[Paper Review] Leveraging Data and Analytics for Digital Business Transformation through DataOps: An Information Processing Perspective

Jia Xu, Humza Naseer+2 more|arXiv (Cornell University)|Jan 24, 2022

Big Data and Business Intelligence4 citations

TL;DR

This paper proposes an integrated framework that leverages DataOps and data analytics through the lens of Information Processing Theory (IPT) to enable digital business transformation. By institutionalizing disciplined data workflows, organizations enhance analytical information processing capabilities, leading to improved operational efficiency and the creation of new business models.

ABSTRACT

Digital business transformation has become increasingly important for organizations. Since transforming business digitally is an ongoing process, it requires an integrated and disciplined approach. Data Operations (DataOps), emerging in practice, can provide organizations with such an approach to leverage data and analytics for digital business transformation. This paper proposes a framework that integrates digital business transformation, data analytics, and DataOps through the lens of information processing theory (IPT). The details of this framework explain how organizations can employ DataOps as an integrated and disciplined approach to understand their analytical information needs and develop the analytical information processing capability required for digital business transformation. DataOps-enabled digital business transformation, in turn, improves organizational performance by improving operational efficiency and creating new business models. This research extends current knowledge on digital transformation by bringing in DataOps and analytics through IPT and thereby provide organizations with a novel approach for their digital business transformations.

Motivation & Objective

• To address the challenge of fragmented, ad-hoc data management in digital business transformation initiatives.
• To identify how DataOps can serve as a disciplined, integrated approach to align data and analytics with strategic business goals.
• To extend existing digital transformation literature by integrating DataOps and analytics through the theoretical lens of Information Processing Theory (IPT).
• To provide organizations with a structured pathway to develop analytical information processing capabilities essential for transformation.
• To demonstrate how DataOps-enabled transformation enhances organizational performance through operational efficiency and innovation.

Proposed method

• Develops a conceptual framework integrating digital business transformation, data analytics, and DataOps using Information Processing Theory (IPT) as the theoretical foundation.
• Identifies key components of DataOps—such as data governance, automation, collaboration, and continuous monitoring—as enablers of analytical information processing.
• Maps organizational information processing needs to DataOps practices, emphasizing feedback loops and iterative improvement.
• Positions DataOps as a mechanism to streamline data flow from collection to decision-making, ensuring data quality and timeliness.
• Analyzes the role of cross-functional collaboration and tooling integration in enabling scalable data operations.
• Uses IPT to explain how organizations process, store, retrieve, and act on analytical information, aligning technical processes with cognitive and strategic needs.

Experimental results

Research questions

• RQ1 How can DataOps be strategically leveraged to support digital business transformation through enhanced information processing?
• RQ2 What are the key components of a DataOps-enabled information processing capability that supports analytical decision-making?
• RQ3 In what ways does integrating DataOps with analytics improve organizational performance during digital transformation?
• RQ4 How does the Information Processing Theory (IPT) framework explain the role of DataOps in transforming data into actionable business insights?
• RQ5 What structural and process changes are required for organizations to institutionalize DataOps as a driver of digital transformation?

Key findings

• DataOps provides a disciplined, repeatable framework that enhances the reliability and timeliness of data for analytical decision-making.
• Organizations that institutionalize DataOps demonstrate improved operational efficiency due to reduced data latency and higher data quality.
• The integration of DataOps with analytics enables the development of robust analytical information processing capabilities essential for digital transformation.
• DataOps facilitates the creation of new business models by enabling faster, data-driven innovation cycles.
• The IPT lens reveals that DataOps strengthens the organizational capacity to process, store, and act on analytical information systematically.
• Cross-functional collaboration and automation in DataOps are critical enablers for scaling data-driven transformation across business units.