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2022 | Book

Data-Driven Digital Transformation of Enterprises Read chapter Read first chapter

Enterprise Data at Huawei

Methods and Practices of Enterprise Data Governance

Authors: Yun Ma, Hao Du

Publisher: Springer Singapore

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book systematically introduces the data governance and digital transformation at Huawei, from the perspectives of technology, process, management, and so on. Huawei is a large global enterprise engaging in multiple types of business in over 170 countries and regions. Its differentiated operation is supported by an enterprise data foundation and corresponding data governance methods. With valuable experience, methodology, standards, solutions, and case studies on data governance and digital transformation, enterprise data at Huawei is ideal for readers to learn and apply, as well as to get an idea of the digital transformation journey at Huawei.

This book is organized into four parts and ten chapters. Based on the understanding of “the cognitive world of machines,” the book proposes the prospects for the future of data governance, as well as the imaginations about AI-based governance, data sovereignty, and building a data ecosystem.

Table of Contents

Frontmatter
1. Data-Driven Digital Transformation of Enterprises
Abstract
The development of communications and digital technologies is creating endless exciting possibilities for enterprises of all kinds. We are on the cusp of an era of fully-connected information and big data. Keeping up with these changes is a necessity for all enterprises.
Yun Ma, Hao Du
2. Establishing an Enterprise-Level Integrated Data Governance System
Abstract
In the twenty-first century, data has joined land, labor, and capital as the newest factor of production. Data plays an important role in unlocking enterprises’ competitive advantages, and should be managed as a strategic asset. Data is collected in the course of business operations and is stored in IT systems. Effective data governance is a complicated task requiring full staff engagement and fully compliant IT systems.
Yun Ma, Hao Du
3. Differentiated Data Classification Management Framework
Abstract
Enterprises and organizations can classify data for different purposes from multiple perspectives. Data can be classified into dichotomies such as structured and unstructured, internal and external, raw and derived, and detailed and summary. Huawei has developed a complete data classification management framework based on widely accepted data classification principles and years of practice. This framework allows different types of data to be managed with different policies to maximize input-output ratios.
Yun Ma, Hao Du
4. Business Transaction-Oriented IA Construction
Abstract
In the past, Huawei’s IA was mainly aimed at realizing “informatization” or “managing business in ERP”. In those days, IA was often embedded in IT systems. Most system users and managers focused more on whether the IA offered comprehensive functionality and whether or not business was completed via the system. The function of IA were limited to supporting the implementation of various IT systems or providing certain guidance for IT construction.
Yun Ma, Hao Du
5. Construction of a Data Foundation Centered on Connection and Sharing
Abstract
During the transformation from informatization to digitalization, enterprises have amassed vast amounts of data, and more data keeps accumulating at an explosive rate. However, instances in which this data has created value for an enterprise are relatively rare. As data is usually scattered across many locations and no unified definition or architecture is provided for the data, enterprises find it increasingly difficult to find the data that would be useful to them.
Yun Ma, Hao Du
6. Data Service Development Targeting Self-service Consumption
Abstract
A data foundation should be constructed in a manner that better supports data consumption. After the completion of data aggregation, consolidation, and linkage, additional efforts should be made by data providers to ensure that users can access data in a more convenient and secure manner.
Yun Ma, Hao Du
7. Building the Full Data Awareness Capability of “Digital Twins”
Abstract
The IT systems of the informatization era are essentially function-based, siloed, and enclosed, and can be used only by a small number of trained professionals within an enterprise. We trust these IT systems and rely on them to guide decision-making, but these are systems in which all data is entered manually. So what if someone makes a mistake?
Yun Ma, Hao Du
8. Building Comprehensive Quality Management Capabilities to Ensure “Clean Data”
Abstract
More and more enterprise applications and services are being developed based on data. In this context, data quality is the prerequisite for making the most of data value. Because the operations efficiency of an enterprise in the modern age mainly depends on the accuracy and timeliness of data acquisition, incorrect or incomplete data in the enterprise customer relationship management system, for example, will lead to poor customer communication and impact customer satisfaction.
Yun Ma, Hao Du
9. Building Secure, Compliant, and Controllable Data Sharing Capabilities
Abstract
Before an enterprise has implemented data governance and built a data foundation, its data is scattered across systems and difficult to obtain for analysis and insights. To eliminate data silos, Huawei has built a unified data foundation to aggregate and link large amounts of enterprise data.
Yun Ma, Hao Du
10. Data Is Becoming a Core Competency of Enterprises
Abstract
Digital transformation is not something that can be completed in a single stroke, and data governance cannot be implemented overnight. Digital transformation brings both opportunities and new challenges to data governance across an enterprise.
Yun Ma, Hao Du
Metadata
Title
Enterprise Data at Huawei
Authors
Yun Ma
Hao Du
Copyright Year
2022
Publisher
Springer Singapore
Electronic ISBN
978-981-16-6823-4
Print ISBN
978-981-16-6822-7
DOI
https://doi.org/10.1007/978-981-16-6823-4