Data driven decision making -Milestone 4

Data Driven Decision Making - Milestone 2

Data Driven Decision Making

January 20, 2026

Tertiary Sources

Provost, F., & Fawcett, T. (2019). Data science for business: What you need to know about data mining and data analytic thinking (2nd ed.). O’Reilly Media.

This book is a tertiary source as it summarizes the available researches and professional practices into a comprehensible summary of data based decision making. The authors are renowned specialists in data science, which contributes to the validity of the content. The aim is educative and it will focus on learners and practitioners that are new to the field of analytics based decision making.

This source assists in making sense of the basic ideas like predictive modeling, analytical thinking, which are required to frame the research topic. It also helps to refine the research question by describing how organizations use data insights in practice to make decisions (Provost and Fawcett, 2019).

Marr, B. (2020). Data driven business transformation: How to innovate, scale, and compete using data. Wiley.

The book is a tertiary source as it gives a general overview of data driven decision making implementation in different industries. Marr is a known expert in the field of business analytics, and the publishing source is also reputable, which increases the credibility. Business leaders are the target audience of the book, therefore, it is written in a practical, strategic manner.

The source is helpful in providing some background on how data driven decision making can be applied in the real world. It facilitates the study by explaining how information culture and top management affect the outcomes of decisions and assists in relating theory and organizational practice (Marr, 2020).

Secondary Sources

George, G., Haas, M. R., & Pentland, A. (2019). Big data and management. Academy of Management Journal, 62(2), 319–337. https://www.researchgate.net/publication/274118998_Big_Data_and_Management

This journal article is a peer reviewed secondary source as it examines and discusses available research and theoretical frameworks in relation to big data and decision making. The authors work at reputable academic institutions, which helps in authority and credibility. The article is aimed at a scholarly audience and it provides empirical evidence to prove its arguments.

This source influences this study because it describes the implications that big data has on managerial decision making processes. It assists in addressing the research question by determining the advantages and shortcomings of utilizing data analytics in company decision-making (George et al., 2019).

Sharma, R., Mithas, S., & Kankanhalli, A. (2018). Transforming decision making processes: A research agenda for understanding the impact of business analytics on organizations. European Journal of Information Systems, 27(2), 133–152. https://www.researchgate.net/publication/264089951_Transforming_decision-making_processes_A_research_agenda_for_understanding_the_impact_of_business_analytics_on_organisations

The article is a secondary source in the sense that it is the synthesis of previous research and a framework of understanding analytics driven decision making is proposed. The journal is peer-reviewed, and authors are highly qualified academically in terms of their research in information systems. It is analytical and theory oriented.

The article is relevant to the topic of research as it explains how analytics can alter the speed, quality, and accountability of the decision. It assists in clarifying the research question by bringing out organizational influences on the success of data driven decision making (Sharma et al., 2018).

Primary Sources

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

This article serves as a primary source in situations when utilized as an original testimony to the earlier managerial views on the data driven decision making. The authors provide first-hand interpretations and original ideas as opposed to synapsing with others. Still, despite its age, it is still fundamental to the explanation of how data driven thinking became a part of management practice.

With the help of this source, it is possible to analyze the initial framing of data by the leaders on the basis of decision making. It offers historical background through which it is possible to compare the old expectations and current empirical data.

U.S. Bureau of Labor Statistics. (2023). Business employment dynamics data. U.S. Department of Labor.

The reason why this dataset is a primary source is that it has raw government collected data that is not interpreted. Bureau of Labor Statistics is a very reputable source which possesses clear methodology which favors accuracy and reliability. The information is recent and updated on a regular basis.

With the help of this source, it is possible to directly examine the trends in employment and evaluate the ways, in which organizations can utilize employment data to guide the decision-making. It augments the study by giving actual world information that can be viewed using a data based decision making methodology.

References

George, G., Haas, M. R., & Pentland, A. (2019). Big data and management. Academy of Management Journal, 62(2), 319–337. https://doi.org/10.5465/amj.2019.4002

Marr, B. (2020). Data driven business transformation: How to innovate, scale, and compete using data. Wiley.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

Provost, F., & Fawcett, T. (2019). Data science for business: What you need to know about data mining and data analytic thinking (2nd ed.). O’Reilly Media.