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Guide to modern Business Intelligence Tools

Estimated read time – 2 min

In our new series, we will try to give a detailed representation of  several BI tools using the SuperStore Sales dataset. The data in SuperStore Sales reflect sales and profit of the retail chain in US dollars.

In the upcoming blog post, we will discuss a real problem statement that could arise when creating a dashboard based on the SuperStore Sales data and design a functional layout to provide clear answers. Throughout this task, we’ll stick with a predefined set of colors to make the comparison more unbiased.

Next, we’re going to create a dashboard that would assist in data-based decision-making with each of the BI tools. We also plan to involve industry experts to learn from their experience.

A complete list of BI systems and tools to be tested in our experiment is provided below. I want to welcome everyone who is willing to help us in solving this challenge to message me on Telegram  – @valiotti. I will be glad to hear from you. Although it’s a non-profit project, it’ll be really useful for the open-source community.


We plan to cover the following list of tools:

Free Open Source:

  • Metabase
  • Redash
  • Apache Superset
  • Dash / Plotly

Free Cloud-Based:

  • Google Studio
  • Yandex Datalens

Paid Cloud-Based:

  • Mode
  • Cluvio
  • Holistic
  • Chartio
  • Periscope
  • DeltaDNA
  • Klipfolio
  • Count.co


  • PowerBI
  • Tableau
  • Looker
  • Excel
  • Alteryx
  • Qlik Sense
  • Qlik View

The final goal is to evaluate the BI tools against the following criteria:

  • learning curve of BI tool (1 — too hard to learn, 10 — easy)
  • tool functionality (1 — very poor functionality, 10 — multifunctional)
  • ease of use (1 — very inconvenient, 10 — super convenient)
  • compliance of the result (1 — far from the designed layout, 10 — too close to the designed layout and objective)
  • visual evaluation (1 — poor appearance, 10 — great visual appearance)

An integral weighted score for each tool will be calculated based on the internal estimates.

The results will be posted to our Telegram channel @leftjoin_en and followers will also be able to share their thoughts on the experiment.
By the end, each tool will be represented as a point in the plane, which will be divided into 4 parts.

This article will be updated with links and ratings as we new posts come out.

 No comments    112   8 mon   BI-tools   excel   looker   powerbi   redash   tableau

Diagram of BCG (Boston Consulting Group) Matrix

Estimated read time – 5 min

I will water down the blog with an interesting report, that was developed for Yota company on November, 2011. BCG Matrix has inspired us to develop this report.

We had: one Excel package, 75 VBA macro, ODBC connection to Oracle, SQL queries to databases of all sorts and colours. We will review report construction within this stack, but first, let’s speak about the very idea of the report.

BCG Matrix – is 2x2 matrix, whereon the clients’ segments are displayed by circumferences with their centres in the intersection of coordinates, formed by the relevant paces of two indicators selected.

To make it simple, we had to divide all the clients of the company into 4 segments: ARPU above average/below average, traffic consumption (main service) above average/below average. Thus, it turned out that 4 quadrants appear, and you need to place a bubble chart into each one of them, whereas the size of a bubble means the total amount of users within a segment. In addition to that, one more bubble was added to each quadrant (smaller one), that showcased the churn in each segment (author’s improvement).

What did we want to get at the output?
A chart of the following type:

Representation of the BCG matrix on the data of Yota company

The task statement is more or less clear, let’s move to the realization.
Let’s assume, that we’ve already collected all the required data (meaning that, we’ve learned to identify the average ARPU and average traffic consumption, in this post we won’t examine SQL-query), then the paramount task lies in understanding how to display the bubbles in the required places by means of Excel tools.

For this aim, a bubble chart comes to help:

Insert – Chart – Bubble

Going to the menu Selection of data source and evaluating, what is required in order to build a chart in the type that we need: coordinates X, coordinates Y, values of bubbles’ sizes.

Great, so it turns out that if we assume that our chart will be located in coordinates on the X axis from -1 to 1, and on the Y axis from -1 to 1, then the centre of the right upper bubble will be the spot (0.5; 0.5) on the chart. Likewise, we’ll place all the other bubbles.

We should separately consider the bubbles of Churn type (for displaying of the churn), they are located more to the right then the main bubble and might intersect with it, therefore we will place the right upper bubble to empirically obtained coordinates (0.65; 0.35).

Thus, for four main and four additional bubbles, we can organize the data as follows:

Let’s review more thoroughly how we’ll use them:

So, we set on X-axis – horizontal coordinates of the centres of our bubbles, that lie in the cells A9:A12, on Y-axis – vertical coordinates of the centres of our bubbles, that lie in the cells B9:B12, and the sizes of the bubbles are stored in the cells E9:E12.
Furthermore, we add another data set for the Churn, once more indicating all the required parameters.

We’ll get the following chart:

Then, we’re making it pretty: changing colours, deleting axis and getting a beautiful result.

By adding the required data labels, we receive what we initially needed in the task.

Share your experience in comments – did you build such charts and how you solved the task?

 No comments    283   2019   analysis   data analytics   excel   marketing   sql   strategy   visualisation