Recently, business intelligence and analytics has gained much interest among practitioners and researchers because of many cases of success and improvements reported in the organizational performance. Business intelligence and analytics helps develop techniques, methods, and practices that analyze the business data and enhance an understanding of the markets and the business. This knowledge contributes to improving services and products offered to customers, as well as helps achieve greater operational efficiency and nurture customer relations. Currently, a crucial study area reflects the impact and magnitude problems experienced in the contemporary business organizations. Success stories, however, depend on the technologies that support and influence the decision-making process of an organization.

Two interrelated fields, namely business intelligence and analytics and big data analysis, have become significantly important in business and academic communities over the past twenty years. This crucial development has been highlighted by the industry studies. For instance, according to the 2011 IBM Tech Trends Report, which was based on a survey conducted by more than 4000 professionals of information technology (IT) from 25 industries in 93 countries, business analytics was among the major trends of the 2010s technology (Chen Chiang, & Storey, 2012). Additionally, according to a survey done by Bloomberg BusinessWeek in 2011 regarding the state of the business analytics, about 97 percent of all companies that had revenue above 100 million dollars were using the system of business analytics (The Economist, 2011). However, a report written by McKinsey Global Institute projected that the United States will have a shortage of between 140,000 and 190,000 personnel with excellent analytical skilled by 2018 (Chen et al., 2012). Notably, about 1.5 million managers with data knowledge will not be sufficient to analyze huge amount of data before making practical decisions. Hal Varian, the Professor at the University of California and Google’s Chief Economist, remarked that students and IT professionals would benefit from many emerging opportunities in data analysis (Chaudhuri, Dayal, & Narasayya, 2011). Numerous opportunities for data analysis in many organizations have generated considerable interest in business intelligence and analytics. The paper explores the opportunities and challenges of business intelligence and analytics. It also provides an insight into the applications, evolution, and future research opportunities that will assist in better business decision making.

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The term business intelligence and analytics dates back to 1989 and it has gained popularity in use among academicians and IT practitioners in the last twenty years. This term refers to technologies, practices, techniques, applications, systems, and methodologies used to analyze important business data with the aim of understanding the market and business of an organization to make effective business decisions on time (Watson, 2010). Besides the primary purpose of analytical technologies and data processing, business intelligence and analytics entails business-centric methodologies and practices applied to such applications as e-government, security, e-commerce, market intelligence, and health care. Initially, business intelligence was used to describe methods and concepts with the aim of improving the decision-making process in a business through support systems based on facts.

Business intelligence also denotes important databases, architecture, methodologies, applications, and tools. The main objectives of business intelligence are enabling easy and interactive access to various data, empowering data transformation and manipulation, and providing analysts and business managers a way to perform suitable actions and analysis (Shanks, Sharma, Seddon, & Reynolds, 2010). The resourcefulness of business intelligence is used in large industries such as airlines, health care, telecommunication, and IT firms.

Business intelligence is a data-based application; thus, it relies on different data extraction, analysis, and collection technologies. The techniques for collecting, analyzing, and extracting data are referred to as business analytics. The foundation of business intelligence is data warehousing. Therefore, tools and data marts are usually designed to transform, load, extract, integrate, and convert specific data of an enterprise. Exploring characteristics of relevant data, a company needs to adopt tools for advanced reporting, data query, and online analytical processing (Davenport & Harris, 2012). With the current business intelligence that greatly depends on data analytics, it is appropriate to combine the two terms, business intelligence and business analytics, into business intelligence and analytics.

Since 2004, web analytics, web intelligence, micro-blogging, and social networking have introduced a new age of business intelligence, which can be referred to as BI 2.0. Many companies and industries can now gather relevant product and consumer information from business web or databases ( The Economist, 2011). This information is visualized and organized through a web portal, multilingual retrieval methods, and mapping knowledge.

Web analytics methods, for instance, Google Analytics, analyze clickstream data logs of a customer to track online activities of users and reveal their purchasing and browsing patterns. Therefore, companies and industries can quickly understand the needs of their customers, hence making such products available in the market. Web analytics can assist in the design of the website, product recommendations, analysis of customer transaction, and optimization of product placement (Chen et al., 2012). Thus, business intelligence and analytics evidently helps companies make best decisions regarding products and services needed by their customers.

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Scholars’ Opinions on Business Intelligence and Analytics

In any business situation, there are different ways of how business intelligence and analytics can be used or considered interchangeably. Business analytics is a set of processes and techniques that can be used in analyzing data to improve business performance through making fact-based decisions. Moreover, it is considered to be a subset of business intelligence, which provides companies with an ability to compete in the market successfully. Through analytic reasoning, organizations advance their knowledge to support the decision-making process. In the book Competing on analytics: The new science of winning, Devonport and Harris (2012) argue that recent studies have revealed that most organizations lack sufficient information needed for proper decision-making. Lack of knowing how to use analytics is the main hindrance in adopting it. Therefore, business performance cannot be derived properly because there is a lack of business analytics that uses statistical management and operations research tools.

Nevertheless, business intelligence and analytics ideas have gained more interest among experts and researchers owing to several issued success cases which report huge improvements in the performance of organizations (Shanks et al., 2010). Business intelligence and analytics systems offer support for collecting and transforming information with a particular emphasis on improving decision-making in the business.

Furthermore, decision support systems research area receives acknowledgment it deserves from its relationship with the business intelligence and analytics systems which deal with systems of information and support decision making. At the same time, the current understanding of business intelligence and analytics has been molded by research and developments of decision support systems in areas like executive information systems, decision support systems, and data warehouses (Watson, 2010). Organizations are supposed to focus on the decision-making process to realize performance benefits from business intelligence and analytics systems. The successive utilization of business intelligence and analytics technologies depends highly on organizational integration during decision-making processes. Therefore, understanding how the technology works is vital to the success of a business.

Different Characteristics of Business Intelligence and Analytics

One of the business intelligence and analytics characteristics is that it depends heavily on different data gathering, extraction, and analysis knowledge and skills (Chaudhuri et al., 2011). The currently adopted business intelligence and analytics technologies can be considered the latest generation in the industry. The data collected through various systems are structured and often stored in commercial relational database management systems.

Additionally, the considered foundation of business intelligence and analytics is warehousing and data management. In data management, the essentials for integrating and converting enterprise-specific data are the design of tools and data marts for extraction, transformation and load (Davenport & Harris, 2012). Moreover, in various applications of different businesses reporting functions, techniques of data mining and statistical analysis are accepted for data clustering and segmentation, association analysis, predictive modeling, anomaly detection, and regression and classification analysis. The leading commercial platforms of business intelligence and analytics have incorporated the technologies of data analysis and processing in their systems.

By using the technologies developed, an enormous amount of information about different industries, companies, products, and customers can be found on the web, visualized using various techniques of web mining. As mentioned earlier, analytic web tools such as Google Analytics can perform customers’ clickstream data logs analysis and reveal a trail of online activities, thus providing their patterns of browsing and purchasing (Davenport & Harris, 2012). Hence, the web analytic can design, optimize, place, and recommend products, conduct customer transaction analysis, and make a market structure analysis.

Moreover, the development of Web 2.0 created more user-generated subjects from different online social media games and virtual worlds. Timely opinions and feedback from a broad range of customers can as well be gathered through Web 2.0 applications, thus capturing everyday event references and celebrity chatter.

With business intelligence and analytics technologies in mind, a good number of marketing researchers agree that analytics of social media present an exceptional opportunity for the market to be treated as a conversation between customers and interested businesses (Watson, 2010). Compared to the previous business intelligence and analytics technologies and applications integrated into the commercial enterprise systems, the new system will require advanced and scalable integration techniques in text mining, network analysis, web mining, and spatial-temporal analysis.

Different Perceptions of Related Themes and Arguments

As argued in the article Beyond the PC by the Economist (2011), there are unforeseen integrated commercial new business intelligence and analytics systems. It is evident whereby community businesses and industries have made significant steps in adopting business intelligence and analytics for their personal needs. The information system community is facing unique opportunities and challenges, hence the need for making appropriate and long-lasting societal and scientific impacts (Chen et al., 2012). Education and research programs on information systems need to be evaluated carefully for future action plans and curricula.

Application of Business Intelligence and Analytics where Issues Exist

The use of an enormous amount of data in achieving a tremendous impact is one of the applications of business intelligence and analytics systems. The help of some global IT and business trends has shaped the past and the present of the business intelligence and analytics’ research directions. In addition to fast worldwide IT connection, enterprise data creation and consumption have been facilitated greatly by the improvement and distribution of business-related electronic data interchange, data standards, and databases.

Recently, the big data era has reduced gently in different communities, ranging from health organizations and governments to e-commerce. The main comprehensive understanding can be found from the detailed and contextualized contents of relevance to any particular organization or business (Davenport & Harris, 2012). Business intelligence and analytics system is data driven, and its application can influence opportunities offered by enormous amounts of data required for numerous critical application areas. The promising business intelligence and analytics applications combine analytic and data characteristics.

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E-commerce and market intelligence. This application has brought the excitement surrounding big data and business intelligence and analytics, significantly transforming the market by the leading vendors in electronic commerce such as eBay and Amazon. It has been done through various highly innovative e-commerce stands and systems for a product recommendation. The major leading firms in the Internet business, such as Facebook, Amazon, and Google, continue to be ranked top in the development of platforms, social media, cloud computing, and web analytics (Chaudhuri et al., 2011).

Various newsgroups, forums, and crowd-sourcing systems are offering the materialization of customer-generated Web 2.0, a new research opportunity. Therefore, it provides a platform for the investors, employees, media, and customers to interact. The modern e-commerce system collects less structured, rich data which have customers’ behavioral and opinion information (Shanks et al., 2010). For the social media analysis of opinions of customers, sentiment and text analysis techniques are usually adopted. For systems such as product recommender, various methods for analysis have been developed, including graph mining, anomaly detection, and database clustering and segmentation. In the end, it has become possible to reach millions of small markets in business through searches which have highly personalized recommendations.

E-government and politics 2.0. The introduction of Web 2.0 has caused significant excitement concerning the redefining of governments. The first sign of the success of e-government and politics was in 2008 during the U.S. elections, political participations, and campaigns (Davenport & Harris, 2012). Currently, multimedia websites are highly used by politicians for campaign advertisements, event announcements, online donations, and policy discussions. Due to the fact that the processes of government and politics become more transparent, there are more opportunities to embrace business intelligence and analytics.

Online political blogs, participation, transparency, accountability, and e-democracy can be supported by an analysis of social networks and social media. To  serve the people better, there is the development of applications of e-government with a directory of semantic information. Despite the efforts of transforming e-government to significant heights, there is little academic research since e-government is mainly involved in public policy and political science (Davenport & Harris, 2012). The political blogosphere provides a simpler way of dealing with extensive information, for example, “OntoCop” system which helps organize and summarize public comments online.

Science and technology. This area of specialization experienced crucial improvements owing to environmental research, oceanography, astrophysics, and genomics. To enable data analytics and information distribution, the National Science Foundation has recently mandated the provision of data plan management. Cyberinfrastructure has become sensitive in supporting such type of data-sharing. The best example of the U.S. governmental financially supporting agency is the 2012 NSF BIGDATA which aims at the massive data analytics promotion (Shanks et al., 2010). The program’s purpose is to advance the essential technological and scientific means of analyzing, managing, visualizing, and extracting useful information from large and heterogeneous data sets. It also assists in the acceleration of scientific innovation and discovery, leading to new and better possibilities. It will involve the development of new analytic tools, as well as the improvement of sustainable understanding of the social process and human interactions.

Some scientific and IT disciplines, in particular, have already implemented huge data analytics. For example, there was introduced the use of cyber-infrastructure by iPlant Collaborative. The iPlant is intended to support the current generation of well-equipped biologists in using the ever-expanding computational techniques and data sets to address major issues of the biology of plants (Watson, 2010). Moreover, in astronomy, the Sloan Digital Sky Survey clearly depicts how big data and computational methods can assist in the facilitation of decision and sense making at both macro and micro levels in a fast expanding worldwide field research. In the astronomy history, the Sloan Digital Sky Survey can be considered one of the most influential and determined studies.

Smart health and well-being. In the health society, there is a considerable number of patient points of contact which generate vast amounts of health and health-related contents using the advanced medical equipment. It poses a significant challenge in extracting information from health big data, considering the Health Insurance Portability and Accountability Act and International Review Board rationales for constructing a responsible health organization (Watson, 2010).

The health analytics is developed slowly because it hardly takes advantage of the computational platform. However, over the years, there has been an adoption of electronic health records. Such a collection allows solid clinical knowledge and understanding of patient disease patterns. To provide more insight, longitudinal electronic health records can be used to determine associations in the diagnosis and to consider the chronological relationship between different events to identify better disease progression patterns (Chen et al., 2012).

Furthermore, social sites like Daily Strength provide exclusive opportunities for health care patient empowerment and decision support, especially concerning chronic illnesses, including cancer, Alzheimer’s, and diabetes.

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Security and public safety. Research on the safety has gained more attention over the years, especially after the events that occurred on September 11, 2001 (Davenport & Harris, 2012). All individuals belonging to various areas of expertise have been assembled together to combine efforts for a first security upgrade, which presupposed fighting terrorism, cyber-crimes, violence, and other online security concerns. Moreover, they identified important mission areas where one can share information about any illegal activity or advise on certain security matters.

A significant number of financial resources were spent on dealing with security matters, such as gathering information from multiple sources. Therefore, different companies now have to spend more money on defending themselves against cybercrime as a part of safety protocols (The Economist, 2011). Thus, it is an obligation in any sector to have enough intelligence on how to deal with cyber-crimes.

Solutions and Recommendations

With emerging applications which seem to focus on the improvement of systems, there are extra factors to consider while implementing business intelligence and analytics. Integrated systems work well in dealing with issues; thus, there is a need to combine skills and knowledge. It will help in correlating different opinions that various individuals have for the betterment of the whole system at large.

Additionally, there can be developed a dynamic system that can crosscut across all sectors in the system, hence same issues can be combined and addressed together. It should be promoted by the essence of learning by doing. While dealing with massive amounts of information, clustering and combining similar data will assist in retrieval and dissemination of information whenever it is needed (Shanks et al., 2010). Currently, most companies either have much unnecessary information which they do not need or lack essential data needed.

When dealing with information systems which were primarily focusing on business needs, the attention should be shifted to management strategies to ensure that the internal and external planning process in the management of business intelligence goes smoothly. Therefore, the skills and knowledge needed should be considered and discussed (Watson, 2010). Furthermore, there is a need to improve the IT education by offering professional online IT courses with a possibility to receive a certificate after the completion. Finally, while dealing with cyber-crime, online certification of programs should be made mandatory to have fewer online crimes.

Future Directions

Business intelligence and analytics has three main emerging trends, including text analytics, network analytics, and web analytics. Network analytics, an evolving area, entails computational models for analysis of social network and online community. Web analytics, an emerging active field in business intelligence and analytics, introduces statistical analysis and data mining on NLP models and information retrieval (Chaudhuri et al., 2011). Through web search engines, it has helped locate Internet-based technologies for website ranking, crawling, and web page updating. Finally, text analytics is the unstructured portion of the content that an organization collects. Usually, it is in the form of the text, ranging from e-mails and organizational documents to social media and web page content. These three areas, however, need to be improved to cope with the changing needs; thus, these future directions are necessary to consider in business intelligence and analytics.

Text Analytics

In the future, there is a need to find ways to conduct text analytics in an unstructured data. The text analytics has already been successfully implemented in written documents, such as government documents, news, and company reports. However, in social media and web, text generated by the user typically contains spelling and grammar errors, abbreviations, mixed languages, and emotions. Solutions for a standard text analytics frequently do not work well with such data (Chen et al., 2012). Moreover, popular micro-blogging sites such as Twitter impose message length limits, hence limiting the context of the text which readers need to understand. The task of text analytics, therefore, is to address this challenge by introducing other research approaches such as developing a duo-lingual site that recruits users to translate texts manually as they learn other languages.

Future research should also consider how text analytics can work for stream data. Stream data are produced by applications or online sensors. Then, they are received and managed by applications of business intelligence and analytics in the actual time. Many techniques of data analytics for structured data are usually performed in numerical computations (Watson, 2010). However, there are a few data analytics techniques for unstructured data streams. The amount of text data collected from social media and the web is increasing every day. Thus, the research on the future data stream needs to focus on extracting and summarizing unstructured data and text for detecting important topics and events.

Network Analytics

Furthermore, future research should concentrate on extending the present network analytics techniques to deal with the compound systems. The majority of the network analytics that already exists can be used only in simple systems that have a single node type and edge type. However, networks can often be multidimensional with different node types such as products, buyers, and sellers. Moreover, they can have various labeled links such as seller-sells-goods and buyer-purchases-goods (Chen et al., 2012). When predicting mining link relationships in complex networks, there is a need to consider the idea of different link types and nodes. As a result, social recommendation and community detection should be performed in various complex systems since different nodes can interact in a various way depending on the link type.

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Additionally, future research should focus on distinguishing network selection from social influence. Marketing and social recommendations are applications that use network analytics and assume that network users in proximity to one another have the same preferences. The similar effect can be achieved through social influence and self-selection mechanisms. The latter presupposes that users with same characteristics commonly form social links while the former means that related users inspire one another to have the same preferences (Davenport & Harris, 2012). These two mechanisms are difficult to distinguish, however, finding the cause of this similar effect will allow suitable recommendation approaches that will help attain set business goals of choosing and tracking customers. Thus, this area still needs improvement and development in the future.

Web Analytics

Hadoop is a highly efficient application for managing big data, especially those structured in computed and simple summarized statistics applications. Nevertheless, Hadoop is not used for analysis of complex data that have various record comparisons and massive data movement on different servers. Business intelligence and analytics needs to develop new image indexing, unplanned one-time processing, and advanced text analytics in Hadoop environments to process unstructured data (Shanks et al., 2010). For the future research, there is a need to investigate whether Hadoop can be used for an individual business analytics application if different requirements for the company analytics application are provided. Therefore, there is a necessity to understand the limitations and strengths of the Hadoop framework before analytics algorithms and models are developed. It will help discover whether the Hadoop framework can be applied in analytics operations, specifically in data post and pre-processing.


Business intelligence and analytics is a field of study that has attracted the attention of many research communities, computing industries, and enterprises because of the new needs of the business and the available big data. Various business intelligence and analytics systems and tools are present in the market, and many organizations are using them to make better business decisions. However, there is still room for future development and improvement of existing business intelligence and analytics tools and systems to address issues that come with new mobile technologies, data genres, and computing paradigms. There exist three top business intelligence and analytics trends which need to be taken into consideration in the future to address future issues and problems. They include the network analytics, text analytics, and web analytics.

In response to the challenges experienced in business intelligence and analytics field of study, several recommendations were highlighted. For instance, there is a need to enhance IT education by providing courses related to business intelligence and analytics, such as the web, data, text mining, as well as network and predictive analytics. These courses will also contribute to the training of the new personnel as the scholars predict that there will be a shortage of efficient data analysts if nothing is done. Moreover, the increasing necessity for innovating businesses through business intelligence and analytics calls for research collaboration between academia and industries. These two fields need to emerge to accelerate the pace of technology transfer and research discoveries.

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