(with a nod to @KrispyKreme)
I’d like to wish everyone reading this post a blessed, peace-filled, and happy 2017! I would also like to thank you for reading this post and any other posts you may have read.
Some Blog Post Stats
I published 87 blog posts this year at SqlBlog.com. I published most in December (12) and least in October (2).
These posts attracted more reads than the others (ordered by most reads in descending order):
- Installing SQL Server 2016 Developer Edition, One Example
- SQL Server 2016 Developer Edition is Free
- Announcing Biml Academy!
- PASS Board Elections–Voting is Open!
- SQL Server Developer Edition is FREE!
- Biml Academy 2 Webinar Recordings are Available!
- Microsoft is Listening
- A Couple-Three Thoughts and Questions About Swag at Community Events
- A New Version of SSDT is Available
- BI’s Not Dead
Is This Accurate?
But… Some of those posts have been around a lot longer than others. The oldest post in this list is 325 days old (at the time of this writing). The newest post is 85 days old, the average “age” of these posts is 210 and the median “age” is 215 days.
How does a Data Philosopher account for this? I computed the age of each post and then divided the read count by that number. The results now look like this:
- The Recordings for SSIS Academy: Using the SSIS Catalog are Available
- SQL Server vNext CTP 1.1 is Available!
- SQL Server Management Studio (SSMS) v16.5.1 Now Available
- Learn More About the SSIS Catalog
- Three Free Webinars About Using the SSIS Catalog
- An Interview With Me
- DLM (Database Lifecycle Management)
- SSIS Catalog Browser Update
- Broken References in the SSIS Catalog
- New Versions of SSMS and SSDT Available
Is This Accurate?
But… This second list is skewed towards the newer posts due to the lower denominator. The newest post is 2 days old (and it’s #1) while oldest post in this list was published 43 days ago (at the time of this writing). The median “age” of the posts in this list is 16 days and the average “age” is 18.
I suspect both lists are valid because readership declines after the post scrolls off the front page at SqlBlog.com (which displays the latest 20 posts). I write “both lists are valid” because these results (like all resu
lts in data analytics) must be considered in context. Context is defined by answering the question, “What is the problem we are trying to solve?” There are other ways to analyze this data – lots of other questions we can ask of it. Data science is science and science (well, good science, anyway) involves experimentation.
My theory: The first list contains the posts that experience some “longevity in appeal” (or SEO). The second list is “Hot Now.” Or not. I’d love to hear your thoughts.
Happy New Year!