Business Impact with Data Science
Data Science/Analytics/Machine Learning should not be done in a vacuum or for the sake of doing ML/AI. Instead, it should aim to be a data product/deliverable that helps bring value back to the business. To do so, ahead of development, it helps to understand the following about the business and the stakeholder ask:
- How does the company make money? What is its core mission?
- Why is this initiative important now (above all other priorities)?
- Suppose your client/company spent $200K on data + salary. How can the data product you create business impact? Generate (or save) $300K for the business? $400K+?
- How can the business use the data product you create to make better business decisions and bring value to the business?
- How will the deliverable be used by the stakeholder?
- What will it take to get it into production? What's the rollout strategy to customers?
- What's the deadline and associated checkpoints?
- Recommendation: Start with descriptive analyses; only when you understand what happened in the past, should you move onto predictive analyses to forecast what may happen in the future.
Keywords: Data products, business impact and value
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