Navigating the Analytics Landscape: The Impact of Mindset and Methodology on Data Success

Introduction:

In the realm of data management, the importance of an analytic mindset cannot be overstated. This mindset is not just about crunching numbers; it's about understanding the journey of analytics, from the initial goal-setting to the final analysis. It involves recognizing the objectives at each step and using them to guide our approach to problem-solving.

Understanding the Iterative Nature of Analytics:

An often-overlooked aspect of data analysis is its iterative nature. Each layer of sophistication, whether it's in data collection, labeling, or computationa processing, adds time and complexity to the project. Yet, it's crucial to ask ourselves: Is this complexity necessary for achieving our initial objectives?

Case Study: Improving Inventory through Geocoding:

Let's consider a real-world example. Our goal was to improve inventory management by extracting and utilizing address information from unstructured data. Initially, we were able to extract only a small percentage of geocding information from our sample data. This low success rate was unacceptable for our purposes, leading us to the next crucial step: data cleaning.

The Power of Data Preprocessing:

In analytics, preprocessing is a vital step before running sophisticated models. For our project, we developed numerous Python functions to clean dirty address extractedf rom invoice data. Simple preprocessing steps, like converting 9-digit postal codes to 5 digits (for U.S. addresses) and removing non-address prefixes, were implemented. We created 12 rule-based preprocessing functions as our initial cleanup strategy before even considering machine learning algorithms.

Remarkably, these 12 preprocesing steps significantly improved our geocoding matching rate, showcasing a dramatic improvement without the need for a complex machine learning model. While we didn't hit a 90%+ match rate, the preprocessing alone more brought us from a 20% to a 40% success rate, a substantial leap forward.

Conclusion:

The lesson here is twofold: First, appreciate the power of an analytic mindset and the iterative process in data analysis. Secondly, recognize that sometimes, intermediate steps, like achieving a 40% success rate, can be a milestone worth celebrating. It's about setting realistic expectations and understanding that each phase of improvement brings us closer to our ultimeate goal. The journey of analytics is as much about the steps we take as it is about the final destination.