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Data and decision-making

This is an excerpt from Applied Sport Business Analytics With HKPropel Access by Christopher Atwater,Robert E. Baker & Ted Kwartler.

Data-driven, data-informed, and data-inspired are terms that describe the interaction between the circumstances and methods of data usage (Stewart, 2019). Being data-driven requires the precise data needed to make a decision, yielding an exact answer. Being data-informed requires an awareness of the current metrics in order to inform strategies. Being data-inspired requires predictive inference and trendspotting derived from multiple data sources.

Data-driven decision-making uses data meant to answer a very specific question. Data-driven strategies indicate that the data to determine the outcome is available. Data-driven decision-making requires the most specific type of metrics and is rigid in its data use. For example, the data to be analyzed is predetermined, along with the methodology to ensure a properly implemented plan for measurement. Generally, large sampling is required to ensure stability and replicability. Knowledge of statistical methodologies are essential (Stewart, 2019).

Data-informed decision-making strategies imply that data analyses are employed along with experiential and other factors in the decision process. Data-informed decision-making requires existing knowledge of key performance indicators (KPIs). Upward and downward trajectories are observable and explainable, reflecting both the what and the why (Stewart, 2019). Data-informed analyses are used to refine and inform future strategies—for example, when addressing inevitable organizational or environmental changes (Fullan, 2008). Employing a hypothesis-driven analytics approach informs decision-makers on why a particular strategy will work. Stewart (2019) noted that, in opposition to a data-driven approach, a data-informed analysis does not form a conclusive course of action; rather, it contextualizes and informs new strategies.

Data-inspired decision-making is exploratory in nature, imposing no expectations on specific outcomes. Generally, data from multiple sources is used and commonalities across data sources are sought. Often data-inspired analytics draw on intuition and inference as opposed to concrete, statistically sound methodologies of data-driven and data-influenced analytics. Whereas data is readily used to reveal impacts of the past and project possible futures based on past trends, it is employed less frequently for the creation of innovative ideas. However, data-inspired strategies can analyze disparate metrics and be used to inspire new ideas. Data-inspired processes will not determine a conclusive action, but they can reveal concurrent trends and broader context than the aforementioned methodologies. Data-inspired metrics are not concrete and could reflect spurious interactions.

There are risks and limitations in each of these analytics approaches, yet each serves its purpose, ultimately contributing to the success of an organization.

Also contributing to organizational success, program evaluation is directed toward understanding the successes and failures of a program through a repetitive, systematic, organized approach to gathering and analyzing information to identify the factors that contribute to the program, the decisions that need to be made, and the actions that need to be taken based on the findings of the evaluation process (Durning and Hemmer, 2010). Evaluation involves the review, analysis, and application of data gathered. Clearly, data analytics is a tool in program evaluation, ultimately informing and guiding organizational decisions.

There are two distinct approaches to, and purposes for, evaluation: summative and formative. Summative evaluation, or summative assessment, focuses on the outcomes of a program or its KPIs. In sport, the final score is a summative outcome. Formative evaluation, or formative assessment, summarizes the participants’ development at a specific time in a process of continual improvement. For example, employees who demonstrate effectiveness may be formatively evaluated and provided additional tools for ongoing development. Both summative and formative evaluations are connected to data analyses in that they are evidentiary metrics and used to determine program or participant effectiveness. The emphasis on data allows decision-makers to focus on evidence and continual improvement. The application of data analytics in the form of program evaluations and KPI assessments ensures that organizations and participants are doing what they say they are doing and determines how well they are doing it. Learning organizations are those that employ evidentiary assessments to pursue continuous improvement.

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