Cleansing HR data contributes to better decision-making and better discussion across different teams

Interviewee

Mr. Issei Hirohata

Recruitment Planning Division, Human Resources Department, Hoosiers Holdings Co., Ltd.

Interviewer

Jumpei Kato

Customer Business Partner, Panalyt Inc.

Table of contents

• Introduction of Panalyt improved data accuracy
• Accurate data visualization help better management decisions
• Democratization of HR data also activates cross-functional discussions

Introduction of Panalyt improved data accuracy

Kato:

Can you tell me how you and your team managed the HR data before implementing Panalyt and what kind of challenges you experienced?

Hirohata:

We have a few thousands employees, yet we are still managing the HR data in Excel which caused a lot of mistakes. We did not know whether the HR data was accurate. We were concerned that we may be making an important decision based on the inaccurate and uncertain HR data.

Kato:

You said that you did not know whether the data was correct or incorrect, but how did you do data cleansing (e.g., data correction and/or data quality check) before?

Hirohata:

Just manual effort. We have a structure where certain members input the HR data and the other members check the data accuracy and those are done manually. If the person in charge makes a mistake and if we are lucky enough to find it, we will correct it.

Kato:

HR data often contains errors. When we performed data cleansing for Hoosiers through Panalyt App, we found that there were several employees who had no termination date and are disappeared from the HRIS data. Also, Hoosiers holding is composed of multiple subsidiary organizations which sometimes use different definitions of employee categories and have different HRIS datasets for regular and contractual employees. This made it very difficult and complicated to integrate and organize those datasets. 

Hirohata:

Once we were able to use Panalyt to accurately grasp the data, we were able to notice errors in the total number of employees.

Although we were careful not to make any mistakes, such as the number of full-time employees, there were cases where termination dates, gender, employee categories, etc were incorrecly recorded. But, by using Panalyt, we were able to correct the data.

Accurate data visualization leads directly to better management decisions

Kato:

What kind of impact and values did Panalyt deliver especially on the management level?

Hirohata:

Since the attrition rate is finally displayed more accurately, we analyzed the past attrition rates and the characteristics of those leavers. This caught the attention of the board members and raised a discussion on the HR policies to improve the attrition rate.

For example, we were originally planning to implement a new policy in the next few years to improve the female manager ratio. However, since the female attrition rate is worse than the original expectation, we have decided to implement this policy as soon as possible. 

In addition, we found that some departments actually have better attrition rates and the younger generation’s attrition rates have been increasing in the past few years. Thus, we have decided to provide more training opportunities especially for young employees to learn and communicate with each other across different teams.

Kato:

It is impressive that you uncovered that some assumptions were false, and then this findings were translated into actions such as promoting a better work environment for women.

Calculating the attrition rate is very complicated. It is relatively easy to do it for an entire organization, but to look at it over time, by organization, by gender, by position, etc., requires advanced calculations and knowledge. I think it is one of Panalyt’s strengths to be able to do it quickly.

Democratization of HR data also activates cross-functional discussions

Hirohata:

We are planning to use Panalyt for the monthly HR report currently handled by Corporate Planning.

Kato:

Are Corporate Planning and HR collaborating together often?

Hirohata:

Actually, the communication between us has not been very effective.

We couldn’t achieve the quality HR data at the level Corporate Planning requires and couldn’t communicate why this is happening and how to improve. Thanks to Panalyt, although we have not yet reached the level required by Corporate Planning, we are now able to discuss with them on how we are cleaning up the HR data, how we define each employee attribute and HR dataset, and how we can improve the data quality in general. I am glad that we are moving forward and the number of people involved has been increasing. 

As communication has become more active across different teams, those members who didn’t  understand or were not interested in HR data before started realizing the importance of HR data and the difficulty of data cleansing.

Kato:

I am glad that Panalyt is being a catalyst for improving relationships and communications within the organization.

I think that better communication will lead to better ideas to find insight and to improve the current situation.

Hirohata:

We would like to continue discussing with Panalyt and hope to stimulate discussions in the use of data within the company. We feel that Panalyt not only provides tools, but also supports us in the overall data cleansing process, such as organizing data and finding errors

Kato:

If you have any problems with HR data analysis, please contact us for anything.

Thank you for taking your time for the interview today.