Gartner has recenty predicted that by 2020, a corporate “no-cloud” policy will be as rare as a “no-internet” policy is today. CIOs will increasingly leverage a multitude of cloud computing providers across the entire IT stack to enable a huge variety of use cases and meet the requirements of their business unit peers. Indeed, the tides are shifting toward a “cloud-first” or even “cloud-only” policy... 

Marc Wilczek

Big Data – Productivity, Innovation And Competitiveness

Big Data – Productivity, Innovation And Competitiveness

Big Data Analytics

Big data refers to datasets that are so large, diverse, and fast-changing which need advanced and unique storage, management, analysis, and visualization technologies.  According to McKinsey, Big Data is “the next frontier for innovation, competition and productivity”.  The right use of Big Data can increase productivity, innovation, and competitiveness for organizations. Inhi Suh, IBM vice president of big data, stated that businesses should place a greater emphasis on analytics projects. In fact, big data analytic is an important step to extract knowledge from a huge amount of data. It is a competitive advantage for most companies.


According to Gupta and Jyoti (2014), “Big data analytics is the process of analysing big data to find hidden patterns, unknown correlations and other useful information that can be extracted to make better decisions”.Agrawal et al. (2011) described the multiple phases in the big data analysis which are Data Acquisition and Recording; Information Extraction and Cleaning; Data Integration, Aggregation, and Representation; Data Modeling and Analysis; and Interpretation. All these phases are crucial and high accuracy in each of these steps will lead to effective big data analytic. In this way, the promised benefits of big data will be achieved.

A wide variety of analytical techniques and technologies can be used to extract useful information from large collections of data. Such information helps companies to gain valuable insights to predict customer behaviour, effective marketing, increased revenue and so on. Maltby (2011) reviewed several literatures on big data analytics and introduced several techniques, such as Machine learning, Data mining, Text analytics, Crowdsourcing, Cluster analysis, Time series analysis, Network analysis, Predictive modelling, Association rule, and Regression, that can be used to extract information from a data set and transform it into an understandable structure for further use . In fact, using data analytic techniques depends on the research objectives/ questions, nature of the data, and the available technologies.

In addition, there are a wide variety of software products and technologies to facilitate big data analytics. EDWs, Visualization products, NoSQL databases, MapReduce & Hadoop, and cloud computing are examples of the more common technologies used in big data analytics. All these techniques and technologies cannot be used for every project or organization. Needs and potential of each organization should be evaluated in order to choosing the appropriate tools for big data analytic.

Studies indicates that data analysis is considerably more challenging than simply locating, identifying, understanding, and citing data. Many researchers believe that the most of the challenges and concerns with data is related to volume and velocity. However, a recent survey conducted by the creator of open source computational database management system on more than 100 data scientist indicates that variety of data sources (not just data volume & velocity) is the main challenge in analysing data. Furthermore, results of this study indicated that Hadoop cannot be a viable solution for some cases that require complex analytics.  It would seem that data analysis is a clear bottleneck in many applications. In line with this idea, Agrawal and his colleagues (2011) reported common challenges in big data analysis: Heterogeneity and Incompleteness of data, Scale, Timeliness, Privacy, error-handling, lack of structure, and visualization. It is recommended that the highlighted challenges should be addressed for effective data analysis.

By Mojgan Afshari

Mojgan Afshari

Mojgan Afshari is a senior lecturer in the Department of Educational Management, Planning and Policy at the University of Malaya. She earned a Bachelor of Science in Industrial Applied Chemistry from Tehran, Iran. Then, she completed her Master’s degree in Educational Administration. After living in Malaysia for a few years, she pursued her PhD in Educational Administration with a focus on ICT use in education from the University Putra Malaysia.She currently teaches courses in managing change and creativity and statistics in education at the graduate level. Her research areas include teaching and learning with ICT, school technology leadership, Educational leadership, and creativity. She is a member of several professional associations and editor of the Journal of Education. She has written or co-authored articles in the following journals: Journal of Technology, Pedagogy and Education, The Turkish Online Journal of Educational Technology, International Journal of Education and Information Technologies, International Journal of Instruction, International Journal of Learning, European Journal of Social Sciences, Asia Pacific Journal of Cancer Prevention, Life Science Journal, Australian Journal of Basic and Applied Sciences, Scientific Research and Essays.