Q&A: How Moody's tailors the data user experience

Moody’s Analytics is a leading global provider of financial and risk data and services. They serve thousands of customers with a variety of best-in-class data products and services. In early 2021, they launched Moody’s DataHub, a next-generation data delivery experience built on the Harbr platform.

Saurabh Patel is a Product Consultant at Moody’s Analytics. With a focus on big-data delivery platforms — such as DataHub — and credit applications, Saurabh works closely with clients to help them get valuable insights as quickly and effectively as possible.

I recently sat down with Saurabh to learn more about his role and how Moody’s DataHub drives immense value to customers and internal users alike.

Saurabh, can you tell me a bit about what you do at Moody’s Analytics?

As a product consultant specializing in data delivery channels, particularly DataHub, my role involves guiding clients through the entire journey of onboarding onto DataHub. This includes assisting them in becoming acquainted with the data and tailoring data products to meet their specific needs. My role also involves aiding clients in either exporting data to their systems or effectively utilizing the data within DataHub Spaces.

You mentioned tailored data products. What does this involve?

There are two main use cases here. The first is basically custom data feeds for clients. Rather than giving them the entire data product, we can help clients configure data feeds that can integrate more easily into their systems. The second is customizations for internal users — that is, people within Moody’s Analytics. These internal users can access these customized products through DataHub.

Can you elaborate on what one of these custom internal products looks like?

Typically, this situation arises with our larger data products, many of which exceed a billion rows. Such volume complicates the process for users who need to sift through individual files and conduct effective queries. The complexity is further heightened when merging different tables, as this process is prone to errors. To alleviate these challenges, we have been handling these customizations within Spaces. This allows us to efficiently create and execute queries on the data, thereby aiding our colleagues in their research and development of new data products.

Shifting focus to users outside of Moody’s — people in customer organizations — what do you see as one of the main benefits of DataHub?

Our long-term customers have traditionally received data through direct feeds and APIs integrated into their systems. With the implementation of DataHub, we’ve observed a significant improvement in our ability to rapidly develop and demonstrate proofs of concept (POCs) for data products. Customers can conveniently evaluate and test these data products on the platform, using the necessary endpoints. This capability greatly expedites the process, reducing the research and development time required to create data feeds tailored to their specific needs.

Can you give me an example of how DataHub saves this time?

Providing customers with a Proof of Concept (POC) is often the most challenging aspect, especially considering compute time. For instance, if data ingestion from the source to the compute environment takes 20 days for a task requiring only two days of compute time, it significantly increases operational duration by tenfold. However, if the data is pre-loaded, the POC can be executed much more rapidly, accelerating idea development.

To illustrate further, consider a client needing regular conversion of data from a native format to Parquet, followed by an automated upload to their S3. This process can be completed without manual intervention, which is a significant advantage for clients modernizing their operations and transitioning to cloud-based solutions. They benefit from direct data ingestion into platforms like GCS, Azure, or S3, eliminating the need for manual uploads via SFTP. This automation streamlines their processes and enhances efficiency.

Interesting. You mentioned the format of data products. How does DataHub affect delivery of data in the various formats that your clients use?

Our products are predominantly in traditional formats. However, there is a growing client demand for delivery in Parquet format. This requirement necessitates an additional operational process for conversion, which introduces potential risks of data loss and errors. Harbr’s technology significantly streamlines this conversion process, mitigating these risks and enhancing efficiency.

Thank you Saurabh!

To learn more about the award-winning Moody’s DataHub, check out their website or our case study. You can also find Saurabh on Linkedin.

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