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Wang's five-volume set confronts the challenges of information retrieval in the age of Big Data

Interview with John Wang, IGI Global Editor of the forthcoming Encyclopedia of Business Analytics and Optimization

By IGI Global on Apr 23, 2014
Contributed by Ann Lupold, Promotions and Communications Coordinator

Recent advances in technology have resulted in major transformations in massive data processing infrastructures. The rise of Big Data brings both the potential for endless possibilities as well as innumerable threats. Trying to manage and utilize such vast resources can be a very daunting task.

Interview With John WangJohn Wang is a professor in the Department of Information & Operations Management at Montclair State University, USA. He has committed much of his professional career to the study of Big Data and operations research. Editor of the forthcoming Encyclopedia of Business Analytics and Optimization, his five-volume set evaluates the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focuses these dimensions towards one critical emphasis - value.

Professor Wang recently took some time out of his busy schedule to collaborate on an interview discussing his forthcoming encyclopedia, as well as his research and discoveries in the fields of big data and data analysis.


The areas of business analytics and big data really appear to be trending right now. How and why did these topics become so popular and important to modern organizations?

Yes, the areas of business analytics and big data are currently very hot now. The infancy of Big Data was created at AT&T’s InfoLab with live, real-time data over a decade ago. As such, data is growing at an astronomical rate. Research has shown that we produce more data now in a single day than we did from the dawn of civilization through 2003 (more than five exabytes). According to Kolb and Kolb (2013), approximately 90% of the entire world's data has been created in the past 2 years. An explosion in terms of the Internet of Things (IoT) applications will raise the scale of data to an extraordinary level.

With the Age of Big Data upon us, should we risk being drowned in an overwhelming tide of digital data? Or should we instead be lifted up to an ever higher level of knowledge by capitalizing on a reservoir that is flowing with meaningful information? Clearly, we confront the challenges of extracting a fountain of knowledge from mountains of big-data (Wang, 2014).

Big Data is as much about “The Management Revolution” as the potential to transform traditional businesses (McAfee and Brynjolfsson, 2012). Steven Hillion, chief data officer for Alpine Data Labs, believes that business intelligence is becoming more of a “question generator” and that data analytics is rapidly becoming the “answer machine”. “With reports and dashboards now running against larger datasets and increasingly in real time, the ability to get a snapshot of your business and identify where you need to pay greater attention in sales, support, marketing and finance is extremely valuable.”

Your five-volume Encyclopedia of Business Analytics and Optimization crosses into a number of industries and discipline areas. Which individuals and industries do you feel can utilize and benefit from the discussions surrounding business analytics and big data as they are presented in this timely publication?

I feel that every individual and industry can utilize and benefit from the discussions surrounding business analytics and big data as they are presented in this timely publication. Let us name a few of them: healthcare, retail, pharmaceuticals, banking and financial institutions, the public sector, manufacturing, transportation ( smart routing using real-time traffic information is a good example), service operations, the travel industry, energy management, environmental protection, biology, genomics, clinical research, internet searches, meteorology, complex physics simulations, and even small businesses.

From top executives to general managers, from government legislators to administrators, and from scientists to engineers, Big Data touches on every corner of the modern society. The use of Big Data will create significant value for individual firms and the world economy. Businesses, government, and consumers will benefit from insights generated using mined datasets. According to research from the McKinsey Global Institute (MGI), retailers embracing big data see the potential to increase their operating margins by 60 percent. Users of services enabled by location-based data can possibly capture $600 billion in consumer surpluses.

It has been a few years since you edited your previous encyclopedia projects with IGI Global: the 2005, Encyclopedia of Data Warehousing and Mining (2 Volumes) and the 2009, Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes); how would you say your approach to this encyclopedia has changed as compared to your past encyclopedia projects?

My past encyclopedia projects were largely focused on broad, yet somewhat specific topic areas. However, this set covers and crosses many related fields. As the need arises to dig deeper into Big Data, consequently there is a need to address this topic comprehensively. As a part of the encyclopedia development process, we searched the databases of more than 12 leading academic journals and more than 16 international conferences. This new encyclopedia now covers a much wider spectrum of the subject.

What criteria did you utilize when selecting article contributions? How did you decide upon the topics covered?

When selecting article contributions, we utilized much the same criteria as for peer-reviewed journal articles. An article can be accepted if receiving an overall positive evaluation in the opinion of the editors, even if subject to minor or major revisions. A revised version is sent for an additional review before final acceptance.

The covered topics were decided upon at the beginning of this project after surveying the editorial board members of four related journals, gleaning EB member feedback, and considering suggestions offered by Advisory Board members, as well as feedback from the (co)authors and our reviewers. In particular, emerging topics were sought and welcome.

What are some of the contemporary challenges and potential directions for these topic areas, and what are the main techniques and disciplines involved?

a) A shortage of people with the required research and analytical skills is one of our most urgent challenges.
b) Also, there are many infrastructure and technical issues that need to be resolved. Data privacy, security, reliability, and integrity are some of the main hurdles facing us. For instance, discordant standards and formats are roadblocks for data integration. The related hardware and software need to be more efficient and economically affordable.
c) How do we capture, cope with and conquer Big Data? We need to improve currently complex algorithms, and customize toolboxes targeted for each industry/sector, to name a couple of areas.
d) Use of Big Data for customer understanding.
e) Integration of structured and unstructured data for business applications.
f) Cognitive computing - Watson-like applications to solve problems previously thought of as impossible.

The process of knowledge creation therefore can include multiple components and perspectives.


Big Data Technologies –

  • Neural networks
  • Association rule learning
  • Sequence analysis
  • Classification
  • Cluster analysis
  • Anomaly detection
  • Scalable storage systems
  • Data-mining grids
  • Sentiment analysis
  • Cloud computing platforms
  • Supervised and unsupervised learning
  • Search-based applications
  • Signal processing
  • A/B testing (online split testing)
  • Massively Parallel-Processing (MPP) databases
  • Distributed systems (such as Hadoop, MapReduce, etc.)
  • Crowdsourcing
  • Natural language processing (NLP)
  • Data fusion and integration
  • Predictive modeling
  • Regression and Time series analysis
  • Tensor-based computation
  • Ensemble learning
  • Simulation
  • Visualization

Big Data Disciplines: Big Data employs an interdisciplinary approach and bridges the gap among different disciplines, such as –

  • Data Mining
  • OR/MS
  • Statistics
  • Decision Support System
  • Computer Science
  • Graphic Design
  • Human-computer interaction
The process of knowledge creation therefore can include multiple components and perspectives.

What are some of the key reasons an academic or special library should add this title to their reference collection?

In order to meet huge market needs, we need to train and educate our students to become more capable decision makers. A McKinsey Global Institute report calls “big data” the next frontier for innovation, competition and productivity. The report estimates that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

According to a Gartner research report: “By 2015, 4.4 million IT jobs globally will be created to support Big Data. Yet the limited application of Big Data in universities and colleges today will contribute to a skills gap.” Companies require a new set of specialists who can bridge the gap between IT and business.

What are some of the key reasons a corporate library should add this title to their reference collection?

By adopting a diverse set of tools/techniques while employing the synergies involved, companies and organizations can drastically –
  • Improve their effectiveness
  • Increase operational efficiency
  • Gain competitive advantage
  • Develop the next generation of products and services
  • Minimize risks
  • Unearth higher levels of insight
  • Unlock big deal
  • Reduce waste, fraud, and errors
  • Improve their bottom line
Because companies and organizations can make effective decisions

  • Faster (real-time)
  • More Frequently due to continuous challenges
  • Solidly based on fact

As the price of sensors, communications devices, and analytic software continues to fall, more and more companies will be joining this managerial revolution.

How will this publication add to the future developments in this research area?

The encyclopedia contains a total of 246 chapters, written by an international team of 372 experts including leading scientists and talented young scholars from the following 44 countries and special regions: Australia, Austria, Bangladesh, Barbados, Brazil, Bulgaria, Canada, China, Colombia, Croatia, Denmark, Egypt, Ethiopia, France, Germany, Greece, Hong Kong, Iran, Israel, Italy, Japan, Korea, Kuwait, Malaysia, México, New Zealand, Papua New Guinea, Philippines, Poland, România, Russia, Saudi Arabia, Singapore, Slovenia, Spain, Sultanate of Oman, Sweden, Switzerland, Taiwan, Thailand, Turkey, UAE, UK, and USA.

In summary, as an expert in the field of business analytics and big data management; what do you consider to be the three most emerging trends surrounding the effective management and utilization of big data? And how will these trends affect organizations worldwide over the course of the next 3-5 years?

As I see it, we are only in the early stages of this Big Data “tidal wave.” We may not yet understand or be able to envision its full potential.

a) Big Data will continue to be the most significant trend in business intelligence for the foreseeable future, as claimed by the experts interviewed by Enterprise Apps Today. Determining what data is important is a critical first step for any successful project.

b) Cloud-based platforms and software will become more user-friendly. Corporate analytic applications will increase due to the cloud’s low-cost, scale-out, and elastic nature.

c) Virtual assistants, such as natural-language processing and cognitive systems, will make access to information much easier.

d) Big Data technologies will provide more cutting-edge algorithms that will enable more comprehensive analyses. Decision makers can link information from unstructured sources with structured ones in order to glean more value.

e) The large number of applications that do not run on Map-Reduce - more apps will be ported over time, allowing organizations to treat Big Data as more of a mainstream IT infrastructure

f) Lack of Big Data skills - will improve over time allowing organizations to scale their Big Data efforts

g) Big Data is hard to use - this is solved by porting more applications over and growing the skill base.


REFERENCES

Davenport, T., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
INFORMS. (2013). What is analytics? https://www.informs.org/About-INFORMS/What-is-Analytics
Kolb, J., & Kolb, J. (2013). The Big Data revolution (Kindle Edition). Plainfield, IL: Applied Data Labs Inc.
McAfee, A., & Brynjolfsson, E. (2012, October). Big Data: The management revolution. Harvard Business Review, 1-9.
Wang, J. (Ed.). (2008). Data warehousing and mining: Concepts, methodologies, tools, and applications (6 Volumes). Hershey, PA: IGI Global.


Encyclopedia of Business Analytics and Optimization (5 Volumes) 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, refer to the encyclopedia web page or contact Customer Service at 717-533-8845 x100 or email cust@igi-global.com.
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