Business Case Analytics

Business Case Analytics

Francisco Cua (FCC Consultants, Inc., Philippines)
Copyright: © 2014 |Pages: 14
DOI: 10.4018/978-1-4666-5202-6.ch034
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Introduction

Analytics is about big or small-data sets. It is also about single or multiple data sources. Big data analytics excites, because the expectation of analytics is in the discovery of meaningful patterns in big data mines with the deployment of software programming, operations research, and statistics (Terry, 2012a). For instance, an analytics spreads as widely as possible across a large number of patients’ characteristics and exhausts every combination of genetic and environmental variables to generate meaningful information and insights. Exciting discoveries in small-data analytics are also possible with hospital registries (Terry, 2012b).

With analytics, the important result is the information gained. The methodology that is deployed to generate the information is also important. For instance, small-data analytics zooms in to gather details of the data set and zooms out for broader patterns. The methodology looks at the “jungle,” while the analysis examines the “trees”.

The business case analytics belongs to small-data analytics (Goldman, 2012; Reiter et al., 2012; SkyFoundry, 2011). It explores one or more data sets to gain information as to how an executive sponsor (the Source) persuades the top management (the Target) to accept a capital investment and fund it (Cua & Garrett, 2009). The persuasion is through the special messages in the business case document (Cua & Reames, 2012). A compelling and concise business case enables the Target to make better and more favorable informed decisions, as compared to instances where there is no business case (Goldman & Schmalz, 2012; Wolfe, 1994).

  • The argument: Business Case Analytics (BCA) is a well-documented field that provides proven research techniques for use in analyzing data in order to craft output for organizational benefits.

Above argument assumes that BCA can provide good information with or without minimal use of software programs. This chapter identifies two keywords, the innovation and diffusion, which appear on the left side of Figure 2 from the argument on the right side of Figure 2. The chain of reasoning to be discussed validates the argument.

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Background

The Business Analytics

Inefficient use of good data and the lack of corporate infrastructure to convert the data to high-quality decision-making information translate to missed opportunities. An important premise is that there is an abundance of good data that the organization recognizes and that the organization can access. Another premise is that the executives of the organization are sophisticate to demand good information. These premises are necessary conditions for the organization to realize the need to establish an infrastructure to transform the good data to good information with the characteristics with regards to “volume, velocity, and variety” (Bartlett, 2013, p. 1). To reiterate, the important result of the business analytics is the information gained for smart analytics-based decisions. Figure 1 depicts the process of business analytics with the smart analytics-based decisions as the expected final outcomes. The business analytics is a transformation process of good data to good information. It expects sophisticated decision makers to make smart analytics-based decisions. The antecedents to the process are the presence of good abundant data for analysis and the presence of tactical applications and the integration of the analytics (a process) into the organizational practices and business processes. The competence, creativity, and industry knowledge of the people and the organizational culture moderate the business-case analytics process.

Figure 1.

The intention and moderators of business analytics

Key Terms in this Chapter

Big-Data Analytics: Deploys software programming, operation research, and statistics, analyses big data mines, and spreads the analysis as wide as possible across a large number of variables and exhausts every combination of these variables to generate meaningful visual information and insights.

Linkage Assumptions: Are inferences derived from the reasons to deploy a project. Collectively, the set of assumptions constitute a bridge connecting the reasons and the conclusions in the business case.

Business Case Process: Refers to the project-setting (or the agenda-setting) phase of contemplating the deployment of a capital investment initiative (such as an ERP), request for information (RFI), request for proposals (RFP), evaluation of the information gathered from the RFI and RFP documents, and the writing of a formal written document to be submitted to the Board of Directors or Trustees for the approval of the project.

Business Case Analytics: Illustrates a form of small-data analytics. Similar to big-data analytics, the important result is the information gained from the analytics. Unlike big-data analytics that necessarily deploys tools, such as software programming, operation research, and statistics, to generate meaningful visual information and insights from every exhaustive combination of variables, business case analytics deploys methodology that looks at the business case document and other auxiliary data as the “jungle” and examines the “trees.”

Analytics: Is the process of analyzing single or multiple sets of big or small data to discover meaningful pattern in them. Refer to the definition of big-data analytics, business case analytics, and small-data analytics.

Value: Is an abstract idea that generates positive or negative bias. The biased perception of the value or attributes of the innovation in term influences choices and behaviors into believing that the object of the change is or is not worthwhile, such as: the quality of information, the availability of information, network linkages with stakeholders in the value chain, efficiency, flexibility, reporting compliance, means to achieve vision and excellence, and security.

Small-Data Analytics: Refers to business case analytics.

Business Case Document: Is a formal written document prepared by the executive championing a project. A good business case is simple and straightforward, and uses reason, emotion, personality and character. It takes the form of textual and numerical information, and or spoken word in order to inform, persuade, and motivate the target audience to support or approve the case.

Business Case: Refers to either a business case process or a business case document.

Executive Sponsor: Is the owner of a project. He or she is the project sponsor or the project champion. The person is responsible to the organization to overcome resistance and to ensure the success of the project. In an ERP project, the executive sponsor can be the Chief Finance Officer or somebody below this position.

Diffusion: Is a process of communicating a new idea within a social system, such as an organization. A successful diffusion culminates in the adoption of the idea.

Diffusion of Innovations Theory: Concerns how, why, and at what rate the new idea (the innovation) is diffused and adopted.

Perceived Attributes of the Innovation: Are the positive or negative biases that decision makers perceive important when they make judgment to accept or reject the adoption of an innovation. These attributes (Set 1) may be real or imaginary. Regardless, it is the perception of the presence of these attributes that matters. The general perceived attributes are the relative advantage, compatibility, complexity, trial-ability, and observe-ability. There is another set (the Set 2 positive or negative biases) that pertains to users, such as perceived usefulness or perceived ease of use.

Due Diligence: Is a standard of care involved in the investigation of a business case prior to signing a legal obligation.

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