Inconsistency: the largest contributor to noise in data analytics

There's an incorrect perception that noise in data analytics is mainly caused by noise in the data... apparently, that's not true. Solid's CTO & Co-Founder, Tal Segalov, explains.

In today's business world, data analytics is key. It turns raw data into useful insights that help companies make smart choices. But, analytics teams often face a problem: inconsistency. This is highlighted in the book "Noise: A Flaw in Human Judgment" by Daniel Kahneman, Olivier Sibony, and Cass Sunstein. The key finding in this book is that the largest source of noise and error in human decision flows stems from the inconsistency which is inherent to human analysis. Every time we approach a problem, we do it a little differently due to who we are, what we had in mind and what we did right before it.

The Problem of Inconsistency in Data Analytics

Differences in methods, data sources, and metrics for the same business questions create inconsistency and noise for analytics teams. When two analysts in the same company use different data or metrics to analyze sales, they get different results. This is what the book "Noise" calls “undesirable variability in judgments”. It's not just about personal biases, but about unpredictable differences in human judgment. This noise is actually the dominant cause of errors in many systems.

For example, different teams might analyze sales for the same product differently. One team might use online sales data, while another might include in-store sales data. This leads to different and conflicting insights.

Fixing Inconsistency with Automation and Documentation

These inconsistencies show the need for standardized and automated approaches. Automation tools that standardize data sources and metrics ensure that all analysts work with the same basic data and methods. These automated tools are meant to assist the analysts with a better, more accurate and consistent starting point for their analysis. The documentation component allows them to validate recommendations and reuse past work easily.

Automation doesn't replace human analysts; it improves their abilities. The authors of "Noise" suggest training machines to copy informed decisions made by people, and then letting the people using the systems review and validate its reasoning. This approach reduces the variability that human judgment often introduces. It reflects the growing trend of using AI and machine learning in analytics for consistent processes. It also builds on the strengths of AI systems in scanning through large amounts of data, personalizing the output and doing that at scale.

The Human Touch in Reducing Inconsistency

While automation is important for minimizing inconsistency, human input is still essential. Machine learning algorithms can learn from past data and copy successful decisions, but humans provide the nuanced understanding and context needed to interpret these insights accurately. This is where analysts shine, and what they really want to do - they don’t want to repeat the n’th iteration of the same report others have been providing. They would like to use the data to understand business outcomes, decisions and recommendations.

The goal is to find a balance where AI handles repetitive tasks prone to variability, and humans focus on higher-level analysis and strategic decision-making.

Solid's Approach to Reducing Inconsistency

At Solid, we're building solutions that follow these principles. Our AI-driven analytics workflow platform learns from the most successful past analyses within your company and applies this learning to new tasks. This reuses proven methods, ensuring that each analysis is consistent and stable. By reducing variability, we significantly decrease inconsistency in analytical outcomes, providing businesses with more reliable and actionable insights.

Our approach combines the best of automation and human decision-making: AI systems that reduce inconsistency by copying best practices, and human analysts who add strategic and contextual understanding to these insights. This partnership between AI and human intelligence ensures that companies can trust their data-driven decisions, reducing the risk of inconsistent outcomes.

In conclusion, the book "Noise: A Flaw in Human Judgment" offers valuable insights for data analytics in today's business world. By recognizing inconsistency as a major problem and using automated, standardizing solutions, businesses can improve their decision-making. Solid aims to lead this change, creating a bridge between the capabilities of AI and human judgment to create a more consistent analytical environment.