Data I (DAIO) Operating Expenses (2010 - 2026)
Data I (DAIO) reported Operating Expenses of $4.75 million for Q1 2026, up 33.3% from $3.57 million a year earlier and up 12.7% from the prior quarter.
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Analysis
Data I (DAIO) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Mar 31, 2026, Data I's Operating Expenses came in at $16.9 million, up 19.6% year-over-year; for FY2025, it was $15.71 million, up 7.3% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 2.5% (FY2020 to FY2025).
- By year, Operating Expenses came in at $14.64 million in FY2024 (-7.0%), $15.74 million in FY2023 (+12.7%), $13.96 million in FY2022 (-6.9%) and $14.99 million in FY2021 (+7.9%).
- The Q1 2026 figure ranks as the highest quarterly Operating Expenses since Q4 2012.
- Year over year, Operating Expenses has now increased in each of the last four quarters, with growth averaging 5.3% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q1 2026 (growth of 33.3%); the low point was Q2 2024 (a decline of 21.1%).
- Per Business Quant data, the three quarters before Q1 2026 came in at $4.22 million (Q4 2025), $4.13 million (Q3 2025) and $3.8 million (Q2 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Apple | 5,040.97 Bn | 4,788.46 Bn | 54.77 Bn | 19.08 Bn |
| 2 | Cisco Systems | 424.45 Bn | 360.38 Bn | 11.06 Bn | 6.80 Bn |
| 3 | Dell Technologies | 313.76 Bn | 269.51 Bn | 9.83 Bn | 4.45 Bn |
| 4 | Arista Networks | 232.68 Bn | 186.14 Bn | 1.91 Bn | 532.30 Mn |
| 5 | Sandisk | 202.67 Bn | 191.19 Bn | 7.58 Bn | 545.00 Mn |
| 6 | Seagate Technology Holdings | 152.82 Bn | 147.82 Bn | 1.90 Bn | 2.07 Bn |
| 7 | Sony | 149.85 Bn | 87.11 Bn | 6.53 Bn | 14.83 Bn |
| 8 | Western Digital | 130.59 Bn | 118.72 Bn | 2.03 Bn | 465.00 Mn |
| 9 | Hewlett Packard Enterprise | 77.78 Bn | 55.65 Bn | 4.93 Bn | 10.82 Bn |
| 10 | Data I | 24.65 Mn | 24.65 Mn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2026 | 4.75 Mn |
| Dec 31, 2025 | 4.22 Mn |
| Sep 30, 2025 | 4.13 Mn |
| Jun 30, 2025 | 3.80 Mn |
| Mar 31, 2025 | 3.57 Mn |
| Dec 31, 2024 | 3.99 Mn |
| Sep 30, 2024 | 3.25 Mn |
| Jun 30, 2024 | 3.32 Mn |
| Mar 31, 2024 | 4.08 Mn |
| Dec 31, 2023 | 3.81 Mn |
| Sep 30, 2023 | 3.58 Mn |
| Jun 30, 2023 | 4.21 Mn |
| Mar 31, 2023 | 4.13 Mn |
| Dec 31, 2022 | 3.41 Mn |
| Sep 30, 2022 | 3.40 Mn |
| Jun 30, 2022 | 3.49 Mn |
| Mar 31, 2022 | 3.66 Mn |
| Dec 31, 2021 | 3.65 Mn |
| Sep 30, 2021 | 3.95 Mn |
| Jun 30, 2021 | 3.73 Mn |
API Access
Data I Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=DAIO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DAIO", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=DAIO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();