How can we explain the unexpected 25% increase in data processing time for Escalent's healthcare patient journey mapping projects in the last two weeks?

Introduction

The unexpected 25% increase in data processing time for Escalent's healthcare patient journey mapping projects over the past two weeks is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for our product and users.

To tackle this problem, I'll follow a structured approach that covers issue identification, hypothesis generation, validation, and solution development. My goal is to not only resolve the current performance degradation but also to implement preventive measures that will enhance our product's resilience and efficiency in the long run.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent system update. Has there been any significant change to our data processing pipeline in the last month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, we deployed a new data ingestion module two weeks ago. Impact on approach: If confirmed, we'd focus on the new module's performance and integration.

• Considering the specificity of the increase, I'm curious about our measurement precision. Has there been any change in how we measure or define data processing time recently?

Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No changes in measurement methodology. Impact on approach: If changed, we'd need to recalibrate our baseline metrics.

• Given the healthcare focus, I'm wondering about data complexity. Have we seen any changes in the types or volume of patient data we're processing?

Why it matters: Changes in data characteristics can impact processing time. Expected answer: We've started handling more complex imaging data recently. Impact on approach: If confirmed, we'd investigate our system's capability to handle new data types efficiently.

• Thinking about external factors, has there been any change in our cloud infrastructure or service providers?

Why it matters: Infrastructure changes can significantly impact processing performance. Expected answer: No major changes reported by our cloud provider. Impact on approach: If changes occurred, we'd need to engage with our provider to optimize our setup.

• Considering user behavior, have we noticed any changes in how our clients are using the patient journey mapping feature?

Why it matters: Unusual usage patterns could strain our system in unexpected ways. Expected answer: Some clients have started running more complex, multi-stage journey maps. Impact on approach: If confirmed, we'd need to optimize our system for these advanced use cases.