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Pink's LinkedIn: https://www.linkedin.com/in/pink-xu/

In this episode, Scott interviewed Pink Xu, Change Manager of Business Impact of Data Products at Vista.

Before we jump in, there are a few specific examples in this to Vista but I think it is incredibly relevant when looking at measuring the impact of your data work. As Pink says, set the objective/goal for the data product and then measure if it met that objective/goal. It isn't the impact framework's job to specifically measure if the objective of the data product is valuable, only to provide an objective way to measure how well did the data product meet its goal.

Some key takeaways/thoughts from Pink's point of view:

1. Look to standardize the way you measure impact for data products. Much like data observability/SLA metrics, a centralized team shouldn't be the ones focused on measuring or defining the target impact of a data product, only providing the way to measure it.
2. Again like data observability, an impact measurement framework/methodology means people can trust exactly how impact was measured without having to dig into every measurement decision. It's not like grading your own essay, which is a problem with a not impartial measurement.
3. Impact measurement can only go so far. It shouldn't be the only consideration in valuing a data product but without a fair, impartial framework, measuring the value of work becomes all the more difficult.
4. A data product "enables business impact", it cannot create the impact itself if no one uses it. Think about who gets "credit" for the impact - is it the data product creator or the team that acted on the insights from the data product? Look to reward/credit...