them to better serve your customers. Your workers need an always-on app that syncs with your single source of data truth, regardless of connectivity (which may be an issue as retail workers are constantly on the move). But building a mobile app with data sync capabilities can be a costly and time-intensive investment. MongoDB Realm Sync solves for this with an intuitive, object-oriented data model that is simple to use, and an out-of-the-box data synchronization service. When your mobile data seamlessly integrates with back-end systems, you can deliver a modern, distributed data platform to your workers. Huge investment in the supply chain From microchips to toilet paper, disruptions in the supply chain were a huge issue in 2020 and 2021, and the supply chain pain continues in 2022. And while there continue to be supply chain issues beyond the control of retailers, there are steps that can be taken to mitigate some of the pain and prepare for future disruptions. Warehouse tech is getting smarter, and you need to upgrade your solutions to keep up. For starters, consider adopting the right data platform to unify siloed data and gain a single view of operations . A single view of your data will allow for better management of store-level demand forecasts, distribution center-to-store network optimizations, vendor ordering, truck load optimizations, and much more. With a modern data platform, all this data feeds into one, single view application, giving retailers the insights to react to supply chain issues in real time. With disruption set to dominate 2022, as it did in 2020 and 2021, investing in proactive solution upgrades could help your business not only survive, but thrive. Want to learn more about gaining a competitive advantage in the retail industry? Get this free white paper on retail modernization .

January 13, 2022

What is MACH Architecture for ecommerce?

In the past, retailers faced the looming battle of brick and mortar vs. digital buying experiences. While most in the retail industry accepted the inevitability of needing some kind of digital experience, COVID-19 forced retailers to refocus efforts to digital-first, or at the very least, hybrid digital and in-person buying options. What customers expect (and why legacy systems don't hold up) Which leads us to one of the underlying problems for modern retailers: legacy architecture. The digital solutions many depend on aren’t able to meet consumers’ digital-first (or at the very least digital-friendly) ecommerce expectations. Today’s customers expect: Mobile-friendly architecture - People shop from their phones. If your ecommerce experience was designed with web-first in mind, only retrofitting a mobile component to meet buyer demand, you may need to rethink your mobile offering. Omnichannel experience - Beyond having a mobile-friendly buying experience, consumers want to carry their purchasing power from channel to channel and even into the physical store. Think buying online and picking up in store (BOPIS), or starting an order from your phone and completing it in store, or vice versa. Dynamic product catalogues - Consumers want ample choice and a smooth search experience. Can your systems hold up with thousands of products all displayed, searchable, managed, updated, and dynamically enriched with discounts, product offerings, and more? They also expect real-time stock availability, both in store and online. They want to know you really have an item in stock at their local store before venturing out to buy it. Personalization - Personalization is so ingrained in the online retail experience now that consumers have come to expect it. They want real-time recommendations for the items they’re interested in, with predictions based on past online purchases and searches, items in their cart, and in-person buying experiences. Why is it difficult to live up to these expectations? For many in ecommerce, they’re still running monolithic applications built as a single, autonomous unit. This means even the smallest changes, like altering a single line of code or adding a new feature, could require refactoring the entire software stack, leading to downtime and lost business. In addition, the long-term opportunity cost of having your development team waste time simply maintaining and patching such a brittle ecommerce system is a constant drain, or Innovation Tax , on your business. So retailers face a unique challenge. The thought of overhauling their current systems lead to fears like downtime, expensive investments in new solutions, and ultimately, massive loss of profit. But providing an e-commerce experience that lives up to consumer expectations isn’t optional anymore; it’s how your business thrives. That’s where the MACH Approach comes in. MACH Approach: ecommerce modernization with flexibility in mind So, what’s the MACH approach and, to put it bluntly, why should the retail industry care? The MACH approach, championed by the MACH Alliance , an industry body of which MongoDB is a member, is focused on facilitating the transition from monolithic, legacy ecommerce architectures to modern, streamlined e-commerce applications. Microservices - Microservices break down specific business functionalities into smaller, self-contained services. Instead of taking your whole application offline to add new shopping cart features, you update specific elements of your architecture without disrupting the entire application. This affords developers a level of flexibility that monolithic systems can’t compete with. Greater developer flexibility means minimal downtime, faster updates, an improved experience for consumers, and ultimately faster time to value for your business. API-first - APIs, the pieces of code allowing communication between separate applications or microservices, should be at the forefront of solution development, instead of an afterthought. An API-first approach to development is just that — APIs are built first and all other actions are developed to preserve the original API for greater consistency and reusability. This approach ensures planning revolves around the end product being consumed by different devices (like mobile) and APIs will be consumed by client applications. Cloud-native - At this point, to say “the cloud is the future of app development” is cliche; we’re already there. Building and running applications exclusively in the cloud, whether public or private, allows you to reap all the benefits of cloud development from the start. There are also some cost-cutting benefits to cloud-native environments. You avoid the investment that often comes with on-prem equipment. Most cloud SaaS options have pay-as-you-go cost structures, ensuring you only pay for what you use and leading to most predictable monthly expenses. Using managed cloud solutions, like MongoDB Atlas , also frees up your development team to focus their efforts on where they’re needed most — actually developing your application — instead of sinking valuable time into burdensome administrative tasks. Headless - If your application is down, even for a minute, you run the risk of the consumer simply moving on to another retail option. Downtime equates to lost profits, so to avoid the dreaded disruption to your revenue stream, take a headless approach to application development. With headless, changes to the front end (web store layout, UX, frameworks, design, etc.) can be made without interruption to back end (products, business logic, payments , etc.) operations and vice versa. What's the upside for ecommerce? The four elements of the MACH approach come together to help ecommerce businesses reframe operations, avoid downtime, preserve revenue, provide the best user experience possible, and ultimately ensure your solutions are able to develop and evolve. To maintain a competitive advantage in a growingly competitive commerce market, your application needs to keep up. The MACH approach to ecommerce could be the ideal way to set your application and your business apart. Want to learn more about the MACH Approach and the role cloud-native database solutions like MongoDB Atlas play in the evolving world of digital retail? Get your free copy of Ecommerce at MACH Speed with MongoDB and Commercetools today.

November 30, 2021