Becoming a Data Quality Expert Data science is an exploding field with tremendous demand. Having high quality data is an absolute must for any business today and data informs every decision a business must make. But what if you have poor quality data? What if your company acquired another company and the data structure does not match? What if you have large gaps in the data you have vs. what you need?Imagine yourself as an IT project/program manager who has run many engagements for the business. You have great PM skills and you run your agenda with the precision of a Swiss watch. But you now have to run Data Quality for your organization. Can you just program manage this and be fine? What will be different about this than any other IT project?Wake-up call: a WHOLE LOT! You must acquire a lot of new skills and you must become a data expert as quickly as possible. I want to share with you my journey and experience. I have had to go from deeply technical in some IT areas, to project/program managing general IT projects, to gaining specialized skills in data quality. I will share with you my assessment, gap analysis and mitigation strategy that transformed me into a data quality expert.
Project Risk Management: On-Demand Have you been surprised by unplanned events during your projects? Are you and your project team frequently fighting fires? Well, you are not alone. Uncertainty exists in any project environment. While it's impossible to predict project outcomes with 100% certainty, you can influence the outcome, avoid potential risks, and be ready to respond to challenges that arise. In this course, you'll gain the proper knowledge needed to identify, assess, plan for, and monitor risk in your projects. You'll learn how to set up and implement risk management processes, helping you to minimize uncertainty and achieve more consistent, predictable outcomes as a result. What You Will Learn You'll learn how to: Demonstrate to others how the risk management processes in A Guide to the Project Management Body of Knowledge (PMBOK® Guide) apply to your project's environment, especially for high-risk projects Adapt these processes for a particular high-risk project team's operating principles Explain the importance of using risk management best practices at single and enterprise project levels Lead an initiative to implement risk management best practices in your project environment Foundation Concepts Risk-related definitions The risk management process High-risk projects and project failures Classical failures in implementing risk management Plan Risk Management Project risk management and governance Risk management planning for high-risk projects High-risk variations on a risk management plan Identify Risk Adapting the risk identification process for high-risk projects Recognizing risks spontaneously Confirming and structuring risk events for treatment Wrapping up risk identification for high-risk projects Perform Qualitative Risk Analysis Adapting qualitative risk analysis for high-risk projects Accelerating risk analysis Clearing risk action Wrapping up qualitative risk analysis for the next level Perform Quantitative Risk Analysis Adapting quantitative risk analysis for high-risk projects Ensuring effective risk analyses with data quality assessments Building a foundation for quantitative risk analysis Using discrete quantitative tools Using continuous quantitative tools Wrapping up quantitative risk analysis for high-risk projects Plan Risk Responses Adapting risk response planning for high-risk projects Optimizing active risk response strategies Leveraging contingencies for high project performance Wrapping up risk response planning for high-risk projects Implement Risk Responses Implementing Risk Responses Process Executing Risk Response Plans Tools and Techniques Best Practices Continuous Risk Management Monitor Risks Adapting risk monitoring for high-risk projects Optimizing risk plan maintenance Weaving risk reassessment into the project's progress Maintaining a continuous 'vigil' in high-risk project environments
Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning
Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning
Thinking about learning more understanding the role of data within an organisation? As data becomes an important currency in the world and an enabler for the future, it is imperative that all organisations have a firm understanding of the data available to them and the power it can hold. The BCS Foundation Award in Understanding Data in your Organisation teaches the the terminology, principles, concepts and approaches used within data management, and the overall value of data to an organisation.
Explore Interpretative Phenomenological Analysis (IPA) theory and research methodologies in this comprehensive course. Gain insights into IPA's theoretical foundations, learn to plan and conduct IPA research studies, and delve into advanced designs and innovative approaches. Develop practical skills in data collection, analysis, and writing, guided by expert instruction. Perfect for students and researchers seeking to deepen their understanding of qualitative research and enhance their proficiency in IPA methodology.
This PMI-RMP Certification Training will help you master the processes of risk management and the structured and objective approach to addressing uncertainty in projects. You will learn how to conduct risk planning, identification and analysis, and control both known and unknown risks in projects.
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