![]() ![]() Identify the business levers: The business levers represent the input to the system which can influence the business outcome.Thus, before getting ahead, most important leading and lagging KPIs must be defined for hypotheses validation. Each of the action will be measured by leading KPIs and the final outcome is measured by lagging KPIs. Defining the KPIs: The hypotheses are related to decisions and actions which can be mapped to the final outcome.One can use value-complexity mapping to select the top 3 hypotheses to work on. Laying down the hypotheses: Once the problem is understood well, one should go about laying down one or more hypotheses related to solution of the problem.He/she should be well knowledgeable about the questioning techniques such as 5-whys, Socrates method, etc which helps arrive at the actual problem. He/she could use analytical approaches such as breaking down problems into sub-problems to get a holistic picture of the problem. He/she could be part of design thinking workshop to understand the real problems. Understand the business problem: First and foremost, data science architect should work with product managers / business analysts to understand the business problem.Solving a business problem using data science or machine learning based solution can be done using a 4-step process: How do you go about architecting a data science or machine learning solution for any business problem? ![]() Talk about a cloud-based platform that could be used for training machine learning models by the data science team? What will be your governance strategy for machine learning-based solutions? How would you go about deploying a machine learning model in the cloud and serve predictions through APIs? ![]()
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