Data-Driven Decision Making (DDDM) Specialization

Start Date: 11/22/2020

Course Type: Specialization Course

Course Link: https://www.coursera.org/specializations/data-driven-decision-making

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About Course

Gap assessmentBusiness Process MappingKPIsData CollectionData-Driven Decision MakingData visualization toolsData storytellingData analysis toolsStatistical process control (SPC)ISO 9001: 2015LeanDesign of Experiments (DOE)

Course Syllabus

Operational Context and Data
Data Analysis and Visualization
Applied Analytics and Data for Decision Making

Deep Learning Specialization on Coursera

Course Introduction

Make better organizational decisions. Improve the bottom line by viewing issues from a data perspective

Course Tag

Related Wiki Topic

Article Example
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 based decision making The U.S. Department of Education and the Institute of Education Sciences require to use data and DDDM in past decades to run educational organizations. Hard evidence and the use of data are emphasized to inform decisions. The data in educational organizations means more than analyzing test scores. Educational data movement is considered as a sociotechnical revolution. Educational data systems involve technologies and evidence to explain districts', schools', classrooms' tendencies. DDDM is used to explain complexity of education, support collaboration, creating new designs of teaching. Student performance is central in DDDM. NCLB provided boost in the collection and use of educational information.
Data based decision making The purpose of DDDM is to help educators, schools, districts, and states to use information they have to actionable knowledge to improve student outcomes. DDDM requires high-quality data and possibly technical assistance; otherwise, data can misinform and lead to unreliable inferences. Data management techniques can improve teaching and learning in schools. Test scores are used by many principals to identify “bubble kids”, students whose results are just below proficiency level in reading and mathematics.
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-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-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).
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/
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.
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.
Data based decision making Effective schools showing outstanding gains in academic measures report that the wide and wise use of data has a positive effect on student achievement and progress. DDDM is suggested to be a main tool to move educational organizations towards school improvement and educator effectiveness. Data can be used to measure growth over time, program evaluation along with identifying root causes of problems connected to education. Involving school teachers in data inquiry causes more collaborative work from staff. Data provides increasing communication and knowledge which has a positive effect on altering educator attitudes towards groups inside schools which are underperforming
Decision-making Biases usually affect decision-making processes. Here is a list of commonly debated biases in judgment and decision-making:
Data based decision making For example, in a rural area educators tried to understand why a particular subset of students were struggling academically. Data analysts collected students performance data, medical records, behavioral data, attendance, and other data less qualitative information. After not finding direct correlation between collected data and student outcomes they decided to include transportation data into the research. As result, educators found that students who had longer way from houses to the school were struggling the most. According to the finding administrators modified transportation arrangements to make the way shorter for students as well as installing Internet access in buses so students could concentrate on doing homework. DDDM in this particular case helped to improve student results.
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.
Decision-making In the 1980s, psychologist Leon Mann and colleagues developed a decision-making process called GOFER, which they taught to adolescents, as summarized in the book "Teaching Decision Making To Adolescents". The process was based on extensive earlier research conducted with psychologist Irving Janis. GOFER is an acronym for five decision-making steps:
Decision-making During their adolescent years, teens are known for their high-risk behaviors and rash decisions. Recent research has shown that there are differences in cognitive processes between adolescents and adults during decision-making. Researchers have concluded that differences in decision-making are not due to a lack of logic or reasoning, but more due to the immaturity of psychosocial capacities that influence decision-making. Examples of their undeveloped capacities which influence decision-making would be impulse control, emotion regulation, delayed gratification and resistance to peer pressure. In the past, researchers have thought that adolescent behavior was simply due to incompetency regarding decision-making. Currently, researchers have concluded that adults and adolescents are both competent decision-makers, not just adults. However, adolescents' competent decision-making skills decrease when psychosocial capacities become present.
Decision-making Other studies suggest that these national or cross-cultural differences in decision-making exist across entire societies. For example, Maris Martinsons has found that American, Japanese and Chinese business leaders each exhibit a distinctive national style of decision-making.
Decision-making It is important to differentiate between problem analysis and decision-making. Traditionally, it is argued that problem analysis must be done first, so that the information gathered in that process may be used towards decision-making.