Data to Insights: It’s a journey

Last Friday, my friend Aron, who is CEO of the largest healthcare clinic chains in the Northwest, invited me for a lunch meeting. Knowing his penchant for in-depth discussions, I cleared my calendar for the rest of the day, ready for an engaging conversation.

During lunch, we dabbled in polite conversations, mainly discussing our kids and our usual topics: Seahawks, weather, and hiking. The business discussion gained momentum after lunch as I initiated, "How is business doing, Aron?"

"Our revenue has surged since we launched our new promotional campaigns. However, I'm genuinely concerned about operational costs and could greatly benefit from your team’s AI expertise," the CEO expressed, almost pleading.

"Have you already identified areas for cost reduction, and has there been any internal initiative?" I inquired.

"If you don't mind, I'll have my COO join us." Before I could respond, he swiftly summoned his COO, who was eager to present the work he had undertaken.

"I've thoroughly examined all costs, and our CIO is exploring an AI product to optimize costs across the entire ecosystem," the COO proudly mentioned.

We decided to relocate to the conference room, where we were joined by the CFO, CIO, VP Sales & Marketing, Director Analytics, and VP Product Development. As I entered, I noticed the grand French windows offering a view of the beautiful city despite Seattle's typical cloudy sky.

However, I was eager to delve into the business discussion. "What data do we have, and what insights does it provide today?" I inquired, seeking a deeper understanding of the business intricacies.

"We have data covering our entire operations, from employees, contractors, suppliers, patients to competition," the Director of Analytics stated as he connected his laptop to the projector.

"We've analyzed cost-reduction opportunities across the board," the CFO added.

“It’s good to know that we have data, but are we sure that the data is clean and comprehensive?” I was skeptical as the chain had thousands of clinics spread across multiple states and was still acquiring more clinics. "Yes, we are confident. We're also in the middle of wireframing our AI Product. We'll share it with you and welcome your input on the best deployment approach," the VP of Product Development chimed in.

Following an hour of data examination, I noted several assumptions by the team, each of which I believed to be incorrect to varying degrees:

Leadership appears to believe the company is ready to launch an AI product that will miraculously reduce costs.

The VP of Product Development seems focused on creating the AI product to achieve the targeted cost reduction.

The CIO appears to assume he has complete knowledge of all company systems, while the Director of Analytics believes he has collected all relevant data from every possible system.

Having grasped the fundamental data structure and system architecture, I inquired, "Do we genuinely believe that AI is the starting point for cost reduction?"

"Yes, we see how AI is reshaping the world, and we want to stay ahead," came the response.

"Fair point. Let's examine your dashboard." After a few probing questions, gaps became evident.

"It appears that campaign effectiveness isn't even on your dashboard, despite being a major revenue driver in the last three quarters," I pointed out.

"We're assessing it at each clinic level," they explained.

"Well, there are some connections I can't make. Since all campaign promotional costs are centrally managed, how are we distributing them by clinic to assess the margin impact? Conversely, how are we aggregating these sales and correlating them with marketing and related expenses? Additionally, there are associated sales when a patient visits due to a promotional campaign, which I don't see here. Similarly, I can't discern how doctors' time per patient varies between promotional and non-promotional visits."

"Frankly, I'm uncertain myself, given the significant price reductions in critical areas for the campaigns. The increase in revenue may be due to more visits, but I'm unsure about the margins," the CEO expressed his frustration.

"Yes, but these promotional campaigns were launched with a long-term perspective, aiming to enhance brand recognition and introduce our services to new clients," VP Sales & Marketing noted.

"Do we even know if we're profiting or losing money with these campaigns?" I inquired.

"No, the data can’t be aligned. Our profit and loss data is generated through ERP, while patient data resides in a new patient management system, and campaign data remains in the old system," the CIO explained.

"Well, this presents an opportunity for us."

Over the next couple of hours, we examined various systems and the relevant data fields. It also became apparent that the company lacked a comprehensive inventory of systems and the corresponding data. We devised a plan to inventory systems and assess all potentially relevant data fields. I offered comprehensive support to ensure Aron wouldn't need to hire additional staff for the extra efforts required, including initial cleanup, consolidation, and ongoing maintenance.

I started listing out the next steps as I was rushing for a close after five hours of discussion: "Inventorying systems and relevant data, creating a warehouse for centralizing all data, and creating a comprehensive dashboard for insights and real-time data analysis."?

The COO seemed disheartened, asking, "Are we not using AI at all?"

"We can indeed employ AI-powered data integration tools to automate data movement and transformation into the data warehouse, or even for data cleansing before that. However, the maximum impact will be realized after completing these initial steps," I clarified.

"Does this mean we won't proceed with the AI product we had planned?" inquired the VP of Product Development.

"Our objective isn't product development; it's solving the business problem, which, at this point, is gaining the right insights," I stated, as I saw the CEO nodding.

I could sense disappointment on the faces of the CIO, Director of Analytics, and VP of Product Development, so I continued, "In this scenario, the real impact of AI will be felt when we utilize AI algorithms to monitor data streams, triggering alerts or actions based on predefined thresholds. These could include ‘appointment cancellations due to wait times’ or ‘time spent by doctors per patient’ or similar other business critical metrics. AI can also assist in generating your next promotional campaign, pinpointing the right geographic or target audience. There's a wealth of potential in AI for predictive analytics, forecasting future trends, identifying anomalies and automating tasks."

As I glanced out of the conference room window, the clouds had dispersed, revealing clear Seattle skies.