[Data Analysis from Scratch #3] Key points for data analysis beginners to know about communication to get management involved

When conducting data analysis in a company, it is actually quite common to hear cases where people say, "Even though we did the analysis, it was rejected by management and scrapped..."

To date, I have assisted over 100 clients with data analysis, helping them get management involved and taking action.

In this article, I will explain the key points I have learned from my experience when communicating with management in order to turn data analysis into action.

↓ Click here for a list of articles on "Data Analysis from Scratch"

#1 "8 steps of analysis" that beginners should know first
#2 Three analytical techniques that data analysis beginners should remember
#3 Communication tips to get management involved that data analysis beginners should know
#4 What data analysis beginners need to know: What management expects from data analysis and analysts
#5 4 tips for field staff who are new to data analysis to get management and the organization involved

Why your analysis is useless

The reason why data analysis gets abandoned halfway through is actually simple.

The analysis includes8 basic stepsIf there is a discrepancy in any of these steps, it will be difficult to translate the analysis into decision-making and action.

Previously publishedThis articleSo, we have introduced the eight basic steps of analysis.

First of all,Is the data analysis you are currently working on consistent throughout all steps?It is important to check carefully.

If management were to decide on an action based on incorrect analysis results, it could have a negative impact on business operations.

Be aware that data analysis involves a process, and carefully check that there are no inconsistencies in the analysis process.

After going through the data analysis process, the difficult part isInvolvement of management and organization.

The ultimate goal of data analysis is to take action based on the analysis that has a high probability of success.To take action, it is essential to have go-ahead from the relevant departments and management.

So, what kind of difficulties lie in the stage of getting management and the organization involved?

Difficulty in getting management involved

The difficulty in getting management and organizations involved isThe timing of communicationIt can be said that this is the case.

Some of you may be thinking, "I can't propose this to management until I have perfectly completed every step of the data analysis process."

However, this is a big mistake.

The key to getting management and the organization involved is:"Communicate appropriately with management and key personnel at the optimal time"We'll take a closer look at this in the next chapter.

Key points for communicating with management

Data analysis involves risks.

that isBeing seen as something done by a third partyIf you are seen like that, you will not be able to get the management involved.

Data analysis efforts,It is necessary to get management and members of other departments to make it their own business.

To overcome this, there's a teaching called "Getting management and key people in other departments to use their own brains"It is important to get your own brain moving. When you get your brain moving, you will feel like you have taken an active role in the data analysis, and it will be much easier to get the rest of the organization involved.

Even if the results of data analysis are suddenly presented, the management will not be able to grasp the context. It will take time for them to understand and accept it, making it difficult to get them involved.

However, this doesn't mean it's realistic to share progress frequently.

The important thing isShare information appropriately at the right time and proceed with consensus.

Two points on which management should agree

In order to get management to "make data analysis their own business," there are two points you must reach agreement on.

① After hypothesis design
② After interpretation of the analysis results

These three points.

The reason why we need to reach an agreement on these two points is because, out of the eight steps in data analysis,When it comes to personal matters, it is easy to think of "hypothesis" and "interpretation."That's why.

If agreement can be reached at this time, data analysis will become even more useful to management."My Projects"It will become like this.

① Get consensus on the "hypothesis"

Of the eight steps in data analysis, steps 8 to 1 can be done with relatively little effort. Also, it is only from step 3, the data collection stage, that cooperation from other departments, such as asking for data provision, is required.

So,Once you have progressed to "Step 3: Hypothesis," share your objectives, challenges, and hypotheses with your management team and make sure you align them.

■ Objective: What do you want to achieve?
Example: "I want to maximize sales" "I want to make my new business a success"

■ Problem: What do you want to solve?
Example: "Low number of visitors to listing ads" "Low number of new customers"

■ Hypothesis: What factors are causing the problem?
Example: "The reason for the increase in sales is probably ____" "The reason for the decrease in the number of registered members is probably ____"

By getting everyone to agree on the direction of the analysis before collecting the data, you can avoid mistakes such as collecting incorrect data based on inaccurate hypotheses.

When operating as an organization, trust is extremely important. If the company uses money to collect inaccurate data, it will destroy trust.

So make sure you come to an agreement at this point.

② Reach consensus on the "interpretation"

The results of data analysis are just a series of numbers. You must interpret those numbers and decide what action to take.

In fact, there are two kinds of interpretations.

1) My interpretation
My own interpretation of the analysis results

② Interpretation of yourself and key people
Interpretations considered together with key people

In "Step 6: Interpretation," we review whether the analysis so far has been consistent. We compare the "assumed hypothesis and analysis results" with the "actual analysis results" to confirm whether the hypothesis is verified.

If the hypothesis is correct, then think about what specific actions you can take to solve the problem or achieve your goal.

Once you have your own interpretation, before proposing it to management,It is a good idea to take the time to discuss the interpretation together with a key person who you know will be listened to by management.

You should already know who the key people you need to involve in order to take action (your superiors or people from other departments) are, so it's a good idea to target them from the start.

By thinking about the interpretation together with key people,Personal matters for key peopleAs well as becomingPersonal matters for managementIt also becomes easier to become.

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In conclusion

In this article, we have explained two points on which management should agree as tips for communicating with them regarding data analysis.

In order to run a project dynamically and take action based on data analysis, it is key to get management and key people involved.

I hope you will keep the two points introduced in this article in mind when doing your data analysis.

↓ Click here for a list of articles on "Data Analysis from Scratch"

#1 "8 steps of analysis" that beginners should know first
#2 Three analytical techniques that data analysis beginners should remember
#3 Communication tips to get management involved that data analysis beginners should know
#4 What data analysis beginners need to know: What management expects from data analysis and analysts
#5 4 tips for field staff who are new to data analysis to get management and the organization involved

↓ The business media "PIVOT" explains how to utilize data science in business.

Yoshiaki Hirao, CEO of XICA Corporation 

After his father's bankruptcy, he strongly wanted to "eliminate the hopeless sadness in the world." He encountered statistical analysis while studying at the Faculty of Policy Management at Keio University, and founded XICA Co., Ltd. in February 2012, just before graduating. Before founding the company, he also had a unique career as a band member.