Serve customers by providing tools that help them better run their businesses.
This idea is one that Director of Research John Rong at Supernova Technology said he takes to heart all the time, but especially in the creation of a new data reporting tool. Supernova provides its customers in the wealth management industry with end-to-end digital securities-based lending (SBL) solutions. When customers said they wanted more data analysis and reporting capabilities, Rong and his team — co-led by Supernova’s Co-founder and Head of Operations, Jeffery Finn and Product Manager Mulin Shen — delivered.
“While engaging our customers, we learned that we could provide additional value to them with more data flow from our SBL solution,” Rong said. “We decided we needed to expend more effort on making data more visible and usable for clients, so we asked ourselves, ‘How we can better organize the information, then represent that data in a way that supports their actual workflow? And how do we provide the information they need to have more informed decision-making when they work?’”
Smart Analytics was the answer: a data analytics, visualization and reporting system built from scratch by dedicated cross-departmental teams at Supernova. The system presents data reports to clients based on the specifics of their business and the insights they find most valuable, like mining the loan book data by time period to identify key trends and opportunities for performance improvement.
But building the modular system wasn’t simple.
Data silos needed unification and teams had to dive in for nuanced insights within a deep data lake. Rong, Finn and Shen shared what went into building the Smart Analytics system and how they overcame those challenges.
There are a number of financial entities in Supernova’s client orbit, including banks, financial advisory firms, broker dealers, wealth management technology companies and more. The company’s core platform — which helps manage over $4.9 billion in balances — lets clients interact with data in real time through various user interfaces. Entities in the banking industry must regularly submit reporting to the Federal Reserve and other financial regulatory bodies so they can monitor the health, practices and compliance of the organizations in efforts to prevent widespread financial collapse or other unfavorable outcomes.
According to Finn, clients wanted improved functionality in generating regulatory reports and greater transparency in monitoring what their end users were doing with their SBL lines of credit. Smart Analytics aims to provide the specific reporting, monitoring and analyzation suites that clients need.
The customer’s perspective — combined with market research — played a vital role in how the project took shape. Mulin said he likes to think about projects from customers’ shoes, which is easy to do since his team interacts with them regularly.
Shen: We have a lot of customer-facing teams like program support and technical support that are project-based. During this project, they had daily conversations with clients to get feedback from interviews and other resources.
Rong: We learn a lot by listening to customers about their daily struggles and what they’re trying to accomplish. They’ll say they’re generating these types of reports or gathering this type of information and we’ll internalize that and ask if we’re able to better support them to fulfill those purposes.
We ask how we can combine trends in available solutions and what we learn from customers on a daily basis.”
We also look at industry research, market competitive analysis, business intelligence and big data available in the market. We ask how we can combine trends in available solutions and what we learn from customers on a daily basis. Based on researching the market, we developed the functionality framework we wanted to build. Then we plugged in all the needs from the client.
The challenges of disparate data
The internal and external teams accessing and updating data shared across the core platform can vary significantly. The data Supernova provides, analyzes and stores — from loan documents to borrower information to risk data — is also varied, and often siloed.
Supernova’s Smart Analytics team had to not only wrangle that data, but they also had to determine what had the most value and the best way to present it to users.
Rong: When you look at an SBL business’s workflow for something like loan origination at a large institution, that process crosses different stages and departments of the business. We had to determine how to lump all that data together and look at a workflow from a cross-referencing perspective based on how a customer uses their data so we could service them in a better way once a loan is live, for instance.
We need to know what kind of data the customer is really interested in.”
Shen: Data mining between different structures is one challenge we face right now. We need to know what kind of data the customer is really interested in and how to find the data that a customer will need in our very large data lake. We have to utilize the data we have and determine how it can be presented even better so that customers can leverage it to tell a story and help their users.
Finn: Since the platform is used by a wide variety of users like operations, sales executives, as well as legal and compliance teams, we spent a lot of time getting the navigation correct and making sure all our different customer types can get the information they needed in an efficient manner.
Rong: Clients get a lot of data from different sources and they also get data from us. But these data silos are major challenges that prevent people from getting in-depth insights into what’s going on. When you look at business intelligence tools, they look and feel very similar. So it all comes down to the data you’re looking at. Can that data provide the insight that the user needs? The more you aggregate and cross-reference different types of data, the higher the magnitude of insight you can provide based on it.
So we expended a lot of effort in building our basic database infrastructure. It’s about gathering, storing and cross-referencing data and to make it higher quality so that the output is really meaningful. We have different reports for big data, but everything boils down to whether you have reliable, quality data. When we built our infrastructure around our data lake, we put a lot of thought around how to represent the data in a more usable way. Breaking down silos ties all the knots and provides insight to our institutional customers.
Culture lends a hand
The team said all the work around data unification and customer fulfillment was aided by Supernova’s collaborative and entrepreneurial culture. According to Rong, the company was founded in 2014 by experienced leaders who laid the groundwork for the data-driven success the company sees today with Smart Analytics.
Rong: Our CEO, Tao Huang, is an industry veteran that put the infrastructure in place to support our SaaS solution and workflow. He also influenced how we’ve handled data from the get-go. So for me, if we get positive feedback on Smart Analytics and run it smoothly, we owe some credit to how the system was set up from the beginning.
Shen: We are a young company, so we don’t need to worry too much about any legacy system creating trouble for us. Also, I’m still learning every day from different people in this industry and that’s really exciting for me.
We make sure we communicate with each other effectively. Our product team is in a very centralized position because we need to communicate externally with clients and internally with different teams like operations, programs and engineering on the back end. Our great communication culture also extends to our flat structure. When I have an important question, I can directly email our C-suite executives, including our CEO, and they’re always quick to get back to me.