Explaining the World of Data Through Memes

Introduction

I love memes! Everyone loves memes, right? A great meme can not only make us laugh, but it can articulate a deeper truth we sometimes neglect to acknowledge. So I will now attempt to reveal some hidden truths about the world of data analytics through some of my favorite memes.

Data Engineers

I can vividly remember a client meeting where a data engineer was introducing himself and describing his role to the group. After a short pause, one of the clients replied, in a deadpan tone, “So you move data from point A to point B.” (face-in-palm emoji). While directionally correct, a data engineer does so much more!

If you want to maximize the value of data scientists and analysts, then you need skilled data engineers supporting them. Simply put, data engineers transform raw data into more useful forms. More importantly, they automate the process of this transformation, creating a repeatable and consistent flow of high quality data ready to be further refined.

Data Scientist

Perhaps no other analytics role is as overhyped or misunderstood as a data scientist. Isn’t a data scientist just a fancy way of saying data analyst? Nope. Data scientists are a blend of several talents; mathematics, statistics, computer science, and data modeling. And they also have another key ingredient, business acumen. A data scientist will only be as effective as their understanding of the problem they are trying to solve and how the solution will actually be applied in the business.

Day-to-Day Realities of Working in Data Science

Data science encompasses the tools and methodologies to uncover hidden answers to important business problems. But many non-technical folks don’t have an appreciation for the long and winding (sometimes frustrating!) path data scientists and analysts must travel to get there. There’s a long-standing truth that 80% of the time doing data analytics is spent on cleaning and assembling the data, and only 20% spent on actually analyzing the data. All too often this is the case. It is important for any business serious about analytics to make the right investments in people, tools, and processes to reduce the prep time and maximize the time exploring and gleaning insights.

Practice Makes Perfect

As a data analytics consultant, I’ve encountered many types of client challenges when it comes to getting tangible results from data. There is one particular situation that makes me cringe the most. The conversation goes something like this:

Me: Leveraging the right data & algorithms, we can help you detect anomalies in your process that will reduce costs and increase revenue.
Client: We’ve already tried that.
Me: What did you try and what was the outcome?
Client: Our in-house data team built some sort of model. It took them 6 months to build and it was 50/50 at identifying patterns we could act on. I could have saved myself the time and money and just flipped a coin.
Me: What did your team do next to build on that learning?
Client: We learned this can’t be done. We moved on to other projects since this one failed.
Me: (makes faint groaning noise as part of my soul vanishes)

As the name implies data science is, well, a science. And like any science the process is fundamental to success. It starts with a well thought out hypothesis and can require several iterations of trial and error before honing in on a method that yields results. Giving up too quickly on a particular challenge can demotivate your team and deny your business valuable insights.

And I’ll leave you with this one. It doesn’t shed any light on data science, but it just seems right:

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