Impactive Makes ESG Integration and Stewardship Reporting Easier for Investment Firms

Steve Jurczak
July 6, 2023 | Updated: July 7, 2023
#Startups

Driven by regulations and high demand from asset owners, environmental, social and corporate governance (ESG) performance is now a critical factor in almost every aspect of asset management. But the reporting and management of complex ESG activities is challenging to implement because of the rigidity of legacy data architectures. Investment managers need to be able to seamlessly integrate ESG data, actively engage with portfolio companies on material sustainability issues, and deliver comprehensive reporting and disclosures with increasing accuracy. Legacy silos prevent this from happening. Without a process for managing and sharing data between stakeholders, efficiency is compromised and transparency becomes elusive.

Photo of Mais Callan, Co-founder and CEO of Impactive

UK-based start-up Impactive has just started on their journey to make responsible investing easier by providing a dedicated workspace in which investment and ESG analysts can record their ESG interactions and engagements with companies, and centralize ESG and proxy voting data. This helps break down data silos and democratize data within investment firms, making it possible for different stakeholders, including reporting and client-facing teams, to self-serve to obtain the information they need. Co-founder and CEO Mais Callan started Impactive in 2022 after experiencing these frustrations first-hand in her day-to-day work in the investment industry.

Mais Callan started her career in asset management, just before the United Nations-backed Principles for Responsible Investment (PRI) were launched in 2006, establishing a framework for incorporating ESG factors into investment practices. Hermes Investment Management, the firm where Mais Callan started her career, was a founding signatory of the PRI and a pioneer in responsible investment and thinking about ESG.

The financial crisis in 2008 highlighted the inadequacies in corporate governance and catalyzed the investment industry’s adoption of responsible investment practices. After a decade at Hermes, and having developed its ESG integration strategy, she set up a consultancy in which she helped other asset managers with their ESG frameworks.

She later worked at a major UK pension fund focusing on investment stewardship. In those roles, she noticed a recurring theme: There wasn't a technology solution or process to help facilitate the aggregation, integration, processing, and reporting of ESG activities. At the time, her husband, Impactive Co-founder and CTO Simon Callan, was Head of Product Architecture in the financial services industry. So, he built a prototype that she could put in front of her investment network. That's how Impactive was born.

How financial data silos form

One of the barriers to sharing data for ESG reporting has been the reluctance of financial services firms to move away from legacy architectures, which have a tendency to become siloed over time. In his experience as a software developer and architect in financial services, Simon Callan observed that as systems evolved, new needs arose, which led to the procurement of new platforms to address these always-changing needs. As these systems proliferated, a desire to integrate all of them emerged.

"Data silos are a pervasive issue in mature organizations. As the technology landscape evolves, there's a lack of a coherent view across different systems,” Simon Callan says. "This is particularly prevalent in financial services where there are strong controls in place and certain ways of doing things, often with a skilled in-house team who know their systems inside out."

Callan adds that the situation is compounded in highly regulated environments, where large technology transformations are treated with caution and there is less willingness to be a first mover. Consequently, internal IT teams might not get the same exposure to modern practices as their counterparts in more agile spaces. They're less likely to re-architect their IT landscape to take advantage of new tech and approaches to truly unlock the value in their data.

Another issue is the multitude of data sources in the sector. ESG and financial data, whether it's from Bloomberg, Reuters, or proprietary research that the analysts within the organization performed, come from many different and disparate sources, so aggregating and reporting on it requires chasing it down.

The problem is, some people have access to data because they're responsible for regulatory compliance, but other teams, like client-facing sales and marketing, don't have access to the same information in a format that they can understand or where it's contextualized. "The data isn't democratized," Simon Callan says, "and they're missing out on that holistic view." This results in an inability to deliver new requirements in the event of new regulations or if an existing one changes. Siloed data is bad for organizations because it creates a lot of manual tasks, he says. It also leads to further data hoarding. This is where data silos and inefficiency happen. Impactive is taking a self-service approach to bringing data together, which is why Simon Callan chose to work with MongoDB Atlas.

Jumpstarting app development

Simon Callan calls the choice of MongoDB Atlas a no-brainer because of all the different types of relationships they needed to model. "When we started out, we had a rough idea of what our data model was, the attributes, and how they would interconnect," he says. "But it would have been too rigid in the beginning if we had to keep maintaining and changing tables and columns." Because he had a history of working with relational databases, he understood the hurdles he would encounter along the way. "On the relational side, in my experience, basic problems emerge a lot quicker," he says. "We've seen basic things such as indexes missing and people getting tangled up in table relationships, things like that. MongoDB gives you a lot of leeway. It gave us the ability to just get going."

Simon Callan says they work extensively with the aggregation framework and that he has come to appreciate how powerful it is and what it can do. The aggregation framework, MongoDB Atlas Search, MongoDB Atlas Charts, and ACID transactions are a few of the features being used already at Impactive and he expects to leverage more of the MongoDB ecosystem, such as queryable encryption, in the near future.

Having multiple data sources for ESG research, voting, and other investment data means integrating them. "We aim to consume and ingest as many sources of data as possible," Simon Callan says. "We're not the replacement for an external, specialist system. We just want to surface the key facts, which can be used as a jump-off point to go deeper into the system and get further data. That's the approach we're taking. That’s what we mean by the ESG integration hub."

Impactive and the developer experience

Both at Impactive and outside of it, Simon Callan invests considerable time and resources toward improving the developer experience. In fact, it's more important than ever, he says. "I always ask engineers, 'Why do you not like helping yourself? Why do you keep doing this or that task manually?'" Over time, Simon Callan has seen the developer experience get easier. The tooling and services around it have improved, he says, and the appreciation of it. "There's kind of an internal customer, and it's the engineers. You have to look after them," he says.

"MongoDB was born from an engineers-first mindset. If you can get the engineers on board, get them going, and get them active, it makes a difference," Simon Callan says. "MongoDB is a great example of starting with open source and building on from there."

Being part of the MongoDB for Startups program, Impactive was able to get access to free credits and consultancy time with MongoDB experts. All MongoDB for Startups members get free credits for MongoDB Atlas and related MongoDB products, including Atlas Database, Atlas Search, and Atlas App Services. Members also get expert technical advice and can participate in joint sales motions and go-to-market opportunities.

One of the big issues with confidential and sensitive ESG engagement data is security. After his consultation with MongoDB experts, Simon Callan was confident that they had the security measures in place to ensure that only authorized users have access to data, which is encrypted in transit and at rest. "The two-hour consultation was super high value. We covered a lot of ground such as Atlas setup best practices and how to monitor and set up alerts. Plus, the consultant patiently explained things and answered all my questions with a positive attitude," Simon Callan says.

On a mission

Impactive's mission is to help investment firms implement responsible investment effectively through technology so the big problems we're facing can actually be solved. It's not just about ticking checkboxes, it's about actually doing things meaningfully. Whether it's helping organizations report more easily to address regulatory concerns or eliminating the silos that have built up over time, Impactive acts as a central activity hub, where all investment interactions, ESG engagements, voting, and research share the same space.

For more information about Impactive's solutions for ESG data and career opportunities, visit the website.

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

If you would like to know more about how MongoDB makes it easy to collect, analyze, and visualize ESG data in real time, read more here.

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Operating at Scale Using Resource Tags

We are excited to introduce MongoDB Atlas' new resource tagging functionality. You can now organize and manage Atlas resources with improved efficiency by applying tags to categorize database deployments based on your business needs. In turn, you are empowered to better work with various tools and integrations within your technology stack in order to analyze costs, monitor resource consumption, and automate workflows at scale. What is a resource tag? Tags are key-value pairs that associate with a resource to provide additional context. For example, tagging a cluster with the key environment and value production would help to identify this cluster as being deployed to production. A tag can only have one value for a given key, on a particular resource. Therefore, this cluster could not have another tag with the key environment and the value production . Why use tags? Adding metadata such as the environment, application, team, or cost center can help you visually or programmatically identify, filter, or group related resources. You can also perform automation tasks in bulk using the existence of tags on database deployments. Some common use cases include: Cost management - use tags to create reports and view consumption by characteristics unique to your business. Resource management - use tags to easily identify or group related resources by team, service, application, or owner. Security risk management - use tags to identify which resources may contain sensitive information, or automate processes to detect security and compliance issues (e.g. Cluster A is a production deployment without backup enabled). Operations management - use tags to help teams audit and track whether resources meet certain business requirements. Automation - use tags on database deployments to automate workflows. Let’s walk through a hypothetical scenario for my LeafyGreen Application, an online plant center: I manage several types of deployments with different purposes. My team primarily uses tags for resource management and our team’s tagging policy requires that every deployment has the application , environment , cost-center , type , and service tags . Let’s look at how this works in Atlas. Managing Tags in the Atlas UI We started off by adding tags in the Atlas UI . Tags can be added whenever you create a deployment, or to an existing deployment on the Database Deployments page. Managing Tags in the Atlas CLI Another team prefers to use the Atlas CLI to create and manage their deployments. In the example below, they are using the --tag flag on the atlas clusters create operation to add tags to a new cluster named “TagsUsingAtlasCLI.” Next, they confirm the cluster details using the atlas clusters describe command. Notice that all five tags are included in the output. You can also use any of the following Atlas CLI operations to create, update or modify tags on clusters or serverless instances. atlas clusters create [--tag key=value]... atlas clusters update [--tag key=value]... atlas clusters upgrade [--tag key=value]... atlas serverless create [--tag key=value]... atlas serverless update [--tag key=value]... Additionally, the commands atlas clusters describe , atlas clusters list , atlas serverless list , and atlas serverless describe will contain tags on the JSON output. Managing Tags using the Atlas Admin API Another team has scripts set up to manage deployments for their projects using the Atlas Admin API . This team manages serverless instances, so to create a new deployment with tags, they’d use the Create One Serverless Instance operation and include the tag keys and values in the request body. Here is an example of the body of that API call. If they wanted to add tags to existing serverless instances, they can use the Update One Serverless Instance operation to accomplish this. At this point, we have all of our deployments tagged with our required tags. We can use this information to see which clusters are associated with which departments, the different services that are running, as well as which environment they are running in. As a next step, the security team can easily set up deployment tags to detect security and compliance issues. Once resource tags have been added to your database deployments these tags will appear on the Database Deployments page, as well as other areas of Atlas. For this reason, be sure not to include any sensitive information in tag keys or values because these values are stored in plain text and can be exposed in billing invoices, audit logging, and other operations on different resources. Resource tagging best practices Tagging approaches can range from simple to complex, depending on the needs of the organization. To get started, here are a few best practices to help you operate at scale. Do not include any sensitive information such as Personally Identifiable Information (PII) or Protected Health Information (PHI) in your resource tag keys or values. Use a standard naming convention - including spelling, case, and punctuation for all tags. Define and communicate a strategy that enforces mandatory tags on all database deployments. We recommend you start with identifying the environment and the application/service/workload. Use namespaces or prefixes to easily identify tags owned by different business units. Use automated tools such as Terraform or the Admin API to programmatically manage database deployments and tags. Plus, you can now leverage resource tags within billing invoices , which empowers you to categorize spending based on custom dimensions (such as cost center) and streamline financial reporting. These tags are available in the Invoice CSV export and the Invoices Admin API operation. Additionally and as part of our future roadmap, you will be able to apply tags to other resources, such as Projects. Do you have other ideas on how you’d like to use resource tagging in Atlas? Share your ideas on feedback.mongodb.com . Conclusion MongoDB’s Atlas resource tagging capability provides a simple and easy way to organize and manage database deployments at scale. Tags can be used to create cost allocation reports, support automation scripts, or detect security and compliance issues. Sign up for MongoDB Atlas , our cloud database service, to see tagging in action. Resource tagging is available in MongoDB Atlas, for clusters and serverless instances. For more information, see Atlas Resource Tagging .

July 6, 2023
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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024