.png)
Data-Fueled Telehealth: A Startup’s BI Innovation in Their $100M+ Exit
- Christian Steinert
- May 6
- 6 min read
Driving ROI in the pandemic with a healthcare external analytics platform
Setting the Stage
In 2020, the medtech startup I worked for was riding the telehealth wave. Its growth was huge, raking in the largest revenue numbers in company history. However, this scale created a bottleneck for customer usage data reporting.
I was the Lead Business Intelligence Analyst on the team at the time. We relied on automated CSV email sends via our internal reporting platform. Curating these scheduled sends and ensuring the data was accurate/without errors for every customer was a time-sink. If customers had questions or feedback, there was always an elongated loop of emails and meetings to attend to. We recognized this wasn’t sustainable at scale.
Instead, the Director of BI & Analytics and I brainstormed a solution to automate this. To eliminate the time spent answering customer questions, managing scheduled CSV report sends, and better handling data governance, we planned to build an external analytics platform.
The Data Reporting Scale Up
Beyond efficiency, this initiative among many others at the company were a huge reason why the start-up was acquired for $100M+ at the end of 2020.
Prior to this platform, the scheduled CSV sends were queued up in our internal Looker environment. The burden was placed on me to manage all 60+ enterprise customer report sends. The reports contained usage data on a healthcare provider’s electronic fax, reminders, video chat, log ins, broadcast messages and secure text usage numbers.
Providers used these numbers during the pandemic to make critical financial decisions. Externally, this current state was far too static of a solution. Our customers needed the freshest insights possible, and our current process was not giving them that.
Internally, the burden on me, as the analyst, was ensuring that each day the reports sent out were sent successfully and accurately. This was a constant manual stressor, and took time away from my focus on more important data analysis initiatives to grow the company's bottom line.
The External Customer Analytics Platform
We partnered with Google Cloud to build out the external analytics platform. Due to our internal tech stack in Matillion, Snowflake and Looker, it made sense to leverage Looker for this external customer analytics platform. Building off the data and logic found in our internal data platform was crucial to ensuring the accuracy of the reporting found in this external platform.
We set up special permissions to ensure one customer could only ever see their data and not a different customer’s data accidentally using LookML, Liquid and advanced permissioning. Once the code and reports were built out, we validated the data against our internal platform.
To ensure self-service and adoption, I built out a set of training documents and videos for customers that was part of the info section of the home page. We covered topics such as how to use the dashboards, filter and explore their own data.
Furthermore, we showed them around the platform in person and even enabled them to export the data to CSV if needed. All of this was far more powerful and efficient than waiting for a one time per week CSV email of their usage reporting.
The platform contained detailed analytics dashboards and reporting on the following usage:
Electronic fax
Reminders
Video chat
Log ins
Broadcast messages
Secure text
Check out the photos below showcasing some of the main dashboards in the platform.
The Real Value of Data Innovation in Healthcare
Like most healthcare data teams, the greatest challenge is working with customers to adopt a new technology. The comfort found in spreadsheet reporting will never go away, but our goal with the external analytics platform was to enhance their existing workflows.
After showcasing the platform to a few Chief Medical Officers of some of the largest medical practices in the area, we knew we had something great with their positive feedback and suggestions for improvement.
Additionally, we could indirectly contribute this initiative to one factor for the company’s record breaking $100M plus acquisition at the end of 2020. The acquiring company was ultimately very impressed with the data environment we built with a small team in record time.
Below are a few critical soft Return on Investment (ROI) wins.
1. Heightened Customer Value With Reduced Time to Insight for Their Critical Decisions
The pandemic brought on unpredictable and volatile staffing requirements for healthcare practices. Waiting for reporting numbers one time per week wasn’t enough. Our customers needed the most up to date data possible.
With up to date data, our customers were making critical staffing decisions that served to maintain margin for these practices so they could survive the pandemic. It also helped them optimize to enable them to serve as many patients as possible.
Furthermore, they didn’t want to be stuck waiting on us, the data team, to provide additional details and ways to slice and dice their usage. This analytics platform greatly increased their ability to track trends, understand their telehealth usage like never before, and uncover new insights into areas of focus with the telehealth platform.
2. Decreased Internal Maintenance Overhead and Data Governance at Scale
This external analytics platform allowed me to focus on the strategic data tasks such as predictive analytics and forecasting. Once the platform was deployed, gone were the days of managing a series of CSV emails weekly to each customer.
This reduced the number of fire drills I was answering to each day if a given customer’s reporting was wrong or they had questions.
It also enforced a level of data governance not possible from the CSV reporting solution. When we were doing manual CSV sends, there were times when I’d make a human error and miss a critical filter for a customer’s data. This resulted in inflated usage numbers that we had to correct, making for difficult and awkward conversations with customers.
The external analytics platform’s source code enabled strong data governance, which greatly reduced the risk of error on our end.
A Look at a Hard ROI
While the qualitative benefits like enhanced customer value and reduced internal friction were significant, the external analytics platform also delivered a clear hard return on investment. The primary saving stemmed from reclaiming valuable analyst time previously consumed by manual reporting processes for our 60+ enterprise clients.
Managing the weekly CSV reports, validating data, troubleshooting errors, and fielding ad-hoc questions consumed an estimated 10 hours per week for me, the Lead BI Analyst. At an estimated fully loaded cost of $75/hour (based on typical analyst compensation and overhead), this translates to roughly $37,500 in annual operational savings simply by automating this workflow via the new platform. This unlocked approximately 500 hours of specialized analyst time annually. Instead of reactive report maintenance, this time was redirected towards strategic initiatives that we mentioned above, directly contributing to the company’s growth goals, especially vital for a company at multi-million ARR aiming for further expansion.
Furthermore, the platform mitigated the tangible costs associated with data errors inherent in the manual CSV process. Each significant error, like the previously mentioned inflated usage numbers, required considerable time for identification, correction, and customer communication – easily representing thousands of dollars annually in reactive effort. Automating delivery and standardizing logic through the platform drastically reduced this risk and associated cost avoidance, protecting revenue and client relationships.
While building the platform required an initial investment utilizing our existing Matillion, Snowflake, and Looker stack alongside Google Cloud, the direct annual savings of over $37,500 offered a rapid payback, justifying the resource allocation even within a 100+ person startup environment.
Conclusion: Data as a Strategic Growth Engine
The journey of developing this external analytics platform illustrates a critical lesson for rapidly scaling medtech companies. Faced with operational bottlenecks and increasing customer demands during the unprecedented telehealth surge of 2020, our team transformed a reporting challenge into a strategic advantage.
By moving beyond static, manual CSV reports to a dynamic, self-service analytics platform built on our existing data stack (Matillion, Snowflake, Looker), we achieved significant wins on multiple fronts. We drastically improved the time-to-insight for our healthcare provider clients, empowering them to make critical operational decisions during a volatile period. Internally, we eliminated manual overhead, reduced the risk of costly errors, and freed up valuable analyst resources for higher-impact work. We delivered a clear, quantifiable return on investment exceeding $37,500 annually in direct savings alone.
Ultimately, this initiative wasn't just about better reporting; it was about building scalable data infrastructure that enhanced customer value, improved internal efficiency, and strengthened data governance.
It demonstrated a commitment to data-driven operations that impressed stakeholders and contributed positively to the company's successful $100M+ acquisition. This experience underscores the power of proactive business intelligence innovation – transforming data from a simple operational output into a core strategic asset that fuels growth and significantly enhances company valuation.
Comments