Data-driven Decision Making

Start Date: 11/17/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/decision-making

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Course Syllabus

In this module you'll learn the basics of data analytics and how businesses use to solve problems. You'll learn the value data analytics brings to business decision-making processes. We’ll introduce you to a framework for data analysis and tools used in data analytics. Finally, we’re going to talk about careers and roles in data analytics and data science.

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Course Introduction

Welcome to Data-driven Decision Making. In this course you'll get an introduction to Data Analytics

Course Tag

Data-Informed Decision-Making Big Data Data Analysis Data Visualization (DataViz)

Related Wiki Topic

Article Example
Data-driven instruction Swan, G., & Mazur, J. (2011). Examining data driven decision making via formative assessment: A confluence of technology, data interpretation heuristics and curricular policy. Gene, 1(1), 1.
Data-driven instruction Kennedy, B. L., & Datnow, A. (2011). Student Involvement and Data-Driven Decision Making Developing a New Typology. Youth & Society, 43(4), 1246–1271.
Data-driven instruction Moriarty, T. W. (2013). Data-driven decision making: Teachers’ use of data in the classroom (Ph.D.). University of San Diego, United States—California. Retrieved from http://search.proquest.com/docview/1432373944/
DoMS, Indian Institute of Science Business Analytics- With the era of gut based decision making past us, it is time to usher in the era of data driven decision making
Data based decision making Data based decision making or data driven decision making refers to educator’s ongoing process of collecting and analyzing different types of data, including demographic, student achievement test, satisfaction, process data to guide decisions towards improvement of educational process. DDDM becomes more important in education since federal and state test-based accountability policies. No Child Left Behind Act opens broader opportunities and incentives in using data by educational organizations by requiring schools and districts to analyze additional components of data, as well as pressing them to increase student test scores. Information makes schools accountable for year by year improvement various student groups. DDDM helps to recognize the problem and who is affected by the problem; therefore, DDDM can find a solution of the problem
Data-driven instruction Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use to improve instruction by looking at the information they have about their students. It takes place within the classroom, compared to data-driven decision making. Data-driven instruction works on two levels. One, it provides teachers the ability to be more responsive to students’ needs, and two, it allows students to be in charge of their own learning. Data-driven instruction can be understood through examination of its history, how it is used in the classroom, its attributes, and examples from teachers using this process.
EBay eBay uses a system that allows different departments in the company to check out data from their data mart into sandboxes for analysis. According to Goul, eBay has already experienced significant business successes through its data analytics. eBay employs 5,000 data analysts to enable data-driven decision making.
Data-driven The adjective data-driven means that progress in an activity is compelled by data, rather than by intuition or personal experience. It is often labeled as business jargon for what scientists call evidence-based decision making. This often refers to:
Data-informed decision-making Data-informed decision-making (DIDM) gives reference to the collection and analysis of data to guide decisions that improve success. DIDM is used in education communities (where data is used with the goal of helping students and improving curriculum) but is also applicable to (and thus also used in) other fields in which data is used to inform decisions. While data based decision making is a more common term, "data-informed" decision-making is a preferable term since decisions should not be based solely on quantitative data. Most educators have access to a data system for the purpose of analyzing student data. These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators’ data-informed decision-making. In Business, fostering and actively supporting DIDM in their firm and among their colleagues could be the main rôle of CIOs (Chief Information Officers) or CDOs (Chief Data Officers).
Jeanne Woodford Woodford has more than 30 years’ experience in corrections and law enforcement as an administrator, author, and public speaker. In 2004, she was appointed by Governor Arnold Schwarzenegger as the Undersecretary of the CDCR, where she oversaw an eight billion dollar budget, brought accountability to the department through data-driven decision-making, and advocated for rehabilitation programs and a sentencing commission for California.
Friday Night at the ER During the debrief, participants are guided to focus on collaboration, innovation and data-driven decision-making as key strategies necessary for successful system performance. These three strategies are examined and they are shown to be interdependent. Participants see that these three strategies produce excellent performance in the gameplay, and that the same applies to their real-world endeavors within organizations.
GALVmed For these diseases, GALVmed and partners aimed to identify suitable mechanisms for the development of control tools (vaccines, diagnostics and pharmaceuticals) and to facilitate their access and adoption. They also aimed to develop data-driven decision making tools and to communicate, network and support advocacy and global access strategy requirements of project delivery.
Responses to the West African Ebola virus epidemic At the Ebola Open Data Jam held on 18 October 2014, a group of volunteers initiated an open data repository, EbolaData.org, containing the wide variety of data sources related to the Ebola response that are publicly available. Data-driven decision making and planning, which is enabled by this repository, has much promise for effectively responding to the EVD epidemic. EbolaData.org is currently being curated by the Data Science Group of the Thomas J. Watson Research Center.
Office of Social Innovation and Civic Participation Presidential Innovation Fellow,: Scott Hartley focused on Evidence-based Policy, data driven decision making, and competitive grant programs, helping the Office of Social Innovation, OMB, and other agencies consider Silicon Valley methodologies such as "Lean Startup" philosophy to drive staged decision making, faster or more iterative feedback loops, and risk mitigation without stifling innovation. On leave from Mohr Davidow Ventures, and on assignment from USAID’s Development Innovation Ventures, he managed agency workshops related to the President’s Management Agenda.
Collaborative decision-making software Most decision-making and discussion surrounding business processes occurs outside organizational BI platforms, opening a gap between human insight and the business data itself. Business decisions should be made alongside business data to ensure steadfast, fact-based decision-making.
Collaborative decision-making software In the 1960s, scientists deliberately started examining the utilization of automated quantitative models to help with basic decision making and planning. Automated decision support systems have become more of real time scenarios with the advancement of minicomputers, timeshare working frameworks and distributed computing. The historical backdrop of the execution of such frameworks starts in the mid-1960s. In a technology field as assorted as DSS, chronicling history is neither slick nor direct. Diverse individuals see the field of decision Support Systems from different vantage focuses and report distinctive records of what happened and what was important. As technology emerged new automated decision support applications were created and worked upon. Scientists utilized multiple frameworks to create and comprehend these applications. Today one can arrange the historical backdrop of DSS into the five expansive DSS classes,including: communications-driven, data-driven, document driven, knowledge-driven and model-driven decision support systems. Model-driven spatial decision support system (SDSS) was developed in the late 1980s and by 1995 the SDSS idea had turned out to be recognized in the literature. Data driven spatial DSS are also quite regular. All in all, a data-driven DSS stresses access to and control of a time-series of internal organization information and sometimes external and current data. Executive Information Systems are cases of data driven DSS.The very first cases of these frameworks were called data-oriented DSS, analysis Information Systems and recovery. Communications-driven DSS utilize networks and communications technologies to facilitate decision-relevant collaboration and communication. In these frameworks, communications technologies are the overwhelming design segment. Devices utilized incorporate groupware, video conferencing and computer-based bulletin boards.
Decision-making Decision-making techniques can be separated into two broad categories: group decision-making techniques and individual decision-making techniques. Individual decision-making techniques can also often be applied by a group.
Martin C. Jischke The five-year strategic plan was adopted in November 2001. The plan called for data-driven decision making, focusing on collecting data on various performance benchmarks for comparison with peer institutions. Jischke also advocated steps to improve diversity, expand interdisciplinary research, add 300 new faculty positions, and engage government and business leaders to advance economic development. One of the most visible expressions of his vision is Discovery Park, a $100 million multidisciplinary research and entrepreneurial complex on Purdue's West Lafayette campus.
Decision-making Biases usually affect decision-making processes. Here is a list of commonly debated biases in judgment and decision-making:
Decision-making In psychology, decision-making is regarded as the cognitive process resulting in the selection of a belief or a course of action among several alternative possibilities. Every decision-making process produces a final choice; it may or may not prompt action. Decision-making is the process of identifying and choosing alternatives based on the values and preferences of the decision-maker.