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Mining Insights from Data

Knowledge discovery within databases, weather singular or multiple, is about sifting through massive datasets to uncover patterns, trends, and other truths about data that aren’t initially visible using machine learning, statistics, and database systems.

Some of the basic tools for knowledge discovery do much more in developing insights and strategic decisions, these are such as understanding significance and variance, finding dominant variables or looking for similarities, the following are basic tool in data analysis but they are vital in insights generation.

One way ANOVA – Do you want to know if your brand is unique or differentiated, based on various attributes? Using Tukey’s test, we can tell if your offer exclusive, and what makes it so.

Principal Component Analysis Do you have multidimensional data set with various quantitative variables (e.g., customers height, weight, time spend in your site, age, marital status) and you would like to summaries the information? We can make you understand how customers group, and which attribute best explains their behavior, e.g., repeat purchase.

This can be used to strategically optimize marketing campaigns and target our customers better.

Multiple Linear Regression – Would you like understand your customers / clients / participant better, like what may influence them to act in a certain way, for example, what attribute influences amount of purchase? Multiple linear regression will help you know which attribute and by how much. This would help in investment decisions on ambiance (simpler term) product features after sales service etc.

Basic Descriptive statistics – Would you want to describe your subjects / customers quickly and in a pictorial manner? Descriptive statistics will help you to better identify the characters of your subjects and detect potential association between the different attributes.

Correlation test Do you wish to understand the relationship between different characteristics in your data? Correlation will help to strategically understand what would be affected if one attribute is changed.

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