Turning Big Data Analytics into Intelligent Government
Knowing the future requirements of citizens is a very complex matter. Organizations have multiple analytical approaches to making predictions and gaining insight into the future. This course introduces the big data analytics process and its methods for supporting the decision-making and planning processes for projects, policy development and permanent improvements in organizational processes. The course focuses on the analytics process and on the creation of a roadmap for developing intelligent government using different types of data and various analytics tools. In the course, participants will discuss value creation, problems to be solved, big data analytics architectures, required data and algorithms according to different types of problems requiring solutions and how to embed and maintain an analytics architecture in federal, provincial and municipal organizations.
This course is designed to connect theory and practice through the review of applications on strategic, managerial and operational levels such as the supplier management relationship, accounts payable, asset maintenance optimization, fraud detection, healthcare planning, fundraising, human resources, transportation, tax collection and more. The course uses several methods that can be found in multiple analytics platforms. Examples will be illustrated using mainly Excel and R. Participants will be introduced to the Hadoop ecosystem and Apache Spark.
Overall, participants will gain a better understanding of the strategic importance of data—big and regular—in developing intelligence, creating and maintaining knowledge management systems, policy development and learning how to efficiently use analytic techniques in their daily work.
Defining a problem and reviewing the project scope and desired results for organizations, socio-economic sectors and society as a whole
Using analytics capabilities applicable to public sector data
Managing MS-Excel and R tools to support predictive analytics in government in areas such as service, marketing, risk, operations and human resource management
Interpreting model outcomes with different contexts and problems
Reviewing results and stress testing the created models
Troubleshooting the analytics process: limitations and possible avenues
- Public sector professionals working in policy development, data analysis, problem-solving, public policy design and planning
Eduardo Rodriguez MBA, MSc, PhD, has extensive experience working internationally with private and public organizations in the fields of analytics, knowledge and risk management. He has developed academic programs and organizational solutions in analytics to address problems in various types of organizations and economic sectors. He is principal at IQAnalytics Inc., a research centre and consulting firm in Ottawa. He is also an analytics adjunct professor at the Telfer School of Management at the University of Ottawa, a corporate faculty member of the MSc in analytics program at Harrisburg University of Science and Technology in Pennsylvania and a senior associate faculty at the Center for Dynamic Leadership Models in Global Business at The Leadership Alliance Inc. in Toronto. Eduardo has been a visiting scholar at Chongqing University, China, and a strategic risk instructor at the SAS Institute, Cary, North Carolina. He is chair of the Analytics Think Tank in Ottawa and creator/leader of the collaborative program for development of analytics at the Latin American Council of Management Schools (CLADEA).
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