Industrializing your AI and data science models with IBM Cloud Private for Data

Industrializing your AI and data science models with IBM Cloud Private for Data

Companies are entering “chapter two” of their digital transformation. The next chapter is all about moving from experimentation to true transformation. It’s about gaining speed and scale. We are helping businesses activate data as a strategic asset, with desire to maximize the impact of AI
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The ‘Unprecedented Challenge’ of Cybersecurity in an Age of Burgeoning Threats

The ‘Unprecedented Challenge’ of Cybersecurity in an Age of Burgeoning Threats

The ‘Unprecedented Challenge’ of Cybersecurity Technological and legal complexities abound in this age of heightened cybersecurity threats—including a rise in state-sponsored hacking. This “unprecedented challenge” was the topic of conversation between Dorian Daley, Oracle executive vice president and general counsel, and Edward Screven, Oracle’s chief
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Big Data Survey

Today’s organizations should become more collaborative, virtual, adaptive, and agile in order to be successful in complex business world. They should be able to respond to changes and market needs. Many organizations found that the valuable data they possess and how they use it can make them different than others. In fact, Big Data can transform many fields such as business, management, public administration, science, and so on. In 2012, Gartner defined Big Data as “high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making”. The term volume refers to large amounts of data, velocity indicates the speed of data in and out, and variety describes the range of data types and sources.

Big Data Survey infographic

(See Jaspersoft Big Data Survey Results)

According to an industry report prepared by McKinsey Global Institute, the effective use of Big Data is a key basis of competition and delivering a new wave of productive growth. In other words, managing, analysing, visualizing, and extracting useful information from large data sets will help organizations to increase operational efficiency, to inform strategic direction, to develop new products and services, to identify new customers and markets, to make better decisions and to become more innovative. (Davenport, Barth, & Bean, 2012).

Although Big Data bring many attractive opportunities, organizations are also facing a lot of challenges such as data capture, storage, searching, sharing, analysis, and visualization. In recent years, a large number of Big Data techniques and technologies have developed to overcome all the obstacles. Big Data techniques such as statistics, data mining, machine learning, neural networks, social network analysis, signal processing, pattern recognition, optimization methods and visualization approaches; can be used to process efficiently large volume of data.

In addition, organizations need platforms or tools to make sense of big data. They should determine which platforms and tools can help them to meet their business goals. Current tools concentrate on three classes which are batch processing tools, stream processing tools, and interactive analysis tools. Majority of batch processing tools are based on the Apache Hadoop infrastructure which is one of the most important software platforms that support data-intensive distributed applications. It can load, store and query massive data sets on a large, flexible grid servers, as well as perform advanced analytics. It uses a programming model, which is called Map/Reduce, to process and generate great volume of data sets. Map/Reduce breaks down a complex problem into many sub-problems. These sub-problems are solved in separate and parallel ways. Finally, the solutions of sub-problems are combined to create a solution to the original problem. Although Hadoop can process large amount of data in parallel, it is not a real-time and high performance engine. It is not appropriate for high volume, high velocity and complex data types. Hence, other platforms such as SQL stream, Stream Cloud, and Storm can be used for real-time stream data analytics.

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.

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