- 1.1 The Origin and Evolution of Business Analytics
- 1.2 Developing Analytical Thinking
- 1.3 Operationalizing Big Data from Global Perspectives
- 1.4 Extracting Useful Information from Big Data
- 1.5 Unique Challenges for Business Analytics
- 1.6 Capitalizing on Business Analytics for Building a Winning Global Strategy
1.6 Capitalizing on Business Analytics for Building a Winning Global Strategy
While leading economies (e.g., U.S., Germany, United Kingdom) in Europe and North America gradually lost their economic clout due to their stagnant domestic markets for the past decade, emerging economies (e.g., China, India, Brazil) in Asia and Latin America have begun to flex their economic muscles thanks to their rapid population growth and increasing purchasing power. To cope with this economic power shift, a globalization of business activities has become a norm for many companies. A typical rationale for going global includes the following: (1) the expansion and diversification of customer bases; (2) the extension of product life cycles due to a possibility that established but waning products in one market can turn into hot-selling products in another overseas market; (3) spreading financial and market risks across the countries; (4) less competition in untapped but emerging foreign markets; (5) cost-saving opportunities in low-cost countries. Despite the aforementioned benefit potential, an entry into a foreign marketplace can bring a myriad of unforeseen risk, uncertainty, and headaches. For instance, since business customs and customer behavior would differ from one country to another, marketing strategy has to be tailored toward each local market. Laws and regulations governing business activities will vary from one country to another, and thus business activities can be further constrained with few options. Due to a geographical dispersion, the cost of logistics will be higher and a supply chain associated with the global flow of goods and services will be stretched further with a greater complexity. In a global marketplace, a margin for error would be small due to unfamiliarity with local practices and business culture. That is to say, business success in a global marketplace rests heavily on the firm’s ability to make a right strategic decision based on the right information at the right time. Such ability can be enhanced by the smart use of big data (namely, business analytics). Thus, the role of business analytics in a global business setting is greater than in a domestic setting. This means that the exploitation of business analytics should be embedded within global business strategy.
Despite a growing significance of business analytics to global business success, the recent SAP survey reported that a mere 27% of U.S. firms had a plan for the use of business analytics or any form of business intelligence tools, and only 13.5% of the surveyed firms used business analytics on a daily or ongoing basis (Primault 2012). A lack of business analytics application may be attributed to the user’s unfamiliarity with this tool, unproven benefits, implementation cost concerns and hassles, internal resistance against the adoption of a newly introduced tool, and a difficulty in leveraging it as the competitive differentiator. To overcome these hurdles, the potential users of business analytics should identify key success factors and then formulate business analytics implementation strategy as part of their global business strategy. Figure 1.2 displays a list of key success factors for business analytics.
Figure 1.2 Success pillars of business analytics.
The details of key success factors are described here:
- Information and Communication Technology (ICT) Infrastructure: Valuable data cannot be captured, stored, and analyzed without the support of computer systems (both hardware and software), multimedia communication tools, and analytical tools. As such, proper investment in such supporting systems (i.e., ICT infrastructure) is essential for the successful use of business analytics. Since both overinvestment and underinvestment can hurt the business bottom line, in that the former will lead to wasted resources, and the latter will limit ICT capabilities, a careful investment strategy should be developed by involving all the affected parties and users who will share the cost and benefit of the ICT investment.
- Database Management System (DBMS): DBMS is the system software that enables the users and programmers to create, store, retrieve, modify, update, and manage data in an organized fashion. Without the DBMS, the business analysts cannot get access to data and lose the opportunity to interact with data to extract meaningful information. Also, the DBMS gives the business analysts a chance to maintain the concurrency, security, privacy, integrity, and relevancy of collected data. Furthermore, as the central storage of data, the DBMS allows its users to use the same data and thus obviate the potential confusion and inconsistency emanating from multiple data sources.
- Data Discovery Governance Policy: The companies are becoming increasingly serious about the notion of “data as an asset” as they face increasing pressure for reporting a “single version of the truth.” In a 2006 survey of North American firms that had deployed business analytics, a program for the governance of data was reported to be one of the five success factors for deriving business value from big data (Khatri and Brown 2010). As such, one of the important prerequisites to business analytics is to develop disciplined rules and clear guidelines as to who will be collecting data, which data should be collected, who will be responsible for monitoring and measuring data quality, and how data will be collected, stored, and maintained to ensure its integrity and security.
- Business Analytics Platforms: Business analytics can be powered by many different choices of platforms provided by various IT vendors (e.g., Oracle, SAP, HP, IBM, and Cloudera). These different platforms come with different functionalities, different cloud options, different levels of memory and stream-analysis capabilities, and varying data processing power. Thus, a caution should be exercised in selecting multiple platforms. This caution includes the establishment of specific selection criteria (e.g., architecture, query engine, security, scalability, disaster recovery) and the integration plan of utilizing multiple platforms complementing each other.
- Knowledge Management: After insights are gained through business analytics, those will be the sources of the company’s knowledge properties for a long time to come. To keep them as the valuable assets and leverage them as the competitive differentiator, the knowledge has to be classified in terms of its content and format and managed accordingly. In addition, the company should ensure that knowledge properties will not be lost or released to its rivals during knowledge-sharing activities.
- Continuous Improvement: No competitive advantage will be permanent, since it is a relative concept. Likewise, a competitive edge created by business analytics cannot be sustained forever, unless more timely, more accurate, and deeper insights are created through the continuous upgrade of business analytics tools and their supporting mechanism. Thus, it is necessary to build the closed-loop framework containing “plan, do, act, check, and improve” cycles of business analytics application.
As discussed previously, the best way to maximize business analytics is to incorporate it as part of the global business strategy and then develop specific action plans for its successful implementation.