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Product-facing Data Engineering: A Review of Emerging Practices for Metrics, Instrumentation, and Decision Impact
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 76 |
| Year of Publication: 2026 |
| Authors: Deep Patel |
10.5120/ijca2026926302
|
Deep Patel . Product-facing Data Engineering: A Review of Emerging Practices for Metrics, Instrumentation, and Decision Impact. International Journal of Computer Applications. 187, 76 ( Jan 2026), 14-21. DOI=10.5120/ijca2026926302
Abstract
This paper explains how product-focused data engineering fits into today's data systems. Its main goal is to turn raw data into insights that improve user experiences and guide company decisions. By reviewing relevant studies and industry reports from 2017 to 2024, the paper shares best practices about the frameworks, tools, and methods companies use for instrumenting their products, building a solid metrics layer, and measuring how decisions are affected. The study is divided into three sections: Section 1 describes how to analyze business impact in data-driven decision making; Section 2 discusses best practices in instrumentation that yield clear signals about product performance; and Section 3 describes the metrics and semantic-layer designs that keep definitions consistent across organizations. Recent advances are discussed in feature stores, data contracts, data observability, and data mesh approaches to increase safe business use. Key advances in how product data is represented drive interests of product strategy. Empirical studies link two key goals of data engineering-speedier decision-making and increased organizational agility-to better quality data. Common challenges are multiple toolsets, cost management as data scale, and juggling flexibility with consistency in decentralized systems. Semantic layers, automated data governance, and AI-assisted decision-making frameworks can improve practices from data collection to measurement of business impact.
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