At IKAUE, we’ve organized these techniques into a matrix to simplify their understanding and application. This matrix is the result of years of experience in data analysis and represents an intuitive and effective way to approach manual data analysis. The IKAUE matrix is a practical guide that allows you to choose the most appropriate analysis technique peru phone number data based on your objectives and the type of data you want to analyze.
The matrix is structured on two axes:
Types of analysis:
Descriptive vs. Comparative. Descriptive analysis seeks to understand the nature and composition of data, answering questions like “What is happening?”
On the other hand, comparative analysis focuses on identifying differences, patterns, and anomalies, seeking answers to questions like “Why is this happening?”
Focus of analysis:
Macro Metrics, Trends, Granular Analysis, and Key Segment Analysis. This axis defines the level of detail and type of data we focus on when analyzing, from an overview of the business with macro metrics, through analyzing data evolution over time with trends, to analyzing each piece of data individually with granular analysis, and analysis of question #1 from the dipifr exam december 2018 grouping data into segments with key segment analysis.
This structure, when combined (2 types x 4 focuses), gives rise to 8 types of analysis. We can perform both a descriptive analysis of sales trends (Descriptive + Trend) and compare channel conversions across our different markets (Comparative + Key Segments). If you think about it, these 8 blocks alone cover almost everything you could initially expect from a business.
Then, of course, for each of these typologies, we’ll need to develop different specific techniques. The important thing is to have the resources to address different types of questions and gain a holistic understanding of your data.
How to navigate the IKAUE analysis type matrix
The analysis progresses from top to bottom (descriptive to comparative) and from left to right (major metrics to key segments). This path allows us to go from the general to the specific, starting with a global understanding of the business and gradually delving deeper into the data analysis. It’s as if we were zooming in on the information, starting with a panoramic view and getting closer and closer to the details that interest us as we discover information about them. If you prefer, you can think of it as playing a gambler data board game, where you have to reach your goal, but to do so, you’ll have to cross the previous boxes.
The early stages provide context and general understanding. They allow us to familiarize ourselves with the data, identify key metrics, and establish a solid foundation for further analysis. In this initial phase, the goal is to gain an overview of the landscape, understand key trends and patterns, and identify potential areas of interest for further analysis. As you progress, you gain deeper insights and can delve deeper into the analysis of specific patterns, trends, and segments.
It ‘s important to know when to stop. Data analysis isn’t an end in itself, but a tool for taking clear action. The goal is to gain insights, not to generate noise or get lost in a sea of data. Once you’ve achieved the understanding necessary to make a decision about a problem or situation, you don’t need to continue analyzing or moving through the matrix. The key is finding the balance between depth of analysis and efficiency—that is, obtaining the greatest amount of relevant information with the least possible effort .