Ignite Launches Smart Spend Management Solution on MongoDB

Expenditures are under a microscope in today's macroeconomic environment. Organizations everywhere are trying to do more with less and stretch their budgets further. But managing spend can be an uphill battle when data is siloed across the organization. Ignite Procurement's spend management solution helps organizations unify procurement data across the enterprise, making procurement easier, better, and more efficient.

Building a smarter, scalable stack

Ignite CTO Valdemar Rolfsen says the company uses MongoDB Atlas because it gives them the flexibility they need to serve customers. Customer data can be structured or unstructured, and Atlas handles both seamlessly. He also says the rapid growth the company experienced in its early stages necessitated a database with more scalability than PostgreSQL could offer. MongoDB Atlas easily scales up or down, giving Ignite exactly what it needs to handle current workloads and the future growth the company anticipates.

Rolfsen says that while the company doesn't run as many writes and reads as in other use cases, it does run a lot of advanced operations in its in-house analytics platform, and MongoDB handles those needs easily and efficiently. He also praises the professional support he's received from the MongoDB for Startups program.

Photo of the Ignite team

Ignite uses a mix of Atlas dedicated clusters and serverless instances, which has been generally available (GA) since June. Rolfsen says the work they're doing at Ignite fits very well within the serverless model. The Ignite platform is a mid-market and enterprise application that's used by companies in the U.S. and Europe. The platform sees heavy usage at certain times and very low usage at other times. With serverless, Rolfsen says he doesn't have to worry about the time it takes for an idle server to ramp up. When resources are in high demand, the serverless database automatically scales up to meet the demand and back down as demand subsides. With serverless pricing, you pay for what you use and not for idle resources. The transparency of the pricing model is something Rolfsen says is especially attractive in the serverless offering. Ignite is also benefiting from credits the company received as part of the MongoDB for Startups program.

Rolfsen's affinity for the serverless offering goes beyond the pricing benefits of serverless. He recognizes the impact on CO2 emissions associated with large virtual machines operating continuously. Serverless technology, he says, is a much better alternative for the environment and sustainability. Ignite's cloud usage accounts for 25% of the company's CO2 emissions, according to Rolfsen. Getting that down is, "really cool to see," he says.

In addition to MongoDB Atlas serverless instances, Ignite also runs a combination of Google Cloud Run, Cloud Functions, and Kubernetes clusters. As the company expands its services, it's starting to move more to microservices and cloud functions to limit the scope of the domain teams have to work in.

How to find out more

The MongoDB for Startups program helps startups along the way with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Learn more about the benefits of the MongoDB for Startup program and even sign up today.

For more startup content, check out our wrap-up of the 2022 year in startups.

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Real-Time ESG Data Management

ESG (Environmental, Social, and Governance) data collection and reporting has become a corporate priority, with over 96% of S&P 500 companies publishing sustainability reports in 2021, according to research from the Governance and Accountability Institute. There are several factors driving the adoption and use of ESG data; ranging from consumer preference for companies with positive ESG information, to employees, who increasingly believe environmental, social, and governance metrics are important indicators when choosing an employer. Many government bodies and regulators either have, or are considering, mandatory ESG data collection and ESG data reporting requirements for corporations under their jurisdiction. The European Union is taking the lead here , with several key pieces of legislation either already enacted, or coming soon. In the US, the SEC has also announced proposed rule changes for securities reporting, mandating companies make detailed climate-related disclosures in their filings. In addition to companies that report on their own data, financial firms, including the private equity industry, use ESG data and research to weigh risks and identify opportunities for the companies they invest in. Faced with growing scrutiny around ESG reporting and scoring, companies are struggling to meet ever more detailed and comprehensive reporting requirements. At the heart of the problem is the sheer volume and variety of data companies are expected to ingest and analyze to produce the scores that investors, consumers, and government entities demand. And with real-time data making its way into reports, ESG data management is becoming even harder. ESG data collection and analysis The volume and variety of ESG data makes collection and analysis difficult. The data collection problem can be broken down as follows: Variety Unlike financial datasets, which are mostly numerical, ESG metrics can include both structured and unstructured datasets, like an email or a media report. If a company wants to analyze satellite data to derive their own climate dataset, they may even need to analyze images and videos. Given these variables, companies need to employ a data model that can support many different types of data . Velocity As companies increasingly integrate real-time data sources into their ESG scoring systems, the velocity of data collected and analyzed increases exponentially. One example is loan due diligence in the financial sector. As customers demand faster loan approval turnaround times, financial institutions that currently rely on quarterly ESG data to make those decisions now need the information in real-time to instantly approve loans in an ESG compliant manner. Volume The increased variety of data sources, coupled with the growing velocity of data being collected leads to an increase in the sheer volume of data requiring analysis. Currently, ESG ratings and scores are derived from a blend of human judgment and model driven quantitative rating. But as the volume of data increases, along with the need for instant analysis of that data, real-time analytics and an increased use of AI/ML tools will become an ever greater part of ESG ratings and reporting. On top of this, there are also no universally applicable ESG standards, leaving companies having to deal with multiple different standards, with different data requirements, depending on which jurisdictions they operate in. Real-time ESG data analytics Companies are increasingly incorporating real-time data into their ESG analysis, reporting, and scoring. Harnessing technologies such as cloud computing, AI, and machine learning, those that utilize real-time data can, for instance, instantly parse breaking news stories for ESG-related data on their investments, or incorporate up-to-the-minute satellite data into reports on a firm’s environmental impact. The financial services industry in particular is taking a lead on integrating real-time ESG data into investment decisions. Asset and fund managers use real-time data platforms that allow them to calculate up-to-date ESG scores to aid investment decisions and risk calculations. For example, a bank looking to invest in an electric vehicle company would be alerted to a breaking news story about a hazardous accident at the manufacturer’s battery plant, with follow up data from social media or analyst reports quantifying the size of the public reaction and the level of negative market sentiment around the accident. MongoDB and ESG data management MongoDB Atlas is an ideal data foundation for ESG platforms. MongoDB Atlas uses the document data model, giving users the ability to ingest data from almost any source, consolidate data from a number of siloed data sets, enable the easy search of that data, and with a few clicks, create customized views of the data without the need for additional ETL operations to other databases or tools. MongoDB Atlas also future-proofs your ESG data platform with a flexible data schema that can easily adapt to rapidly changing ESG requirements and standards. See why Hydrus chose MongoDB Atlas as the basis for its ESG reporting platform. FAQ ESG data definition ESG (Environment, Social, and Governance) data comes from a growing list of sources, all of which help “score” a corporation based on how well positioned it is to handle the risks and opportunities presented by the environment, societal stakeholders, and corporate governance. Environment - What are a company's greenhouse gas emissions? How about its stewardship over natural resources? And how well positioned is it to weather physical climate risks, like global warming, flooding, drought, fire etc. Social - How does a company measure up against prevailing fair wage and employee engagement metrics? What impact does a company have on the communities where it operates? Governance - How well is a company managed? How responsive is a company to shareholders? How accountable is leadership? What safeguards are in place to ensure transparency? The growing interest around ESG data science and data analytics has prompted the rise of a new industry of ESG data companies and ESG data management software vendors. What are the different ESG data sources? ESG data may come from two primary sources; 'inside-out' and 'outside-in'. Inside-out data is supplied by companies, used for analysis, and usually lags 6-12 months due to annual ESG-related disclosures. Outside-in data is more regularly updated, sometimes even in real time. Most financial institutions, including banks who often have access to a lot of financial and company data from their customers, do not rely solely on their own data. ESG data analysis requires a broad range of inputs and data that the bank does not possess or can obtain even from their customers. For example, a bank may want to assess the risk of flooding for a chip manufacturing company that has factories in several provinces in China. The bank would need to collect the flood data from the different operating locations in order to score the risk. As banks don’t typically collect flood data themselves, the bank would purchase data from third-party climate data vendors. At this nascent stage of climate risk assessment within the banking industry, it is likely that the bank would not even attempt to collect the raw climate data and create the risk models to score the risk, relying instead on third-party risk scoring vendors. The bank would then make use of these scores and combine in models which they have strong competencies eg. credit risk to come up with flood risk-adjusted credit risk scores for loan approvals. Why is ESG data essential for investors? ESG data is used by asset managers and investors for market analysis, supporting asset allocation and risk management, and in providing insights into the long-term sustainability of investments in various corporations.

February 14, 2023
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The MongoDB AI Applications Program: Delivering Customer Value

When people ask me about MongoDB, I tell them that they’ve probably interacted with MongoDB without realizing it. In fact, many of the world’s leading companies—including 70% of the Fortune 100—are powered by MongoDB. Everything we do at MongoDB is about serving our customers, but that often happens in the background, where our work is invisible to many users. In my case, that means building an ecosystem of partners who enable customer innovation. A recent example is how MongoDB teamed up with Amazon Web Services (AWS) and Amazon Bedrock to help Base39 —a Brazilian fintech provider—automate loan analysis, decreasing decision time from three days to one hour, and reducing cost per loan analysis by 96%. And there’s the Indian company IndiaDataHub, which joined the MongoDB AI Applications Program (MAAP) to access AI expertise, in-depth support, and a full spectrum of technologies to enhance AI functionality within IndiaDataHub’s analytics platform. This includes connecting relevant data in MongoDB with Meta's AI models to perform sentiment analysis on text datasets. I could go on and on—after all, tens of thousands of MongoDB’s customers have success stories like these. Enabling customer success is precisely why we launched MAAP last summer, and why the program has evolved since. Customers tell us that they want to take advantage of AI, but they’re unsure how to navigate a fast-moving market, how to control costs, and how to unlock business value from their AI investments. So with MAAP, MongoDB offers customers a full AI stack and an integrated set of professional services to help them keep pace with the latest innovations, identify the best AI use cases, and to help them future-proof AI investments. With today’s announcement , Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured have joined the 22 companies that now comprise the MAAP partner network. Which means that the MAAP ecosystem (which was founded with Accenture, Anthropic, Anyscale, Arcee AI, AWS, Cohere, Credal, Fireworks AI, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI) offers additional cutting-edge AI integration and solutions to customers—and more ways to set them on the path to AI success. CentralReach: Making an impact on autism with AI More than 150 customers have already gotten involved with MAAP, but I’m particularly excited to share the work of CentralReach . CentralReach provides an AI-powered electronic medical record (EMR) platform that is designed to improve outcomes for children and adults diagnosed with autism and related intellectual and developmental disabilities (IDD). Prior to working with MongoDB and MAAP, CentralReach was looking for an experienced partner to further connect and aggregate its more than 4 billion financial and clinical data points across its suite of solutions. CentralReach leveraged MongoDB’s document model to aggregate the company’s diverse forms of information from assessments to clinical data collection, so the company could build rich AI-assisted solutions on top of its database. Meanwhile, MAAP partners helped CentralReach to design and optimize multiple layers of its comprehensive buildout. All of this will enable CentralReach to support initiatives such as value-based outcome measurement, clinical supervision, and care delivery efficacy. With these new data layers in place, providers will be able to make substantial improvements to their clinical delivery to optimize care for all those they serve. “As a mission-driven organization, CentralReach is always looking to innovate on behalf of the clinical professionals—and the more than 350,000 autism and IDD learners—that we serve globally,” said Chris Sullens, CEO of CentralReach. “So being able to lean on MongoDBs database technology and draw on the collective expertise of the MAAP partner network—in addition to MongoDB’s tech expertise and services—to help us improve outcomes for our customers and their clients worldwide has been invaluable.” Working backward from customer needs The addition of Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured to the MAAP partner network offers customers additional technology and AI support options. It also builds on MongoDB’s larger partner ecosystem , which is designed to give customers flexibility and choice. By working closely with our partners on product launches, integrations, and real-world challenges, MongoDB has been able to bring a better understanding of the challenges facing customers—and to give them the resources and confidence to move forward with groundbreaking technology like AI . Examples of support MAAP has offered customers include: Guidance on chunking strategies for an AI-native healthcare provider providing patient recommendations based on complex data sources Collaboration on advanced retrieval techniques to improve response accuracies for a large consultancy to automate manual research Evaluation of embedding models for multi-modal data stores for a well-known automaker developing diagnostic applications Guidance on architectures for complex agentic workflows for a mature enterprise technology provider augmenting customer service workflows One way we offer this support is through the MAAP Center of Excellence (CoE). The MAAP CoE comprises AI technical experts from across MongoDB and the MAAP partner ecosystem who collaborate with customers to understand their challenges, technical requirements, and timelines. The MAAP CoE can then recommend custom full-stack architectures and implementation best practices, optimized for the customer’s specific use case and requirements. Indeed, customization is intrinsic to MAAP: MongoDB and our MAAP partners will meet customers wherever they are to help them achieve their goals. For example, if an organization wants to fully own its AI application development, MongoDB and partners can provide guidance and expertise. And in cases where customers want hands-on support, we can help speed projects with professional services. Ultimately, we want MAAP customers—and anyone who works with MongoDB’s partner ecosystem at large—to feel empowered to own their application development, and to transform challenges into opportunities. Let’s build the next big thing together! To learn more about building AI-powered apps with MongoDB, see MongoDB’s AI Resources Hub , the Partner Ecosystem Catalog , or visit the MAAP page . And check out our partner Confluent’s own blog post about MAAP!

December 2, 2024