Case Study

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

Keywords: Business intelligence and analytics, big data analytics, Web 2.0

Introduction

Business intelligence and analytics (BI&A) and the related field of big data analytics have become increasingly important in both the academic and the business communities over the past two decades. Industry studies have highlighted this significant development. For example, based on a survey of over 4,000 information technology (IT) professionals from 93 countries and 25 industries, the IBM Tech Trends Report (2011) identified business analytics as one of the four major technology trends in the 2010s. In a survey of the state of business analytics by Bloomberg Businessweek (2011), 97

percent of companies with revenues exceeding $100 million were found to use some form of business analytics. A report by the McKinsey Global Institute (Manyika et al. 2011) pre- dicted that by 2018, the United States alone will face a short- age of 140,000 to 190,000 people with deep analytical skills, as well as a shortfall of 1.5 million data-savvy managers with the know-how to analyze big data to make effective decisions.

Hal Varian, Chief Economist at Google and emeritus profes- sor at the University of California, Berkeley, commented on the emerging opportunities for IT professionals and students in data analysis as follows:

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So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis. So my recommendation is to take lots of courses about how to manipulate and analyze data: databases, machine learning, econometrics, statistics, visualization, and so on.1

The opportunities associated with data and analysis in dif- ferent organizations have helped generate significant interest in BI&A, which is often referred to as the techniques, tech- nologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions. In addition to the underlying data processing and analytical technologies, BI&A includes business-centric practices and methodologies that can be applied to various high-impact applications such as e-commerce, market intelli- gence, e-government, healthcare, and security.

This introduction to the MIS Quarterly Special Issue on Business Intelligence Research provides an overview of this exciting and high-impact field, highlighting its many chal- lenges and opportunities. Figure 1 shows the key sections of this paper, including BI&A evolution, applications, and emerging analytics research opportunities. We then report on a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related BI&A academic and industry publications. Educa- tion and program development opportunities in BI&A are presented, followed by a summary of the six articles that appear in this special issue using our research framework. The final section concludes the paper.

BI&A Evolution: Key Characteristics and Capabilities

The term intelligence has been used by researchers in artificial intelligence since the 1950s. Business intelligence became a popular term in the business and IT communities only in the 1990s. In the late 2000s, business analytics was introduced to represent the key analytical component in BI (Davenport 2006). More recently big data and big data analytics have been used to describe the data sets and ana- lytical techniques in applications that are so large (from terabytes to exabytes) and complex (from sensor to social media data) that they require advanced and unique data

storage, management, analysis, and visualization technol- ogies. In this article we use business intelligence and ana- lytics (BI&A) as a unified term and treat big data analytics as a related field that offers new directions for BI&A research.

BI&A 1.0

As a data-centric approach, BI&A has its roots in the long- standing database management field. It relies heavily on various data collection, extraction, and analysis technologies (Chaudhuri et al. 2011; Turban et al. 2008; Watson and Wixom 2007). The BI&A technologies and applications currently adopted in industry can be considered as BI&A 1.0, where data are mostly structured, collected by companies through various legacy systems, and often stored in commer- cial relational database management systems (RDBMS). The analytical techniques commonly used in these systems, popularized in the 1990s, are grounded mainly in statistical methods developed in the 1970s and data mining techniques developed in the 1980s.

Data management and warehousing is considered the foun- dation of BI&A 1.0. Design of data marts and tools for extraction, transformation, and load (ETL) are essential for converting and integrating enterprise-specific data. Database query, online analytical processing (OLAP), and reporting tools based on intuitive, but simple, graphics are used to explore important data characteristics. Business performance management (BPM) using scorecards and dashboards help analyze and visualize a variety of performance metrics. In addition to these well-established business reporting func- tions, statistical analysis and data mining techniques are adopted for association analysis, data segmentation and clustering, classification and regression analysis, anomaly detection, and predictive modeling in various business appli- cations. Most of these data processing and analytical tech- nologies have already been incorporated into the leading com- mercial BI platforms offered by major IT vendors including Microsoft, IBM, Oracle, and SAP (Sallam et al. 2011).

Among the 13 capabilities considered essential for BI plat- forms, according to the Gartner report by Sallam et al. (2011), the following eight are considered BI&A 1.0: reporting, dashboards, ad hoc query, search-based BI, OLAP, interactive visualization, scorecards, predictive modeling, and data mining. A few BI&A 1.0 areas are still under active devel- opment based on the Gartner BI Hype Cycle analysis for emerging BI technologies, which include data mining work- benchs, column-based DBMS, in-memory DBMS, and real- time decision tools (Bitterer 2011). Academic curricula in Information Systems (IS) and Computer Science (CS) often

1“Hal Varian Answers Your Questions,” February 25, 2008 (http:// www.freakonomics.com/2008/02/25/hal-varian-answers-your-questions/).

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Figure 1. BI&A Overview: Evolution, Applications, and Emerging Research

include well-structured courses such as database management systems, data mining, and multivariate statistics.

BI&A 2.0

Since the early 2000s, the Internet and the Web began to offer unique data collection and analytical research and develop- ment opportunities. The HTTP-based Web 1.0 systems, characterized by Web search engines such as Google and Yahoo and e-commerce businesses such as Amazon and eBay, allow organizations to present their businesses online and interact with their customers directly. In addition to porting their traditional RDBMS-based product information and business contents online, detailed and IP-specific user search and interaction logs that are collected seamlessly through cookies and server logs have become a new gold mine for understanding customers’ needs and identifying new business opportunities. Web intelligence, web analytics, and the user-generated content collected through Web 2.0-based social and crowd-sourcing systems (Doan et al. 2011; O’Reilly 2005) have ushered in a new and exciting era of BI&A 2.0 research in the 2000s, centered on text and web analytics for unstructured web contents.

An immense amount of company, industry, product, and customer information can be gathered from the web and organized and visualized through various text and web mining techniques. By analyzing customer clickstream data logs,

web analytics tools such as Google Analytics can provide a trail of the user’s online activities and reveal the user’s browsing and purchasing patterns. Web site design, product placement optimization, customer transaction analysis, market structure analysis, and product recommendations can be accomplished through web analytics. The many Web 2.0 applications developed after 2004 have also created an abun- dance of user-generated content from various online social media such as forums, online groups, web blogs, social net- working sites, social multimedia sites (for photos and videos), and even virtual worlds and social games (O’Reilly 2005). In addition to capturing celebrity chatter, references to everyday events, and socio-political sentiments expressed in these media, Web 2.0 applications can efficiently gather a large volume of timely feedback and opinions from a diverse customer population for different types of businesses.

Many marketing researchers believe that social media analytics presents a unique opportunity for businesses to treat the market as a “conversation” between businesses and customers instead of the traditional business-to-customer, one-way “marketing” (Lusch et al. 2010). Unlike BI&A 1.0 technologies that are already integrated into commercial enterprise IT systems, future BI&A 2.0 systems will require the integration of mature and scalable techniques in text mining (e.g., information extraction, topic identification, opinion mining, question-answering), web mining, social network analysis, and spatial-temporal analysis with existing DBMS-based BI&A 1.0 systems.

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Except for basic query and search capabilities, no advanced text analytics for unstructured content are currently con- sidered in the 13 capabilities of the Gartner BI platforms. Several, however, are listed in the Gartner BI Hype Cycle, including information semantic services, natural language question answering, and content/text analytics (Bitterer 2011). New IS and CS courses in text mining and web mining have emerged to address needed technical training.

BI&A 3.0

Whereas web-based BI&A 2.0 has attracted active research from academia and industry, a new research opportunity in BI&A 3.0 is emerging. As reported prominently in an October 2011 article in The Economist (2011), the number of mobile phones and tablets (about 480 million units) surpassed the number of laptops and PCs (about 380 million units) for the first time in 2011. Although the number of PCs in use surpassed 1 billion in 2008, the same article projected that the number of mobile connected devices would reach 10 billion in 2020. Mobile devices such as the iPad, iPhone, and other smart phones and their complete ecosystems of downloadable applicationss, from travel advisories to multi-player games, are transforming different facets of society, from education to healthcare and from entertainment to governments. Other sensor-based Internet-enabled devices equipped with RFID, barcodes, and radio tags (the “Internet of Things”) are opening up exciting new steams of innovative applications. The ability of such mobile and Internet-enabled devices to support highly mobile, location-aware, person-centered, and context-relevant operations and transactions will continue to offer unique research challenges and opportunities throughout the 2010s. Mobile interface, visualization, and HCI (human–computer interaction) design are also promising research areas. Although the coming of the Web 3.0 (mobile and sensor-based) era seems certain, the underlying mobile analytics and location and context-aware techniques for collecting, processing, analyzing and visualizing such large- scale and fluid mobile and sensor data are still unknown.

No integrated, commercial BI&A 3.0 systems are foreseen for the near future. Most of the academic research on mobile BI is still in an embryonic stage. Although not included in the current BI platform core capabilities, mobile BI has been included in the Gartner BI Hype Cycle analysis as one of the new technologies that has the potential to disrupt the BI market significantly (Bitterer 2011). The uncertainty asso- ciated with BI&A 3.0 presents another unique research direction for the IS community.

Table 1 summarizes the key characteristics of BI&A 1.0, 2.0, and 3.0 in relation to the Gartner BI platforms core capa- bilities and hype cycle.

The decade of the 2010s promises to be an exciting one for high-impact BI&A research and development for both indus- try and academia. The business community and industry have already taken important steps to adopt BI&A for their needs. The IS community faces unique challenges and opportunities in making scientific and societal impacts that are relevant and long-lasting (Chen 2011a). IS research and education pro- grams need to carefully evaluate future directions, curricula, and action plans, from BI&A 1.0 to 3.0.

BI&A Applications: From Big Data to Big Impact

Several global business and IT trends have helped shape past and present BI&A research directions. International travel, high-speed network connections, global supply-chain, and outsourcing have created a tremendous opportunity for IT advancement, as predicted by Thomas Freeman in his seminal book, The World is Flat (2005). In addition to ultra-fast global IT connections, the development and deployment of business-related data standards, electronic data interchange (EDI) formats, and business databases and information systems have greatly facilitated business data creation and utilization. The development of the Internet in the 1970s and the subsequent large-scale adoption of the World Wide Web since the 1990s have increased business data generation and collection speeds exponentially. Recently, the Big Data era has quietly descended on many communities, from govern- ments and e-commerce to health organizations. With an overwhelming amount of web-based, mobile, and sensor- generated data arriving at a terabyte and even exabyte scale (The Economist 2010a, 2010b), new science, discovery, and insights can be obtained from the highly detailed, contex- tualized, and rich contents of relevance to any business or organization.

In addition to being data driven, BI&A is highly applied and can leverage opportunities presented by the abundant data and domain-specific analytics needed in many critical and high- impact application areas. Several of these promising and high-impact BI&A applications are presented below, with a discussion of the data and analytics characteristics, potential impacts, and selected illustrative examples or studies: (1) e- commerce and market intelligence, (2) e-government and politics 2.0, (3) science and technology, (4) smart health and

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Table 1. BI&A Evolution: Key Characteristics and Capabilities

Key Characteristics Gartner BI Platforms Core

Capabilities Gartner Hype Cycle BI&A 1.0 DBMS-based, structured content

• RDBMS & data warehousing • ETL & OLAP • Dashboards & scorecards • Data mining & statistical analysis

• Ad hoc query & search-based BI • Reporting, dashboards & scorecards • OLAP • Interactive visualization • Predictive modeling & data mining

• Column-based DBMS • In-memory DBMS • Real-time decision • Data mining workbenches

BI&A 2.0 Web-based, unstructured content • Information retrieval and extraction • Opinion mining • Question answering • Web analytics and web

intelligence • Social media analytics • Social network analysis • Spatial-temporal analysis

• Information semantic services

• Natural language question answering

• Content & text analytics

BI&A 3.0 Mobile and sensor-based content • Location-aware analysis • Person-centered analysis • Context-relevant analysis • Mobile visualization & HCI

• Mobile BI

well-being, and (5) security and public safety. By carefully analyzing the application and data characteristics, researchers and practitioners can then adopt or develop the appropriate analytical techniques to derive the intended impact. In addi- tion to technical system implementation, significant business or domain knowledge as well as effective communication skills are needed for the successful completion of such BI&A projects. IS departments thus face unique opportunities and challenges in developing integrated BI&A research and education programs for the new generation of data/analytics- savvy and business-relevant students and professionals (Chen 2011a).

E-Commerce and Market Intelligence

The excitement surrounding BI&A and Big Data has arguably been generated primarily from the web and e-commerce communities. Significant market transformation has been accomplished by leading e-commerce vendors such Amazon and eBay through their innovative and highly scalable e- commerce platforms and product recommender systems. Major Internet firms such as Google, Amazon, and Facebook continue to lead the development of web analytics, cloud computing, and social media platforms. The emergence of customer-generated Web 2.0 content on various forums, newsgroups, social media platforms, and crowd-sourcing systems offers another opportunity for researchers and prac-

titioners to “listen” to the voice of the market from a vast number of business constituents that includes customers, em- ployees, investors, and the media (Doan et al. 2011; O’Rielly 2005). Unlike traditional transaction records collected from various legacy systems of the 1980s, the data that e-commerce systems collect from the web are less structured and often contain rich customer opinion and behavioral information.

For social media analytics of customer opinions, text analysis and sentiment analysis techniques are frequently adopted (Pang and Lee 2008). Various analytical techniques have also been developed for product recommender systems, such as association rule mining, database segmentation and clustering, anomaly detection, and graph mining (Adomavicius and Tuzhilin 2005). Long-tail marketing accomplished by reaching the millions of niche markets at the shallow end of the product bitstream has become possible via highly targeted searches and personalized recommendations (Anderson 2004).

The Netfix Prize competition2 for the best collaborative filtering algorithm to predict user movie ratings helped gener- ate significant academic and industry interest in recommender systems development and resulted in awarding the grand prize of $1 million to the Bellkor’s Pragmatic Chaos team, which

2Netflix Prize (http://www.netflixprize.com//community/viewtopic.php? id=1537; accessed July 9, 2012).

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surpassed Netflix’s own algorithm for predicting ratings by 10.06 percent. However, the publicity associated with the competition also raised major unintended customer privacy concerns.

Much BI&A-related e-commerce research and development information is appearing in academic IS and CS papers as well as in popular IT magazines.

E-Government and Politics 2.0

The advent of Web 2.0 has generated much excitement for reinventing governments. The 2008 U.S. House, Senate, and presidential elections provided the first signs of success for online campaigning and political participation. Dubbed “politics 2.0,” politicians use the highly participatory and multimedia web platforms for successful policy discussions, campaign advertising, voter mobilization, event announce- ments, and online donations. As government and political processes become more transparent, participatory, online, and multimedia-rich, there is a great opportunity for adopting BI&A research in e-government and politics 2.0 applications. Selected opinion mining, social network analysis, and social media analytics techniques can be used to support online political participation, e-democracy, political blogs and forums analysis, e-government service delivery, and process transparency and accountability (Chen 2009; Chen et al. 2007). For e-government applications, semantic information directory and ontological development (as exemplified below) can also be developed to better serve their target citizens.

Despite the significant transformational potential for BI&A in e-government research, there has been less academic research than, for example, e-commerce-related BI&A research. E- government research often involves researchers from political science and public policy. For example, Karpf (2009) ana- lyzed the growth of the political blogosphere in the United States and found significant innovation of existing political institutions in adopting blogging platforms into their Web offerings. In his research, 2D blogspace mapping with com- posite rankings helped reveal the partisan makeup of the American political blogsphere. Yang and Callan (2009) demonstrated the value for ontology development for govern- ment services through their development of the OntoCop system, which works interactively with a user to organize and summarize online public comments from citizens.

Science and Technology

Many areas of science and technology (S&T) are reaping the benefits of high-throughput sensors and instruments, from

astrophysics and oceanography, to genomics and environ- mental research. To facilitate information sharing and data analytics, the National Science Foundation (NSF) recently mandated that every project is required to provide a data management plan. Cyber-infrastructure, in particular, has become critical for supporting such data-sharing initiatives.

The 2012 NSF BIGDATA3 program solicitation is an obvious example of the U.S. government funding agency’s concerted efforts to promote big data analytics. The program

aims to advance the core scientific and technological means of managing, analyzing, visualizing, and ex- tracting useful information from large, diverse, dis- tributed and heterogeneous data sets so as to accel- erate the progress of scientific discovery and innova- tion; lead to new fields of inquiry that would not otherwise be possible; encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastruc- ture; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life.

Several S&T disciplines have already begun their journey toward big data analytics. For example, in biology, the NSF funded iPlant Collaborative4 is using cyberinfrastructure to support a community of researchers, educators, and students working in plant sciences. iPlant is intended to foster a new generation of biologists equipped to harness rapidly ex- panding computational techniques and growing data sets to address the grand challenges of plant biology. The iPlant data set is diverse and includes canonical or reference data, experimental data, simulation and model data, observational data, and other derived data. It also offers various open source data processing and analytics tools.

In astronomy, the Sloan Digital Sky Survey (SDSS)5 shows how computational methods and big data can support and facilitate sense making and decision making at both the macroscopic and the microscopic level in a rapidly growing and globalized research field. The SDSS is one of the most ambitious and influential surveys in the history of astronomy.

3“Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA),” Program Solicitation NSF 12-499 (http://www. nsf.gov/pubs/2012/nsf12499/nsf12499.htm; accessed August 2, 2012).

4iPlant Collaborative (http://www.iplantcollaborative.org/about; accessed August 2, 2012).

5“Sloan Digital Sky Survey: Mapping the Universe” (http://www.sdss.org/; accessed August 2, 2012).

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Over its eight years of operation, it has obtained deep, multi- color images covering more than a quarter of the sky and created three-dimensional maps containing more than 930,000 galaxies and over 120,000 quasars. Continuing to gather data at a rate of 200 gigabytes per night, SDSS has amassed more than 140 terabytes of data. The international Large Hadron Collider (LHC) effort for high-energy physics is another example of big data, producing about 13 petabytes of data in a year (Brumfiel 2011).

Smart Health and Wellbeing

Much like the big data opportunities facing the e-commerce and S&T communities, the health community is facing a tsunami of health- and healthcare-related content generated from numerous patient care points of contact, sophisticated medical instruments, and web-based health communities. Two main sources of health big data are genomics-driven big data (genotyping, gene expression, sequencing data) and payer–provider big data (electronic health records, insurance records, pharmacy prescription, patient feedback and responses) (Miller 2012a). The expected raw sequencing data from each person is approximately four terabytes. From the payer–provider side, a data matrix might have hundreds of thousands of patients with many records and parameters (demographics, medications, outcomes) collected over a long period of time. Extracting knowledge from health big data poses significant research and practical challenges, especially considering the HIPAA (Health Insurance Portability and Accountability Act) and IRB (Institutional Review Board) requirements for building a privacy-preserving and trust- worthy health infrastructure and conducting ethical health- related research (Gelfand 2011/2012). Health big data ana- lytics, in general, lags behind e-commerce BI&A applications because it has rarely taken advantage of scalable analytical methods or computational platforms (Miller 2012a).

Over the past decade, electronic health records (EHR) have been widely adopted in hospitals and clinics worldwide. Significant clinical knowledge and a deeper understanding of patient disease patterns can be gleanded from such collections (Hanauer et al. 2009; Hanauer et al. 2011; Lin et al. 2011). Hanauer et al. (2011), for example, used large-scale, longi- tudinal EHR to research associations in medical diagnoses and consider temporal relations between events to better elucidate patterns of disease progression. Lin et al. (2011) used symptom–disease–treatment (SDT) association rule mining on a comprehensive EHR of approximately 2.1 million records from a major hospital. Based on selected International Classification of Diseases (ICD-9) codes, they were able to identify clinically relevant and accurate SDT associations from patient records in seven distinct diseases, ranging from cancers to chronic and infectious diseases.

In addition to EHR, health social media sites such as Daily Strength and PatientsLikeMe provide unique research oppor- tunities in healthcare decision support and patient empower- ment (Miller 2012b), especially for chronic diseases such as diabetes, Parkinson’s, Alzheimer’s, and cancer. Association rule mining and clustering, health social media monitoring and analysis, health text analytics, health ontologies, patient network analysis, and adverse drug side-effect analysis are promising areas of research in health-related BI&A. Due to the importance of HIPAA regulations, privacy-preserving health data mining is also gaining attention (Gelfand 2011/ 2012).

Partially funded by the National Institutes of Health (NIH), the NSF BIGDATA program solicitation includes common interests in big data across NSF and NIH. Clinical decision making, patient-centered therapy, and knowledge bases for health, disease, genome, and environment are some of the areas in which BI&A techniques can contribute (Chen 2011b; Wactlar et al. 2011). Another recent, major NSF initiative related to health big data analytics is the NSF Smart Health and Wellbeing (SHB)6 program, which seeks to address fundamental technical and scientific issues that would support a much-needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered, and focused on wellbeing rather than disease control. The SHB research topics include sensor technology, networking, information and machine learning technology, modeling cognitive processes, system and process modeling, and social and economic issues (Wactlar et al. 2011), most of which are relevant to healthcare BI&A.

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