Integrated Data Architecture for Business

Integrated Data Architecture for Business

Richard Kumaradjaja (Renaissance Consulting, Indonesia)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch074
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Abstract

This paper describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this article is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better able to construct proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The paper also discusses about future research directions of the proposed integrated data architecture framework.
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Introduction

According to (Kosala & Kumaradjaja, 2014), to achieve a better analytics capability business organizations must utilize three Business Analytics components altogether so that a 360 degree analysis or a more comprehensive view of data can be implemented. The three Business Analytics components are: Big Data Analytics Tools and custom applications, Data Analytics Tools and custom applications, Unstructured Data Analytics Tools and custom applications form the Analytics Layer of the Big Data Analytics within the Business Intelligence and Business Analytics framework proposed by (Kosala & Kumaradjaja, 2014). For convenience, the structure of the Big Data Analytics framework is shown in Figure 1.

Figure 1.

Big data analytics within the business intelligence and business analytics framework

As stated by (Kosala & Kumaradjaja, 2014), the integration methodology of the three analytics components in Figure 1 and how they can be utilized together to get a 360 degree analysis view of data could represent a major research effort in the next few years. One of those research areas is about the data integration framework between unstructured Hadoop or NoSQL systems to create a structured OLAP data structure, and its real business case studies.

The author believes that the data integration framework must consider all layers of the Big Data Analytics framework shown in Figure 1, namely, Data Layer, Data Acquisition Layer, Data Storage Layer and Analytics Layer. Therefore, the proposed data integration framework must consider the entire Big Data Analytics ecosystem as one integrated data architecture.

The purpose of this article is to clarify and show the concept of an integrated data architecture as a foundation of a modern data architecture which has the potential of addressing and solving critical issues related to big data analytics implementation in business organizations. Eventually, the goal of this article is to improve the likelihood of successful big data analytics implementation in businesses.

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Main Focus Of The Article

The main focus of this article is to address the issues of data integration from the architectural point of view. Addressing the isues of data integration from the architectural point of view will lead to a better understanding of the current situation and better able to construct proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems.

In this section, the data integration issues related to Big Data Analytics adoption will be discussed, and an integrated data architecture for implementing Big Data Analytics will be proposed.

Key Terms in this Chapter

Web Mining: The use of data mining techniques to discover and extract information from web documents and services.

Business Analytics: The broad use of data and quantitative analysis to make business decisions in corporations.

Structured Data: Data that is defined and organized in a structure. For example: data in tables, data in relational databases.

NoSQL: A collection of non-relational database technologies that are designed to store unstructured web data or documents.

Unstructured Data: Data that has no identifiable structure. For example, images, videos, email, text documents, and web documents.

Data Virtualization: Data management approach that allows an application to retrieve and manipulate data without requiring technical details about the data.

Text Mining: The use of data mining techniques to discover and extract patterns from text.

Hadoop: An open source technology developed under Apache Software Foundation that can be used to process Big Data in a distributed manner.

Data Mining: The computational process and technique to find and discover patterns from large data sets.

Big Data Analytics: The process, techniques, and tools used to gain insights from Big Data so that the decision making process could be optimized.

Big Data: The size, speed and sorts of data that exceed an organization’s traditional database capacity to access, integrate, store, and analyze for accurate and timely decision making.

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