Hydrus Helps Companies Improve ESG Performance

Steve Jurczak
January 18, 2023 | Updated: January 26, 2023
#BuiltWithMongoDB#Startups

More organizations are embracing workforce diversity, environmental sustainability, and responsible corporate governance in an effort to improve their Environmental, Social, and Governance (ESG) performance. As investors increasingly favor ESG in their portfolios, organizations are under greater pressure to capture, store, and verify ESG metrics. San Francisco-based startup, Hydrus, is helping companies make ESG data more usable and actionable.

The platform

Hydrus, a MongoDB for Startups program member, is a software platform that enables enterprises to collect, store, report, and act on their environmental, social, and governance data. ESG data includes things like:

  • How a company safeguards the environment
  • Its energy consumption and how it impacts climate change
  • How it manages relationships with employees, suppliers, and customers
  • Details about the company’s leadership, executive pay, audits, and internal controls

The Hydrus platform enables organizations to collect, store, and audit diversity and environmental data, and run analytics and machine learning against that data.

Hydrus offers users a first-rate UI/UX so that even non-technical users can leverage the platform. With the auditing capabilities, organizations can ensure the provenance and integrity of ESG data over time. Other solutions don't allow users to go back in time and determine who made changes to the data, why they made them, what earlier versions of the data looked like, and what time the changes were made. Hydrus gives users complete visibility into these activities.

The tech stack

MongoDB Atlas was the preferred database for Hydrus because of the flexibility of the data model. George Lee, founder and CEO of Hydrus, says the traditional SQL database model was too limiting for the startup's needs. MongoDB's document model eliminated the need to create tables or enforce restrictions of data fields. With MongoDB, they could simply add fields without undertaking any major schema changes.

Hydrus also tapped MongoDB for access to engineers and technical resources. This enabled the company to architect its platform for all of the different types of sustainability data that exist. MongoDB technical experts helped Hydrus model data for future scalability and flexibility so it could add data fields when the need arises.

On top of Atlas and MongoDB technical support, Hydrus leans heavily on MongoDB Charts, a data visualization tool for creating, sharing, and embedding visualizations from MongoDB Atlas. Charts enables Hydrus to derive insights from ESG data, giving its Fortune 200 clients better visibility into their operational efficiency. Charts uses a drag-and-drop interface that makes it easy to build charts and answer questions about ESG data.

A Hydrus customer using MongoDB Charts was better able to understand the impact of their footprint from a greenhouse gas perspective and a resource usage perspective. Another customer detected a 30x increase in refrigerant usage in one of its facilities. The visual analytics generated with MongoDB Charts enabled the company to make changes to improve their ESG performance.

MongoDB Charts enabled Hydrus to visualize sustainability data

"MongoDB Charts enables our customers to directly report their sustainability data, customize the charts, and better tell the sustainability story in a visual format," Lee says. "It's way better than the traditional format where you have data, tables, and spreadsheets everywhere."

The roadmap

Hydrus seeks to take the hassle out of managing a sustainable business by streamlining data collection, reporting, and auditing processes. Its platform is designed to eliminate manual tasks for sustainability managers so they can focus on decarbonization, resource usage optimization, and being able to hit their sustainability goals. Hydrus accelerates these activities by helping companies model their sustainability data around science-based targets so they can better decarbonize and meet other ESG goals.

If you're interested in learning more about how to help your organization become more sustainable, decarbonize, and succeed in your sustainability journey, visit the Hydrus website.

Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now.

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

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Predictions 2023: Modernization Efforts in the Financial Services Industry

As a global recession looms, banks are facing tough economic conditions in 2023. Lowering costs will be vital for many organizations to remain competitive in a data-intensive and highly regulated environment. Thus, it’s important that any IT investments accelerate digital transformation with innovative technologies that break down data silos, increase operational efficiency, and build personalized customer experiences. Read on to learn about areas in which banks are looking to modernize in 2023 to build better customer experiences at a lower cost and at scale. Shaping a better banking future with composable designs With banks eager to modernize and innovate, institutions must move away from the legacy systems that are restricting their ability to show progress. Placing consumers at the center of a banking experience made up of interconnected, yet independent services offers technology-forward banks the chance to reshape their business models and subsequently grow market share and increase profitability. These opportunities have brought to fruition a composable architecture design allows faster innovation, improved operational efficiency, and creates new revenue streams by extending the portfolio of services and products. Thus, banks are able to adopt the best-of-breed and perfect-fit-for-purpose software available by orchestrating strategic partnerships with relevant fintechs and software providers. This new breed of suppliers can provide everything from know your customer (KYC) services to integrated booking, load services or basic marketing and portfolio management functionalities. This approach is more cost efficient for institutions than having to build and maintain the infrastructure themselves, and it is significantly faster in terms of time to market and time to revenue. Banks adopting such an approach are seeing fintechs less as competitors and more as part of an ecosystem to collaborate with to accelerate innovation and reach customers. Operational efficiency with intelligent automation Financial institutions will continue to focus on operational efficiency and cost control through automating previous manual and paper-driven processes. Banks have made some progress digitizing and automating what were once almost exclusively paper-based, manual processes. But, the primary driver of this transformation has been compliance with local regulations rather than an overarching strategy for really getting to know the client and achieving true customer delight. The market is eager for better automated and data-driven decisions, and legacy systems can’t keep up. Creating hyper-personalized experiences that customers demand, which include things like chatbots, self-service portals, and digital forensics, is difficult for institutions using outdated technology. And, having data infrastructure in siloes prohibits any truly integrated modern experience. Using a combination of robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI), financial institutions are able to streamline processes, thereby freeing the workforce to focus on tasks that drive a bigger impact for the customer and business. Institutions must not digitize without considering the human interaction that will be replaced, as customers prefer a hybrid approach. The ability to act on real-time data is the way forward for driving value and transforming customer experiences, which must be accompanied by the modernization of the underlying data architecture. The prerequisite for this goal involves the de-siloing of data and sources into a holistic data landscape. Some people call it a data mesh , some composable data sources, virtualized data. Solving ESG data challenges Along with high inflation, the cost-of-living crisis, energy turmoil, and rising interest rates, environmental, social, and governance (ESG) is also in the spotlight. There is growing pressure from regulators to provide ESG data and from investors to make sure portfolios are sustainable. The role of ESG data in conducting market analysis, supporting asset allocation and risk management, and providing insights into the long-term sustainability of investments continues to expand. The nature and variability of many ESG metrics is a major challenge facing companies today. Unlike financial datasets that are mostly numerical, ESG metrics can include both quantitative and qualitative data to help investors and other stakeholders understand a company’s actions and intentions. This complexity, coupled with the lack of a universally applicable ESG reporting standard, means institutions must consider different standards with different data requirements. To master ESG reporting, including the integration of relevant KPIs, appropriate, high-quality data is needed that is also at the right level of granularity and covers the required industries and region. Given the data volume and complexity, financial institutions are building ESG platforms underpinned by modern data platforms that are capable of consolidating different types of data from various providers, creating customized views, modeling data, and performing operations with no barriers. Digital payments - Unlocking an enriched experience Pushed by new technologies and global trends, the digital payments market is flourishing globally. With a valuation of more than $68 billion in 2021 and expectations of double-digit growth over the next decade, emerging markets are leading the way in terms of relative expansion. This growth has been driven by pandemic-induced cashless payments, e-commerce, government push, and fintechs. Digital payments are transforming the payments experience. While it was once enough for payment service providers to supply account information and orchestrate simple transactions, consumers now expect an enriched experience where each transaction offers new insights and value-added services. Meeting these expectations is difficult, especially for companies that rely on outdated technologies that were created long before transactions were carried out with a few taps on a mobile device. To meet the needs of customers, financial institutions are modernizing their payments data infrastructure to create personalized, secure, and real-time payment experiences — all while protecting consumers from fraud. This modernization allows financial institutions to ingest any type of data, launch services more quickly at a lower cost, and have the freedom to run in any environment, from on-premises to multi-cloud . Security and risk management Data is critical to every financial institution; it is recognized as a core asset to drive customer growth and innovation. As the need to leverage data efficiently increases, however, according to 57% of decision makers , the legacy technology that still underpins many organizations is too expensive and doesn’t fulfill the requirements of modern applications. Not only is this legacy infrastructure complex, it is unable to meet current security requirements. Given the huge amount of confidential client and customer data that the financial services industry deals with on a daily basis — and the strict regulations surrounding that data — security must be of highest priority. The perceived value of this data also makes financial services organizations a primary target for data breaches. Fraud protection, risk management, and anti-money laundering are high priorities for any new data platform according to Forrester’s What’s Driving Next-Generation Data Platform Adoption in Financial Services study. To meet these challenges, adoption of next-generation data platforms will continue to grow as financial institutions realize their full potential to manage costs, maximize security, and foster innovation. Download Forrester’s full study — What’s Driving Next-Generation Data Platform Adoption in Financial Services — to learn more.

January 17, 2023
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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024