Data-Driven Work Cultures: Dr Arun Hampapur of Bloom Value Corp On How To Effectively Leverage Data To Take Your Company To The Next Level

An Interview With Pierre Brunelle

Pierre Brunelle, CEO at Noteable
Authority Magazine
Published in
10 min readMay 22, 2022


Data doesn’t care about organizational boundaries. So, the best-data-driven-solution may require unexpected interactions or collaborations across organizations. Some of these cross organizational collaborations can be challenging and require senior management to look at the goals and metrics of organizations. Data can drive true organizational transformation. But transformation can be painful at times.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Dr. Arun Hampapur.

Founder and CEO of Bloom Value Corporation (a technology startup focused on applying enterprise AI for Healthcare Provider FinOps), Arun Hampapur has a Ph.D. in Artificial Intelligence from the University of Michigan and 25 years of experience in Enterprise AI solutions with the majority of that experience at IBM. Dr. Hampapur was appointed as Distinguished Engineer at IBM for his contributions to video surveillance technology and solutions. He is also a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

I have always wanted to create a startup. After earning my PhD in AI from University of Michigan, I joined a startup in the Bay Area in 1995. Soon after, I met my wife, who had a job in Connecticut. So, I ended up moving East and working for IBM’s Research Division, an amazing place to work where I was surrounded by the world’s smartest data scientists and mathematicians. I was there for 20+ years. Even while working for IBM Research, I continued to be an entrepreneur, as well as helped launch several products for IBM.

When the pandemic hit, like many people, I asked myself the question, “Am I realizing my full potential?” The answer led me to start Bloom Value. Having gathered a wealth of experience in Enterprise AI at IBM, I explored multiple industries for launching Bloom and gravitated to healthcare, possibly because I am married to a physician or because the pandemic brought healthcare to the forefront or, perhaps, both.

So, we are now developing AI Solutions for Revenue Optimization; Managed Risk Contract Optimization; Provider Optimization; Contract Optimization; and Patient Engagement Optimization. Bloom’s proprietary Healthcare FinOpsTM Models delivered on the FAST Platform thru FAST Templates ensures that our customers can get value in weeks.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?

I would like to share a learning, not so much a mistake. During the first wave of the pandemic, hospitals were using paper forms for registration. We built a simple digital self-service registration form which patients could fill out on their mobile phone thus lowering the chance of COVID infection for hospital staff. I learned how even simple technology can make a big difference when applied correctly.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

Steve Jobs’ famous Stanford “Stay Hungry, Stay Foolish” commencement address had a huge impact on me. Jobs talks about how he started Apple, got thrown out of his own company, and then got called back only to make Apple what it is today. Jobs’ story motivated me to think about creating a startup. On many levels, giving up a high income at fifty-four-years-old to launch Bloom Value in a new domain would definitely be considered foolish. I am working harder now that ever in my life. So, that takes care of the “hunger” part. Only time will tell if Bloom Value will leave an Apple like impact on healthcare and beyond. But we are well on our journey.

Are you working on any new, exciting projects now? How do you think that might help people?

Yes, we are working with a hospital in California on Proactive Patient Engagement. The Hospital is very interested in understanding its patients beyond their medical conditions. The hospital is trying to engage with their patients in a proactive, personalized, and empathetic manner by understanding their living conditions, their social conditions, and more. This effort is leading towards a key focus in healthcare today called Social Determinants of Health. More on that here: (

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” My work centers on the value of data visualization and data collaboration at all levels of an organization, so I’m particularly passionate about this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

Let’s use the example of a hospital where the nursing staff is overloaded. Should the CFO approve the hiring of additional nurses? How many nurses can they afford? Where can the CFO reduce expense? What is going to be the impact of the hiring on quality of patient service? How does hiring of additional nurses now impact the reputation of the hospital? While these questions occur to most organizations, finding answers to these questions requires extensive modeling and analysis of data. An organization which uses data to answer these questions stands to benefit on quality, cost, revenue, and margins.

Which companies can most benefit from tools that empower data collaboration?

All organizations can benefit from using data. I have worked with organizations from city police and water departments to airlines to retail stores to manufacturing companies to build AI or big data solutions. Without exception, all of these organizations have seen improvements in their customer service, quality, revenue, and margin. Data is critical to efficient operations.

Data can be critical when it comes to healthcare in terms of delivering timely care and saving lives. In 2020, during the first wave of the pandemic, hospitals ceased elective surgeries from March to May. This led to a huge backlog of cases for this hospital. Which surgeries should we prioritize? How much ICU stay is needed? How does the current COVID load impact capacity? These were the questions in front of the surgical care coordination team. They turned to the use of data to rank all of the backlog by medical need and optimized the scheduling, considering the various resources needed for the surgery from surgeons’ and nurses’ schedules to overtime schedules to recovery time. This is a great example of the use of data to impact the quality of care and the revenues of the hospital.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

I have had the privilege of working with 100’s of clients across multiple industries to help use data to tackle many challenges. When it comes to using data for safety and security, it seems like ancient history. But some of us remember the tremendous focus on Homeland Security post 9/11. Cities, police departments, and various agencies had the tremendous challenge of securing our homeland — a massive task.

Cities deployed 1000’s of cameras to monitor critical infrastructure. But who watches these cameras? They produce massive amounts of data. This was my first project where we applied advanced video analytics to monitor these cameras to enable homeland security professionals to proactively secure the homeland. While public attention has shifted to other challenges, the pandemic, wars, and so forth, I know that our homeland security professionals are hard at work keeping us safe. Here’s an example:

When it comes to using data for coordination, take for example, a newly surfaced street being torn up to fix pipes or cables under the street, it’s no simple matter to run cities, especially large ones, like NY, Chicago, DC, etc. There are multitudes of departments who have to work on many different aspects of the city — public works, traffic, police, parking, events, fire, and the like.

Working with several cities, my teams were able to build out cross-agency coordination solutions which allow various departments to share data and coordinate on projects, thus making our cities run more smoothly. Here’s an example:

The industries with which I have worked include energy, travel, and hospitality, but I will wrap up with an example in retail. When I say online shopping, what comes to mind? Amazon may be the first thing that pops us. But, as you know, other retailers like Target, Kohl’s, and Walmart also have growing online businesses. How do they handle it? Do they have giant warehouses with robots like Amazon? How do their 1000’s of stores factor in? We helped a large retailer optimize e-commerce fulfillment. They were spending 18 cents per dollar of revenue for handling e-commerce shipping. This was eating into their margins. Our team built advanced predictive models for inventory and optimization models to ship product from the most optimal locations. This led to millions of dollars in savings for the retailer improving both finance and operations at the same time. Here’s an example:

Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?

Data doesn’t care about organizational boundaries. So, the best-data-driven-solution may require unexpected interactions or collaborations across organizations. Some of these cross organizational collaborations can be challenging and require senior management to look at the goals and metrics of organizations. Data can drive true organizational transformation. But transformation can be painful at times.

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

  1. Customer Understanding: Using data to better understand our customers

One of the highest volume sales for some department store chains during black Friday is towels. Retailers understand customer patterns and prepare their supply chains to handle peak demand. This understanding of the customer goes relatively deep and enables retailers to plan long supply chains. Profitability and revenue, of course, are directly tied to the retailer’s understanding of the customer across various states, for example. The most popular color choice for towels in Texas, for instance, may be very different than in California. So, the retailer needs to understand these patterns before procuring millions of towels for their Black Friday.

2. Process Monitoring: Using data to plan and track process performance

There are 140K+ miles of train tracks in the US. Trains travel on tracks at 70+mph. There are sensors which monitor various parameters of the train along 1000’s of miles of track. This data is reported back to a command center; processed by sophisticated analytics; and provided to experts in the form of safety alerts. The command center staff processes these alerts and slow trains down when they see potential issues and even stop trains to do emergency inspections.

3. Predicting Outcomes: Using data to predict process outcomes

The pre-cogs in the film “Minority Report” have the ability to predict future crimes through special powers. While our current police departments cannot predict or see the occurrence of a specific crime, they can understand patterns and use the probability of crime to dispatch and position units, thus deterring the occurrence of crimes by their presence. You can find more on that here —

4. Optimizing Outcomes: Using data to optimize processes

City Water Systems are a good example of this. With a small maintenance crew and few trucks, water agencies in cities need to maintain 1000’s of miles of pipes, fire hydrants, catch basins, and other equipment. Often, urgent issues like water main breaks take up the majority of their time, while small problems get ignored. But small issues then become big ones and the vicious cycle continues. We worked with a major city’s water agency to develop an optimized work scheduling solution that assigns low priority work to a truck that is on the way to dealing with a major issue. This approach allowed the water agency to clear out a huge back log of low-priority work, thus optimizing their workforce through the use of data

5. Optimizing Enterprise Value: Using data to optimize enterprise value

Very few enterprises are at Level 5 in their AI journey where they look at connecting profit, loss, shareholder value, and customer satisfaction to the various operating departments like sales, marketing, customer service, and service delivery within their enterprise. Enterprise optimization is a new emerging space where there is significant value to be gained.

The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

For a data driven work culture, two things are critical — acceptance of failure as a valuable outcome for a project and a leadership team that is keen on constant improvement through experimentation. If you have these two ingredients, teams can focus on driving improvements, measuring impact objectively, and adjusting their strategies based on what the data shows. This is not easy in organizations where chest thumping is part of the culture.

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

Yes, work has become fluid. But the needs of the customer/patient haven’t changed. This poses a major challenge for organizations as they have to accommodate the changing patterns of employees while ensuring that the customer and business outcomes are met. Data will play a major role in delivering high-quality outcomes while dealing with fluidity in the work force.

Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you to achieve these goals?

Yes, we have many exciting goals for Bloom Value. I would like to highlight two goals. AI in Healthcare today is expensive and takes a long time to deliver value. At Bloom, we are committed to addressing both of these challenges in our products. I think of this as a “Henry Ford” moment for AI. We need to have an automated, assembly-line approach to building out AI solutions which address the most pressing challenges of healthcare providers. We are focused on achieving this approach through data-engineering, automated machine learning, and integrated process automation. Getting to automated, assembly line AI is a long journey. But we are excited that we are making progress in bite-sized chunks.

How can our readers further follow your work?

LinkedIn —

Instagram —

Facebook —

Twitter —

Thank you so much for sharing these important insights. We wish you continued success and good health!



Pierre Brunelle, CEO at Noteable
Authority Magazine

Pierre Brunelle is the CEO at Noteable, a collaborative notebook platform that enables teams to use and visualize data, together.