Many discussions are going on about the “data analysis” and/or ” data application to business fields”.
However, their definitions and scopes are not necessarily identical, which causes lots of confusions and misunderstandings.
One of the biggest gaps which are NOT recognized in general would be the difference between “Data science” and “(practical) Data analysis”.
In order to clarify the overall picture of the “Data Analysis” world, you can see the following MAP:
Before touching the actual data itself, you need to identify which category are you addressing when you say “Data Analysis”.
It depends on your ultimate goal to achieve with the data.
If you are NOT a data analyst nor a data analytics expert and want to simply apply some basic data analysis for your business problem solving, then you do NOT need to learn “Machine learning” for instance.
What you need would be how to design the problem-solving process (Analysis Design) and how to apply the basic data analysis (mostly by Excel) for your issue(s).
The biggest issue I have observed at my client’s business offices is the fact that many people do not even”Data analysis” but do only “Data arrangement”.
This is the reality, which is far away from the “Data Science” world.
I am helping those “usual” clients to improve their business problem solving skills using “Data Analysis” techniques (not Data Science), as it is what many people need and contribute to thier immediate issues.
I hope this may clarify confusions and help you identify what you should learn and/or apply.
Here is a part of my presentation in the “data analysis” seminar.
I always emphasis the significance of the right approach to a problem when you apply “data analysis” for solving it.
I found that many people struggled to find effective solutions based on the data especially when they started with analyzing the data without properly defining/formulating the problems and making hypothesis. Even if you find something from data, it might not be effective enough to solve the fundamental problem you have.
I call the necessary part in the problem-solving process as “ANALYSIS DESIGN”. My training programs all focus on the skill sets to design the analysis (i.e. problem-definition/formulation and hypothesis making) so that they can find a “right” solution.
This is something you should learn before learning the methodology of data analysis and/or difficult theory of statistics if you want to obtain the analytical skills to apply for business problem solving.
Also it is an important skill in the AI(Artificial Intelligence) era for many business persons as analysis itself can be already done by machines.
I have programs to train business persons and university students on this subject.
One of my best-selling book translated into Vietnamese has been released.
I believe that the book is quite useful and effective for your business application/ problem solving etc.
Please find the practical techniques and thinking process in the book.
If you have any inquiries about my training programs, please let me know.
This is a very interesting presentation as the final exam of my “Business statistics” class at Yokohama National University in Japan.
My classes are all for international students and this is work of a student from Vietnam.
The students learned how to set a practical goal and how to effectively use some analytical techniques to support the conclusion(s).
The student tried to find out the difference of the students from two different countries, Vietnam and Japanese in terms of GPA and objectives to learn at university.
While the sample size is small and the conclusions are not necessarily surprising, the approach and analysis itself was quite interesting.
My class is not just to teach some academic analytical techniques but to teach how to apply those techniques to meet practical goals.
I would be happy to give my lecture anywhere in the world.
This is an assignment of another class “Business data analysis” at Yokohama National University.
Students are required to develop their own story, supported with the analyzed data.
This is the key skill-set when you apply those analytical techniques to the practical business issues/proposals.
We have already experienced several similar exercises at the class and I am so excited that they will enjoy it and present interesting insights.
Please send me an email if you / your organization would like to try those lessons/lectures.
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.
What is the answer? No single “right answer” here. It all depends on your final message.
This is a good exercise to have practical “data managing” skills which will enhance your business competitiveness.
Would you like to try the lesson? Please contact me.
My business statistics class has started at Yokohama National University.
The first exercise of the class today was making a summary of the customer inquiry data at a shopping mall.
The students (international students from 12 countries) were required to make a brief report of the data with the following consideration:
(1) Which category is the most critical, Gender, Age or type of shop?
(2) At which level do you need aggregate the original data to find the insights effectively?
(3) What is the conclusion rather than calculation results?
You do NOT have to rely on any advanced statistical techniques rather you can use simple tools such as TOTAL, AVERAGE, PERCENTAGE etc.
This is the first step for the students who want to manage business data effectively. They were really excited about the exercise.
Enjoy and study hard!