## Data analysis basic exercise (comparison and graphing)

### How to find the insights from data

Business data analysis class started again at Yokohama National University.

The class is designed mainly for the 1st and 2nd year international students to give business data analysis (problem-solving) skills.

We focused on some basic techniques and thinking process to process raw data to get some insights from the data.

This was one of the exercises we used in the class:

The students were required to make their own “conclusion (not just findings nor analysis results) with Excel.

In this case, the output (goal) parameter is “frequency of visit”, while the input (explanatory) parameters are (i) Gender and (ii) Direct Mail (D/M) received or not.

In order to get a first insight from the data, you may compare the data on the each parameter (in this case, gender and D/M received). Let’s look at examples of the outputs:

In order to simplify the original data, averages are used in the upper matrix.

The following bar charts show the comparing results with the two parameters (gender and D/M).

Which parameter is more critical to the goal? Look at the significance of the difference!

(D/M received) has more gap between “YES” and “NO”, rather than one between the genders.

So you may focus on the D/M effects more than gender difference. (Let’s call it “Key parameter“)

Then you may dig into the D/M effects to find more detail (by splitting by gender).

This is an example of a conclusion drawn from the chart above:

” Direct Mail affects the frequency of the visits, while only female customers have such effect. Therefore, my proposal is to keep sending D/M to the potential female customers to get more visitors”

Again, here are the points to learn from the exercise:

• How to find the key parameter to dig into further?

=> Find the parameter with “larger gap” which indicates more critical factor to the goal/issue.

•   What to do once you find the key parameter?

=>Split the data with another parameter to dig into the “Key parameter”

## [Business data visualization exercise at YNU]

This was an exercise at my biz data analysis class in Yokohama National University for the international students.

The topic of the class was “Data visualization/illustration”. After learning some basic types of graph such as bar chart, line chart, pie chart, Histogram, scatter diagram etc., they tackled the following question:

Assuming that you are required to effectively illustrate the sales results data and report your conclusion to the boss.
Data is quite simple but not easy to find an effective way to summarize the data.

(1) Determine your final key message to report to boss

(2) Study the best way to summarize the data to fit the message.

In my class, the students discuss thier own outputs on how they can improve it.

Some examples are:

You can compare the results by product to identify the best selling product and where is the hot area.

This is an “area-wise” view with product-wise breakdown. You can identify strategic potential area and which product may fit the market.

This simply shows the ratio by product type. No detail about the area but it delivers which products are selling more in a very simple way.

This is a good exercise to have practical “data managing” skills which will enhance your business competitiveness.

## Six questions to check the data utilization in your organization

2. Can you explain the current business situations to others in an objective way? YES/NO?

3. Can you identify the root-cause of a problem(s) when happening, not just relying on what you physically see? YES/NO?

4. Can you evaluate the performance of your operations/business with appropriate indicators(KPIs)? YES/NO?

5. Can you connect the evaluation and actions in an effective and logical way? YES/NO?

6. Can you systematically respond to the changes in the markets and estimate the impact to your business, not just following your past experience? YES/NO?

The number of “YES” out of the six questions indicates your organization’s data utilization and data literacy.

## What you need to consider when drilling down the data.(vol.3)

The last factor to consider when drilling down the data is “data availability“.

It seems so natural to care about its availability but it is extremely important and practical to think of it before studying what axis you apply for the drill-down.

You may be able to consider any ideal axis to break the data but you may end up with giving up the idea during the analysis. In reality you cannot obtain all the data you need, especially if you are not an analyst or do not belong to such specialized organization for analysis who can afford to procure the data from outside, you have to manage/struggle to reach required outputs only with already existing (i.e. available) data at hand.

Before you notice the fact such data does not exist at hand at the end of the analysis, you should consider whether you have the data available to apply for the axis you choose.

My suggestions at my lecture and trainings are always very practical as above. They will help you save your time and efforts when you analyze business data.

## What you need to consider when drilling down the data.(vol.2)

There is always a gap between theory and reality.

A theoretically correct idea is not necessarily a right answer in reality. You may observe the same issue in the data analysis.

When you drill down or breakdown a set of data by some axis (age, area, product etc.), you may come up with lots of options of axis. Even if it is theoretically possible, I would suggest that you should make sure that the axis you select will lead to some effective action(s).

For example, if you try to categorize the sales results by the weather (sunny, rainy or cloudy), you may able to technically do it. But it would not help you determine the actions you take for sales improvement because you cannot control the weather anyway.

If you use some axis by which you drill down some data, you may consider whether the axis is actionable or not. This is extremely important from the practical viewpoints.

If you select “age” axis for your analysis, then you may reach some actionable ideas on how to improve the sales from a certain category of age( eg. 20’s).

In business, “actionable” is always a priority.

the axis.

## Data analysis training for a public sector

I had a training program for one of the public sectors in Japan yesterday.

The mayor of the city has a tremendous sense of crisis on the current and future management of the city. He is eager to engage in the human skill development to improve the value and quality of the service and management.

The mayor directly asked me to support his initiative to develop the skills in the organization and it was the first step to have a basic training session.

As it was the initial session, I focused only on the very fundamental but essential topics such as:

– How to define an issue and develop hypothesis for the issue

– How to utilize basic analytical tools to describe the whole picture of the issue and identify the potential issue point(s)

I will continue my support in order for them to acquire those skills/proficiency in the organization.

I am so excited to be involved in such critical initiative and happy to contribute to the enhancement of the people.

Link to my website (in Japanese)  => http://data-story.net

## Final session of “Modeling with statistics” class

It was the last session of my class today at Yokohama National University.

The final exam report was announced with the following requirements:

It was extremely wonderful experience to me and it was really fun!

My goal of this class has been how to solve real and practical issues in business, by developing a logical story with data (analysis).

I  the students enjoyed the class and will see them in the coming semester again for the class “Logical problem-solving”, in which I will talk about “Logical thinking to reach an agreement with stakeholders” !

I am so excited again. Please contact me if you need a lecture (one-day, short-term intensive or long-term) of decision science even outside of Japan.

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