Advanced Data Exploration

Together with dynamic web charts and business analytics, we are also experts in advanced data exploration using tools such as Tableau Software.

With Tableau we can create data visualisations that help you explore your data dynamically with a detail you cannot even imagine. Once we’ve created the visualisation that best fits your needs, you can either receive the data ready to be explored and manipulated with Tableau (if you already know and use it) or access them with a free Tableau reader or we can help you in using the most appropriate technology for your existing environment.

Below you can have a hint of the power of Tableau in terms of advanced data exploration.

Advanced data exploration

Advanced data exploration

This company has lost sales in the last four years. While still healthy, margins have suffered.

Domestic sales seem to have lost the most. Premium products seem to be under pressure.

 

 

Advanced data exploration

Advanced data exploration

The bubble size is proportional to sales, its color shows profitability.

Premium and discount lines are losing ground vs other categories

 

 

Advanced data exploration
This visualisation tries to map the relationship between sales volumes and margins. In this example, products that sell more tend to have higher percentage margins.

Filtering by customer category helps in isolating noise due to channel issues.

 

 

Advanced data exploration

Advanced data exploration

Higher selling products tend to have higher percentage margins.

As time passes, the number of high selling lower margin products tend to go up.

 

 

Advanced data exploration

Advanced data exploration

How much of the change in sales is due to new products, to old products that are not sold any more, and to old products that still enjoy success in the market?

 

 

Advanced data exploration

Advanced data exploration

The box plot is a convenient way of graphically depicting groups of numerical data through their five-number summaries: the smallest observation (sample minimum), lower quartile (Q1), median (Q2), upper quartile (Q3), and largest observation (sample maximum).

In this example, the distribution of the yearly product sales by customer is depicted.

For domestic resellers, the median value of the yearly orders of a product was about 350 euros. 99% of the yearly product orders by customer are under 12500 euros.