Data Gourmetization and LinkedIners 👨‍🍳

Every new year I see companies trying to invent new processes, strategies and technologies, over and over again, gourmetizing data roles, to try to minimize the efforts of working with their messy data stored with no actual purpose through all kinds of monstruous apps just to make it profitable for any person that will buy the idea that this might generate value to their business - most of the times, with no success. Come on, I’m sure you might have heard: “Data is the new oil”.

But why? And what does it have to do with the gourmetization of data and data roles?

Well, first, let me tell you something... Having a PowerBI with glowing pizza charts or having your PostgreSQL view running in less than 10s is not enough to make your data useful. Now, let me explain why.

Ok, but what is data gourmetization?

Yes, I came up with this word, but to help you understand my line of thought, just search the LinkedIn app in your phone (or browser), execute it and start scrolling.

Voilá, if you're a data analyst, data scientist, data engineer or have any other data-janitor role, within the first minute navigating in the platform you will definitely see at least one post talking about neon dashboard designs or why being a data analyst is so important for a company (and sometimes very trustful popups of courses on how to become a data analyst in 3 months which I would highly recommend trying - I'm kidding, don't do that).

Why new strategies doesn't help?

Well, that part is not entirely true. If you know what you are doing, and most importantly, why you are doing, then it will surely help (a lot).

But what are the LinkedIners forgetting? Simple. To sit down with their clients (or internal stakeholders) and evaluate what their business need to look at. No confusion matrix, no random forest, no PowerBI glowing dashboard. For more than 70% of the cases I've worked throughout my career, only an e-mail with some KPIs or an excel file that I extracted with a simple select query solved their issue.

Now... let's go more technical.

What actually helps (technically)

The vast majority of the data issues we have today regarding storage, optimization and architecture can be easily solved with the most basic concepts you can remember of - the ones you (probably) learned early in your graduation and that you are most likely not using in your day-to-day work. Normalization, indexing, entity models, scalable ETL processes, and other basic techniques are key to make your life easier without depending on expensive 3rd party platforms that you will pay tons of dollars to do for you.

Why companies are gourmetizing data, then?

The reasons behind that might be simpler than you think.

• Attracting Talent: Calling it "Data Wrangling 2.0" instead of "Basic ETL" sounds way more exciting and is more likely to attract the bright-eyed with anime profile pictures next-gen analysts.

• Securing Funding: Investors love innovation. They’re more likely to fund a "next-gen data synthesis platform" than a "robust data cleaning tool," even if they don't understand that they mean the same thing.

• Creating Hype: Let’s be honest, hype sells. New terms and concepts generate buzz and keep the tech industry shining and forward-looking.

• Making money (a lot): with the right marketing, big hype and investors that exerce big influence in the tech market, anyone can become a new millionaire (or billionare). Just look at the Theranos case, where they raised over $700 million based on promises that turned out to be hollow.

So...?

The truth is... working with data is not that shiny. Every data you show, can and will be negative to the company's profitability if you don't do your job right. So, NO! working with data is not gourmet and you cannot become an analyst in 3 months - and I'm not saying it because of the companies, but that is risky for you as an employee since you might respond for those mistakes as an individual.

All in all, be careful with PowerBI courses, my friend.

Cheers.