Thursday 9/6/18

Thursdays readings assigned to us were chapters 4 and 6 of Harvard Business Review. Chapter 4 focused on how to ask for data from data scientist the correct way, while chapter 6 focused on knowing and understanding the differences between data and metrics. I found both chapters interesting in their own ways, and I was able to make some life connections with chapter 6.

One thing I found really interesting in chapter 4 was right in the first two paragraphs where the author explains that many individuals of business's and organizations don't know how to  request new data or analytics from data scientist. This stuck with me because wanting to own my own business's in the future, I have never even thought about these aspects of business before. It was quite eye opening to me, and then the author further explains how to fix these individuals problems with information based on what questions to ask, what data is needed, and how to obtain the data gathered. I thought obtaining the data was especially important because I learned that before investing in recourses into new analysis it should ensure that the information gathered will be productive and meaningful. 

Another interesting thing I came across during these readings came from chapter 6, where a Youtube video is the prime example throughout the chapter to reveal that you can't pick your data, but you can pick your metrics.  In the Youtube example, dosomething.org released a video in hopes to get people to donate their old sports equipment to kids that didn't have any. After record breaking views, 1.5 million, you would think it was a whopping success except for the few 9 people that signed up for it with a total of 0 donations at the end. I found this particular  Youtube example to be very helpful to understand the difference between data and metrics because it was clear that their metrics were wrong somewhere. 

Another really interesting thing I took away from chapter 6 was the difference between vanity metrics and and meaningful metrics. Vanity metrics are the ones to be considered maybe good for certain things, but not what we want, while meaningful metrics are the ones that serve a purpose and do not waste recourses. The example used for vanity metrics was obviously the Youtube video explained before, but also simple examples like an organizations Twitter followers, Facebook fans, media impressions etc. Although sales may go up while these numbers go up, if their cant be any proof behind it then they are considered vanity. This really stuck with me as a learning lesson because I experience it every day in my own life while owning an online e-commerce store. Being in the e-commerce business myself I notice daily that Instagram followers, Facebook likes, and different social media platform impressions do not guarantee any sales by any means. Some products may see extreme exposure with no sales, while others will see half of it with hundreds of sales. 

3 questions

My first question is if 95% of the worlds data is unstructured and requires lots of money and recourses how do organizations and business's know what data is clean and where to find it?

Another question is in the case of vanity and meaningful metrics, if Twitter followers, Facebook likes, and media impressions work part of the time but not always, how would you decide how resourceful it is? 

My last question is in terms of the K.I.S.S rule for the author states "it may not be possible to avoid all expenses... by asking the right questions of your analyst you can ensure proper collaboration and get the information you need". In this particular paragraph, what are the "right questions" that need to be asked to decide if the model is too complicated or not?