Data Analytics for Strategic Management: Getting the Right Data

Data Analytics for Strategic Management: Getting the Right Data

Lesley S. J. Farmer (California State University Long Beach, USA)
Copyright: © 2017 |Pages: 13
DOI: 10.4018/978-1-5225-1049-9.ch056
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To direct and maintain smooth operations in coordination with internal and external sectors, strategic managers need to collect and analyze data about those operations and stakeholders so they can improve current management practices and determine new management direction. Particularly in today's data-driven society where evidence-based practice is expected, numbers and other evidence abound. However, data by itself is not very useful or even informative. Managers need to strategically conduct data analytics, that is the process of knowing the right questions to ask, determining the relevant data to collect, choosing the appropriate instruments to collect those data, analyzing that data, recommending appropriate actions, implementing them, and evaluating the implementation. This chapter also emerging technology issues and tools that impact data analytics.
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Theoretical Framework


Every organization requires effective management in order to optimize the production and operation of resources and services. Using a systems perspective, an organization may be considered as a managed system of interdependent parts (Kühl, 2013). As the system receives inputs, such as students and resources, it acts upon those inputs and transforms them into outputs, such as winning teams and prepared graduates. Organizations are configured to optimize their systems to achieve their goals. Their structures define how resources will be allocated and how tasks will be performed on those resources. The organizational structure involves division of labor, rules of authority and responsibility, integration of resources and efforts, monitoring and regulation, and norms of behavior. Within this system, managers are responsible for functions that advance the organization.

Collins (1993) identified ten key managerial roles that fit into three clusters.

  • Interpersonal Roles: Figurehead who carries out symbolic duties, leader of people, liaison with external information networks

  • Informational Roles: Information monitor, information disseminator, spokesperson who transmits information to outsiders

  • Decisional Roles: Entrepreneurial change agent, disturbance handler, resources allocator, and negotiator.

These roles are performed at three levels:

  • Operational: Supervising non-management personnel, and producing products and services

  • Tactical: Translating organizational goals into specific program objectives and activities, and coordinating resources

  • Strategic: Managing interactions with the external environment.

Collins also noted that these roles are only as effective as the manager self-reflects and improves his or her own behavior.

Data touches each of these roles. Norris and Baer (2013) assert that “data expands the capacity and ability of organizations to make sense of complex environments” (p. 13). Even though managers consider data analytics as a priority, “data access and management are proving to be significant hurdles for many institutions” (Macfadyen et al., 2014, p. 22). Data issues of quality, ownership, access, and standardization are barriers to implementing data analysis. While some of these factors may be beyond the manager’s control, some effort can be made to gather and standardize available relevant data.

Key Terms in this Chapter

Stakeholders: Persons and groups who have an investment, interest or concern in an organization.

Quantitative Data: Numerical data.

Data Analytics: A scientific and systematic approach to examine raw data in order to draw valid conclusions about them. Data are extracted and structured, and qualitative and quantitative techniques are used to identify and analyze patterns.

Evidence-Based Practice: Integrating expert opinion, external scientific evidence, and client values in daily service and decision making.

Altmetrics: Alternative metrics; non-traditional ways to measure the impact of a scholarly work.

Web Analytics: The process of analyzing the behavior of visitors to a Web site.

Variable: Factor (e.g., staff, hours of operation, circulation of materials, number of computers).

Internet of Things: Internet property that enables objects have network connectivity, which allows them to send and receive data.

Qualitative Data: Non-numerical data (e.g., interviews, videos, artifacts).

Geographic Information System: An electronic tool used to store, view, and analyze geographical and statistical information.

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