GOAL-Toolkit Based Ontology for Information Entrepreneurs to Evaluate the Goals Achievement: A Research Plan

GOAL-Toolkit Based Ontology for Information Entrepreneurs to Evaluate the Goals Achievement: A Research Plan

Tengku Adil Tengku Izhar (Universiti Teknologi MARA, Shah Alam, Malaysia), Torab Torabi (La Trobe University, Melbourne, Australia) and M. Ishaq Bhatti (La Trobe University, Melbourne, Australia)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/IJBAN.2017070103
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Abstract

The aim of this conceptual paper is to propose a toolkit to assist entrepreneurs to evaluate their organizational goals achievement. This toolkit is design as a clear set of systematic guideline for organizations to analyse their data to extract value that meet certain organizational goals. It allows entrepreneurs to manage their data based on accepted good practice, which their organizations can adopt to evaluate the level of their goals achievement. The implementation of the toolkit explains how data can be analysed in relation to the organizational goals. By using the toolkit, organization can trace how data is flows across the organizations, identify the relevant data and seeks to define a metrics to analyse relevant data. As a result, it improves the understanding of the relationship between organizational data and organizational goals that can lead to an effective evaluation of the goals achievement.
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Research Issues

Today people have access to more data in single day than most people that have access to data in the previous decade. Big data provides significant opportunities for enterprises to impact a wide range of business processes in the organizations (Bihl, 2016). Organizations create huge amount of data in their daily business activities. The problem is this data is created and found in many different forms. All this data captures in different formats and makes it almost impossible to understand the existing relationship between different data. As a result, this data might be redundant with huge volume of data and make it hard to identify which data is relevant to the goals. Although big data does not refer to any specific quantity, this data might create petabytes and exabytes of data, much of which cannot be integrated easily. For example, government agencies and large, medium and small private enterprises in many domains, such as engineering, education, manufacturing, are drowning in an ever-increasing deluge of data. Companies like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning (Davenport & Dyche, 2013).

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