2 and a half years ago, I thought I knew everything on what it meant to be a data in tech and wrote a post “What is a Data Analyst in Tech (Part 1)” Looking at it now, there are many of naivete, but I think a lot of these still hold true. Here is the post from 2015. I hope peers from then can relate to how much we’ve changed, and I hope those of you looking to change into tech can learn something new!
“So what exactly do you do as a data analyst?”
I get this question all the time.
If you are a consultant, banker, or new grad that wants to come into the analytics field, listen closely because I am writing this for you.
First of all, I will be the first to admit that “big data” is a buzzword and heavily overused. In other words:
Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…
– Dan Ariely (Facebook)
OK, OK, its not that bad. For example, big data is what allows Google to do the self driving car, live translations, and all the other Googley things (more like every single Google thing). It is what brings us Watson, Siri, Cortana, and other AIs that will take over the world one day (read: Terminator, Matrix, every cool future movie)
However, if you’re like me and only know Excel coming in, you might be discouraged to enter the data field at first – you aren’t making the next Watson anytime soon. That being said, I want to let you know that being a data analyst isn’t about big data. Its about adaptation, grit, and constantly learning.
A great way to understand the data landscape is to define the job titles that have data in them. What do they mean?!? Lets investigate:
- Data Scientist: Does heavy machine learning/stats stuff like recommendation engines, clustering, etc. These guys are the brains of “big data” you see in the news.
- Also known as: Data Mining
- Need: Stats MS/PhD in Stats or CS
- Data Engineer: Deals with the server side of data, making sure data transfer systems are reliable and can handle high volumes of data. These guys allow “big data” to happen.
- Also known as: Date Warehousing, Data Infrastructure
- Need: CS degree (BS/MS/PhD all good)
- Data Analyst (me): Does analytics on how either a product or the business is performing. In charge of day-to-day metrics as well as driving projects and recommendations.
- Also known as: Business Analyst, Business Intelligence, ____ Analyst
- Need: heart and brain, maybe bachelors degree (for Entry Level)
- Career path is unclear. Options are to work towards above roles, a better analyst, or up into management.
A data analyst role sometimes looks a lot like consulting – this is why I sometimes describe my role as an ‘internal consultant’.
There’s a lot of posts about being a data analyst in places online, such as in Quora’s “What should I study or learn if I want to be a data analyst?“. To supplement others’ views on data analytics, I want to pitch in my experiences and hopefully an easy way to understand what I do.
DATA ANALYST PART 1: THE DATA CYCLE/PIPELINE
To fully explain what a data analyst does in years 0-2 (and on), I will first show you “Blu’s Data Pipeline”. This is the framework I think of day to day, and whenever I get the dreaded “What the hell do you do” question.
There are no numbers in the pipeline, because there is no beginning or end.
You could work an entire quarter within the ANALYZE and PRESENT tasks, creating reports to help your team better understand a certain area of the business or product. You could spend a quarter focusing on the CLEAN and TRACK, working with data already in your warehouse to make dashboards that expose insights and visualize any anomalies in your work.
Personally I’ve never done a full cycle project. Steph, one of my first teammates, could probably tell you about a full product analytics cycle with her work on our recently redesigned Help Center. But since this is my blog, I’ll go ahead and tell you instead.
Lets see what I mean by going through a full analytics cycle:
- First step. Figure out how people were using the old Help Center. What pages were viewed the most? Did users click around? What kind of users clicked what kind of articles? (Analyze)
- First gotta use SQL to get data out of our warehouse.
- Look at competitors’ Help Centers to get inspiration on various designs and flows. (Research)
- Use data/visuals to make a case for making improvements to the Help Center (Present)
- Work with engineering, product, design, marketing, support to discuss direction of project (Collaborate)
- Determine what kind of data we want from users visiting help center (browser, OS used, location, etc.) (Design Logging)
- Use internal systems to shove all the data into our data warehouse in a way that is easy to understand and query. (Clean)
- Build dashboards so you and your project-mates can track who’s viewing, what their clicking, and how they are navigating the new Help Center. (Track)
- Work with team to constantly A/B test UI, content, and flow improvements, and do additional analysis as necessary. (Do it all again)
- Enjoy the impact you made on a beautiful redesign of the help center, going from
Since I’m not Steph, I cannot give you the full ins and outs of how amazing it is to see and work on a project from start to finish, and have something gorgeous to show for it.
However, I can say that whether you decide you like to focus on breadth or depth, understanding and being able to plug into any part of this cycle is what makes you one of the most versatile members of the org.
Think about a tech company and all the various web properties it owns. Think about the desktop version of the product, the web version of the product, the iOS application, the Android application and just how many different features and flows that need nuanced recommendations.
On the operations side, there’s marketing analytics, figuring out how to test ads from various mediums, and their effectiveness. There’s work to be done in learning about how we can help our existing customers become more engaged (retention).
There’s analysis to be made to figure out the financial impact of various initiatives, and what the baselines should even be (what is good? what is great? what is “somebody going to get fired” land?)
There is so much analysis that can be done, and this is why the data field is so hot right now.
Thanks for reading What is a Data Analyst in Tech? (Part 1)
The next few installations will include something along the lines of:
- What I enjoy most about my role as a Data Analyst
- Why Data Analyst job descriptions are obsessed with SQL (and why they shouldn’t be)
- Big list of the buzzwords and terminology you need to sound smart.
- & technologies and software that is being used in the analysis world
- Career Paths for Data Analyst roles (Opinion/Discussion)
Please stay tuned for more, and hope you have a great day!
*I thought I had lost the post in my last WordPress migration – however I was able to find it thanks to the Web Archive at http://web.archive.org/web/20160221010248/http://brianylu.com/2015/01/23/what-is-a-data-analyst-in-tech-part-1/. Thanks Internet!