Datasea Intelligent Technology (DTSS) Enterprise Value (2018 - 2025)
Datasea Intelligent Technology's Enterprise Value was $5.91 million in fiscal Q2 2026 (quarter ended Dec 31, 2025), down 63.3% from $16.13 million a year earlier and down 62.9% from the prior quarter.
Datasea Intelligent Technology (DTSS) Enterprise Value (2018 - 2025) Analysis & Trends
On a trailing twelve-month basis, Datasea Intelligent Technology's Enterprise Value was $3.68 million through Dec 31, 2025, down 75.4% year-over-year; for FY2025 (ended Jun 30, 2025), it came in at $12.19 million, up 6.6% from FY2024.
- Enterprise Value shows a five-year compound annual growth rate of -21.9% (FY2020 to FY2025).
- In earlier fiscal years, Enterprise Value was $11.43 million in FY2024 (-55.0%), $25.41 million in FY2023 (-34.2%), $38.63 million in FY2022 (-27.6%) and $53.4 million in FY2021 (+27.4%).
- The fiscal Q2 2026 figure marks the lowest quarterly Enterprise Value since fiscal Q1 2024.
- Compared with a year earlier, Enterprise Value was higher in four of the last eight quarters, with growth averaging 11.1%.
- The best year-over-year quarter for Enterprise Value over five years was fiscal Q2 2025 (growth of 90.8%); the worst was fiscal Q1 2024 (a decline of 86.3%).
- Per Business Quant data, DTSS's Enterprise Value in the three fiscal quarters before Q2 2026 was $15.92 million (Q1 2026), $12.19 million (Q4 2025) and $15.73 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Infosys | 42.66 Bn | 42.72 Bn | 1.60 Bn |
| 2 | Cognizant Technology Solutions | 25.65 Bn | 18.82 Bn | 1.83 Bn |
| 3 | Td Synnex | 20.51 Bn | 14.54 Bn | 1.34 Bn |
| 4 | Cdw | 16.33 Bn | 14.32 Bn | 1.32 Bn |
| 5 | Cgi | 15.07 Bn | 12.85 Bn | - |
| 6 | Arrow Electronics | 11.72 Bn | 10.75 Bn | 1.13 Bn |
| 7 | Avnet | 8.33 Bn | 7.51 Bn | 865.03 Mn |
| 8 | Ingram Micro Holding | 6.23 Bn | 1.84 Bn | 958.68 Mn |
| 9 | EPAM Systems | 5.55 Bn | 1.19 Bn | 429.57 Mn |
| 10 | Datasea Intelligent Technology | 8.93 Mn | 6.01 Mn | 1.19 Mn |
Historic Data
| Date | Value |
|---|---|
| Dec 31, 2025 | 5.91 Mn |
| Sep 30, 2025 | 15.92 Mn |
| Jun 30, 2025 | 12.19 Mn |
| Mar 31, 2025 | 15.73 Mn |
| Dec 31, 2024 | 16.13 Mn |
| Sep 30, 2024 | 9.13 Mn |
| Jun 30, 2024 | 11.43 Mn |
| Mar 31, 2024 | 23.11 Mn |
| Dec 31, 2023 | 8.45 Mn |
| Sep 30, 2023 | 4.84 Mn |
| Jun 30, 2023 | 25.41 Mn |
| Mar 31, 2023 | 29.57 Mn |
| Dec 31, 2022 | 36.39 Mn |
| Sep 30, 2022 | 35.20 Mn |
| Jun 30, 2022 | 38.63 Mn |
| Mar 31, 2022 | 65.79 Mn |
| Dec 31, 2021 | 35.08 Mn |
| Sep 30, 2021 | 44.31 Mn |
| Jun 30, 2021 | 53.40 Mn |
| Mar 31, 2021 | 69.53 Mn |
Datasea Intelligent Technology Enterprise Value 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=enterprise-value&ticker=DTSS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "enterprise-value", "ticker": "DTSS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=enterprise-value&ticker=DTSS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();