In the beginning, we list questions to address in enterprises to enable efficient data standardisation and management.
Reducing Data Complexity
What you
will learn?
3 data mapping methodologies
Read about the three most common data mapping methods with their pros and cons.
Stakeholder roles and responsibilities in data management
Stakeholders from various parts of the organization play interconnected roles that define the success of governance efforts. Discover them.
Integration strategies
Learn how to choose and develop data integration strategy
What’s inside
Table of contents
01Introduction
02Solving Problem: Fragmented, Inconsistent, and Siloed Data Across Various Systems
Find out the most common challenges for businesses in data integration and processing.
03Data Mapping: Foundations of Data Standardisation and Unification
In this chapter, we depict the three primary components of data mapping which are Source Schema, Mapping Rules, and Target Schema. We also explain the three most common mapping methodologies with their advantages, disadvantages, and ideal use cases.
04Data Governance: Navigating the Control and Quality
In this section, we discuss terms related to data governance and guide you through the role of metadata. We also show you what are roles and responsibilities of stakeholders from various organization divisions in efficient data management.
05Integration Strategies: Bringing Data Together
Data integration is key to holistic analysis and bridging the knowledge gap. We show you how to create an integration strategy step by step.
06Data Future Proofing: To-Be State is Just the Beginning
In this chapter, we look ahead and show how emerging technologies will affect the use and analysis of data in the coming years. This is important in terms of how to prepare data today to make the best use of it in the future.
Reducing Data Complexity
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