|Analytical Consulting
Optimize Data Analytics’ Advanced Analytics Consultants rely on the scientific method, experimental design, mathematical modeling, simulation and optimization to answer marketing questions (Marketing Science) and solve a range of operational and managerial problems (Operations Research and Management Science). Our consultants work closely with clients through each of the following stages:
- Defining Objectives. This is the most critical stage. Much time and effort is devoted to precisely defining the objectives, the exact nature of the problem or opportunity, and the success criteria. Alignment m
- Defining Objectives. This is the most critical stage. Much time and effort is devoted to precisely defining the objectives, the exact nature of the problem or opportunity, and the success criteria. Alignment meetings and interviews involving senior decision-makers are crucial. Without senior management support, analytical projects go nowhere. Often exploratory research is used as an additional investigative tool to precisely define the issues and possible solutions. Accurate definition at the outset is essential to ultimate success.
- Developing Hypotheses. What are the likely causes, key variables, and possible solutions? Typically, Optimize Data Analytics researchers interview client executives, visit with store employees, talk to customers, observe manufacturing processes, ride the delivery trucks, etc., to learn and explore. The challenge at this stage is to learn from those on the firing line, but to question everything and to see everything through fresh, unbiased eyes.
- Defining Data Sources. What objective data is available that might help explain or illuminate? What other data might be needed? Will controlled experiments be necessary? Can secondary data play a role? What is the best way to collect and organize this data to assemble the analytical database?
- Exploring the Data. Often times the data itself can lead consultants to new understanding and new hypotheses or help refine hypotheses. So, typically, a series of exploratory analyses of the data are undertaken to let the data tell its story independent of human biases. This helps provide the insight and understanding needed to build sophisticated simulation models.
- Building Mathematical Models. The goal of model-building is to simulate or represent the real-world system of interest, so that scientific experiments can be conducted. With simulation models hundreds or even thousands of possible solutions can be explored, to identify optimal solutions under differing conditions or constraints.
- Finding Optimal Solutions. Out of the thousands of possible solutions, Optimize Data Analytics identifies a small set of optimal or near-optimal solutions to recommend to the client.
- Creating the DecisionSimulator™. One of Optimize Data Analytics’s deliverables is the DecisionSimulator™. This simulator allows clients to explore “what if” scenarios on their desktop computers with a few clicks of a mouse, as conditions or constraints change over time. This extends the value and the life of the optimization work.
- eetings and interviews involving senior decision-makers are crucial. Without senior management support, analytical projects go nowhere. Often exploratory research is used as an additional investigative tool to precisely define the issues and possible solutions. Accurate definition at the outset is essential to ultimate success.
- Developing Hypotheses. What are the likely causes, key variables, and possible solutions? Typically, Optimize Data Analytics researchers interview client executives, visit with store employees, talk to customers, observe manufacturing processes, ride the delivery trucks, etc., to learn and explore. The challenge at this stage is to learn from those on the firing line, but to question everything and to see everything through fresh, unbiased eyes.
- Defining Data Sources. What objective data is available that might help explain or illuminate? What other data might be needed? Will controlled experiments be necessary? Can secondary data play a role? What is the best way to collect and organize this data to assemble the analytical database?
- Exploring the Data. Often times the data itself can lead consultants to new understanding and new hypotheses or help refine hypotheses. So, typically, a series of exploratory analyses of the data are undertaken to let the data tell its story independent of human biases. This helps provide the insight and understanding needed to build sophisticated simulation models.
- Building Mathematical Models. The goal of model-building is to simulate or represent the real-world system of interest, so that scientific experiments can be conducted. With simulation models hundreds or even thousands of possible solutions can be explored, to identify optimal solutions under differing conditions or constraints.
- Finding Optimal Solutions. Out of the thousands of possible solutions, Optimize Data Analytics identifies a small set of optimal or near-optimal solutions to recommend to the client.