Datasection

Datasection Operating Expenses

The Total Operating Expenses of Datasection (3905.T) as of Oct 10, 2026 is 195.00 M JPY. In the previous year, Total Operating Expenses was 125.00 M JPY — a change of 56.00% (higher).

Operating Expenses

195.00 MJPY

YoY

56.00%

Last updated:

Total Operating Expenses of Datasection is 2025 195.00 M JPY. Total Operating Expenses of Datasection was 2024 125.00 M JPY. It decreases by 56.00% higher compared to the previous year.

The Datasection Operating Expenses history

  • 3 Years

  • 10 Years

  • 25 Years

  • Max

Operating Expenses
Date
Operating Expenses
Jan 1, 2018
265.22 M JPY
Jan 1, 2019
294.48 M JPY
Jan 1, 2020
82.05 M JPY
Jan 1, 2021
85.20 M JPY
Jan 1, 2022
75.00 M JPY
Jan 1, 2023
114.00 M JPY
Jan 1, 2024
125.00 M JPY
Jan 1, 2025
195.00 M JPY
The Datasection Operating Expenses history
YEAROperating ExpensesYoY
195.00 MJPY+56.00%
125.00 MJPY+9.65%
114.00 MJPY+52.00%
75.00 MJPY-11.97%
85.20 MJPY+3.85%
82.05 MJPY-72.14%
294.48 MJPY+11.03%
265.22 MJPY+10.30%
240.45 MJPY+19.55%
201.14 MJPY+16.46%
172.70 MJPY-10.13%
192.17 MJPY—
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Datasection Stock analysis

What does Datasection do? Datasection is one of the most popular companies on Eulerpool.

Frequently Asked Questions about Datasection stock

Total Operating Expenses of Datasection is 195.00 M JPY in 2025.

Total Operating Expenses of Datasection changed from 125.00 M JPY to 195.00 M JPY, representing a 56.00% change. The value is higher than the previous year.

On Eulerpool you can find the complete historical development of Total Operating Expenses Datasection since 2006 – with annual values, charts, and detailed analysis.

Operating expenses are the costs incurred in the normal course of business operations, including COGS, SG&A, R&D, and depreciation.

Total Operating Expenses's JPY is a key factor for investors. Changes in this metric can signal improving or deteriorating fundamentals, directly impacting the stock price. On Eulerpool, you can track Total Operating Expenses's Datasection historically and in real time.

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