The book is written mostly for non-data scientists and ex-programmers like me, who spend more of our time at the strategic level and have to understand at a 50,000 ft. level what and how data science can impact our business.
The main takeaway for those of us in management positions is that we need to be closely looking at HOW to measure our functions and then look at those metrics not for employee performance but forecasting and budgeting.
One thing of note was the following excerpt. In this instance, the author was stating how we as managers need to be careful with the data to ensure we don’t incorrectly apply one result set to another area. From the book, “More broadly, could we apply the learning in one vertical of the business—based on a specific, curated data set—to other verticals with different data sets within the same enterprise? Probably not, because the machine was trained and optimized on the first specific set of curated data. Once the data set drastically changes, the machine will start making mistakes again because this is a new, unfamiliar input. “
Having multiple support units under me, I get data from many sources. I have the support desk with the first call resolution numbers, number of calls per hour/day/week/month, and expected support times. I also have the reprographics unit with the number of pages printed, pages per hour, response times, and various other metrics. Finally, I have the project management team, who run ad-hoc projects and manage others. Understanding the performance of one does not allow for best practices of the other. Using data science I can expect to help guide staffing and project workload.
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