Data Patterns (India) Limited (DATAPATTNS) Fair Value & Analysis
Industrials · IN · Market cap ₹252B
Fair value as of: Jun 29, 2026
Analysis
Data Patterns (India) Limited (DATAPATTNS) currently trades at ₹4,507, while our model-based Fair Value estimate is ₹884.26 — implying the stock looks roughly 80.4% overvalued today. We read business quality at 97/100 (high quality), in the Industrials sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).
About the company
Data Patterns (India) Limited provides defense and aerospace electronics solutions in India. The company engages in the manufacturing of measuring, testing, navigating, and control equipment; and air and spacecraft and related machinery, as well as provides technical testing and analysisIt also offers COTS boards, ATE and test systems, RF and microwave products, electronic warfare products, cockpit and rugged displays, radar and radar subsystems, identify friend and Foe products, avionic systems, laser and electro optic systems, space systems, power supply products, naval systems, communication products, gimbal and actuator controller products, launch and fire control systems, high-speed processors, digital IF processors and waveform generators, consoles, and navigation systems; and software and application products. In addition, the company provides hardware design, product development, ATE/functional test equipment, FPGA, software, microwave, mechanical product design, manufacturi…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.