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Dr. Shokoufeh Mirzaei discusses mysteries surrounding big data

“Big Data” and “Analytics”: the Transformers of Our Time

By IGI Global on Mar 31, 2015

Contributed by Dr. Shokoufeh Mirzaei, Assistant Professor, California State Polytechnic University, USA

What are “Big Data” and “Analytics” in the end? “Big Data” And “Analytics”: The Transformers Of Our Time

“Big Data” And “Analytics”: The Transformers Of Our Time

There are mixed views, arguments, and definitions regarding Big Data and Analytics and their application in business. However, most of these definitions are appropriate as they represent different point of view or real-life applications. For an electrical engineer, signal processing might be the most immediate application of data analytics. For an industrial engineer, perhaps data analytics is predicting demand and production rate. For a bioinformatics expert, data analytics might involve the encoding of the human genome. These different perspectives have made it difficult to come to a consensus in defining the term “Analytics” and avoid misunderstandings.

In a nutshell, Analytics transforms data into knowledge for making practical decisions- regardless of the field in which it is used. Analytics is the gradual evolution of multiple disciplines into one multidisciplinary field in our time. Today, the umbrella of Analytics is the widest it has ever been- covering fields such as computer programming, operations research, statistics, machine learning, image processing, multiple criteria decision making, systems dynamics, etc. Experts in different disciplines shed light on different aspects of business operations and come up with solutions accordingly.

Analytics can be grouped into three major categories: predictive, descriptive, and prescriptive. Researchers in the fields of statistics and machine learning analyze historical data and provide insight regarding the pattern existing in the data in an attempt to predict future events. These efforts are usually categorized as predictive analytics which has a mature literature base. Descriptive analytics is geared toward visualization of data and making summaries using images, charts, and graphs that can provide an immediate impression regarding the knowledge hidden in the data without digging into too much detail. A relatively new field of analytics which falls within the category of prescriptive analytics includes operations research techniques. Operations research is a tool to find the best alternative in a set of possible solutions- usually when numerous solutions exist. The first attempt for scientific application of operations research in planning and managing organizational operations occurred in 1940s during World War II when there was a scarcity of human and material resources and managing war logistics was a daunting task. Another field in prescriptive analytics called decision support systems (DSS) was introduced by the Carnegie Institute of Technology during the late 1950s and early 1960s with the objective of providing educated decisions for business processes. DSS later evolved into business intelligence. Three decades ago, business intelligence was to program simplistic queries in relational databases to find an answer for some basic operational questions, e.g., when is a good time to air a TV show. At the time, business operations mostly trusted executives' common sense to make critical business decisions. Business intelligence has evolved to “Business Analytics” in our time, which takes advantage of “Big Data.”

Perhaps one analogy that could clarify the relation between Big Data and Analytics is our body and brain. Big Data is a set of tools and technologies to collect, store, and retrieve enormous amounts of data. It can be seen as a giant database containing data and its relations. It is similar to our body, encompassing millions of veins and neurons carrying blood and signals throughout the body. However, data analytics is the brain which takes advantage of all these connections to make meaningful functions happen- whether it is making a decision or taking an action. Analytics is the brain for the body of Big Data and is capable of thinking, processing images, seeing evidences, and predicting the future. Analytics analyzes the gathered data and their connections and makes meaningful decisions, way more sophisticated than basic queries in business intelligence. In this analogy, business intelligence can be seen as a brain in coma, where the brain function is limited to dictate the organs’ functions without any analytical capability.

Today “Big Data” and “Analytics” go hand–in–hand to complement each other, and that’s how the field “Big Data Analytics” has emerged. Big Data Analytics is not merely a buzzword in the realm of the tech industry anymore; it is deeply embedded in our daily lives and activities, from optimized GPS signal processing when finding our way around the city, to image processing when depositing our checks into bank accounts.

Big Data Analytics: The Good, The Bad, The Ugly!
There are mixed opinions regarding the involvement of technologies in our lives and the advantages and disadvantages from socio-cultural standpoints. However, we should not neglect the immense opportunities Big Data Analytics has provided for streamlining processes, making better decisions, and even fostering economic growth. Companies such as IBM have set global goals to foster economic growth based on innovations made through Big Data Analytics in developing countries such as South Africa.

Thanks to exponential growth in the advancement of technology in data storage, retrieval, processing, and new generations of analytical tools and methods, new understanding of different aspects of science, technology, and organizational problems have been discovered. These discoveries have influenced policy makers to appropriate financial resources for unleashing Big Data Analytics with the potential for improving the well-being of society. In this year's State of the Union Speech, President Barack Obama unveiled his “Precision Medicine Initiative” that will harness large genomics and epigenomics data to design drugs and treatments specifically customized for individual patients.

Finally, although the rapid advancement of high-performance computing technologies has provided an unprecedented opportunity to make smart decisions, the volume of available data and the opportunities for analysis have grown at a rate far higher than the experts in the field expected. Consequently, giant companies such as IBM have invested millions of dollars to provide analytical solutions that simplifying the application of analytical methods for non-expert people. IBM i2 Enterprise Insight Analysis is an example of such projects which attempts to bring the power of analytics to any organization. However, the majority of these projects are geared toward designing tools that question the need for human expertise and analysis in the decision making process. Although these technologies propose a great opportunity for making smarter decisions, the issue remains whether these technologies will one day eliminate the need for the human brain whatsoever. If they do, what would a world built on decision-making with no human insight be like?

Dr. Mirzaei is a tenure track Assistant Professor at California State Polytechnic University Pomona, in the Industrial and Manufacturing Engineering department. She received her Ph.D. in Industrial Engineering with minor in Statistics from Wichita State University. She got her Masters of Science in Industrial and Systems Engineering from Isfahan University of Technology and her Bachelors of Science from Iran University of Science and Technology. Her core areas of research are applied optimization, data analytics and machine learning. Currently, she conducts research in the application of pattern recognition techniques in protein folding in collaboration with scientists from the Lawrence Berkeley National Laboratory.

Prior to joining to Cal Poly Pomona, Mirzaei worked as a senior analytics consultant, where she developed mathematical programming to solve clients’ problems. Besides teaching and research in school, she actively works with tech start-ups in the city Los Angeles as a data scientist.

Dr. Mirzaei's chapter "Defining a Business-Driven Optimization Problem", featured in the Encyclopedia of Business Analytics and Optimization, provides fundamental information about business analytics and optimization for users ranging from college students to consultants and managers for a successful practice of data analytics.

The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal. For more information on this comprehensive reference source, visit the Encyclopedia of Business Analytics and Optimization web site.

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