I decided to buy a solid state disk (SSD) for my home computer, and want the best deal possible. I require three things: low price, fast performance, and good reliability. Which drive has the best mix of the three? Let’s find out using my favorite tool: data!
Most of us do not have a collection of SSDs and a test rig. Luckily, there are great websites that do have these things and publish their results. I used two of the most popular: AnandTech.com and TomsHardware.com. A quick check shows that there are 12 SSDs that were recently reviewed by both sites.
I chose a reputable website (Newegg.com) and looked up prices. I don’t use mail-in rebate prices because rebates are notoriously hard to redeem. This part was very, very easy.
Reliability data is also available online, sort of. I like to crowd-source my reliability data from feedback consumers leave on large sites. We’ll use Amazon.com and Newegg.com ‘star’ ratings. Let’s consider both the scores and the number of reviews posted. The reason to look at counts is because a large enough number of reviews implies the wisdom of the crowds is accurate (statistically significant is the technical term).
To start with, I put everything into an Excel file. Then I uploaded the data into a common visualization tool, Tableau. To start, let’s look at price versus performance.
The drives we are interested in are in the upper-left corner; they have the best performance and are also cheap. 6 SSDs qualify:
The color represents the ratings given to each SSD by customers. One of them (in red) has bad ratings: the OCZ Vertex 3. We therefore eliminate it from our list. If we look a little closer at the data, 2 SSDs are pricier and slower than others: the Intel SSD 520 and Corsair Performance Series Pro. We therefore eliminate them as well, leaving us with 3 drives: the Kingston HyperX, Samsung SSD 830, and Plextor M3. They are all fast, and all under $280. Time to look at some different data.
Here we see performance scores vs. customer ratings for 3 different performance tests. The SSDs trend (roughly) from bottom-left to upper-right in each graph, indicating that review scores get better as the performance improves. That makes sense; we expect to see faster drives create happier customers. This time the interesting SSDs are in the upper-right corner; they have good customer reviews and good performance.
Three SSDs fit this criteria:
We have already eliminated the Intel drive because it is pricey, which leaves us with 2 that pass our price filter and our ratings filter. I would be confident getting either drive. Success!Permalink
It has been 8 weeks since I started my new job at the University of Washington, and 9 weeks since I left Microsoft. I have been increasingly productive, content, and lighter that entire time. That makes me ask: Why? What is so different? Let’s see, using my favorite weapon of choice: data!
“Data is mightier than the sword”
Measuring personal satisfaction accurately is impossibly hard. So let’s focus on how time is spent, and consider the enjoyment factor for different tasks. The assumption is that overall satisfaction is the sum of satisfaction over various hours. The more enjoyable each hour, the happier we will be.
We each enjoy different activities. Some people enjoy meetings and PowerPoint, and some people prefer solitude and writing code. In an ideal situation our job is full of tasks we enjoy doing, and mostly avoids things we don’t like.
Being a developer, my job is composed of many different activities: commuting, meetings, code reviews, writing code, trainings, research. I gave each my common tasks an ‘enjoyment’ rating, from 0 to 13, indicating how much fun I found in the task. Meetings aren’t much fun. Writing code is enjoyable. Time spent with family and friends is fantastic. The result was an Excel file.
I find data visualization is a great aid, so I put together a Tableau dashboard. First, let’s look at how much time I spend doing various tasks at my new job, compared to the old one.
The color-coding indicates how much I enjoy the task. Ask you can see, two big shifts happened. The first is a drop in tasks I don’t enjoy: meetings and email. The second is a spike in tasks I do enjoy. The other, more subtle change was a reduction of tasks I am indifferent to, such as commuting
However, this analysis is about comparison, not composition. To see the before-and-after effect, let’s try a different chart.
The height of the line is the cumulative ‘score’, which consists of [Time Spent] * [Enjoyment]. The thickness of the line is the time spent. The effect is quite dramatic. Now I have a better answer of why I feel better in my new job. I would encourage my fellow developers and data professionals to do their own analysis in similar situations. The results can be illuminating.Permalink