Thread by @GiordMarco96

Adding value with #DataScience is hard for many #SEO Specialists.

I want to show you what I am learning and how to prove the value of these new skills for those interested. 🧵

Data Science and related disciplines are not for show. They make your life easier and allow you to save time and financial resources.

You don't need to pay a crawler if you just want to crawl a website for broken links.

The trade-off is some months of your life.

Many people (looking at you ""SEO veterans"") think that this is the usual cyclical trend.

It's not, entire companies are changing their IT infrastructure just to be more data-centric. We are still far from having companies with good data engineering but we are going there.

It can be argued that people already in the business don't need to code and that's true. Industries change and the needs of search engines are not different either.

More data in the organization mean there is a need for some people that can uncover their meaning.

No, you cannot delegate everything to a Data Analyst/Scientist because it's super hard to get this much domain knowledge in a single blow.

An SEO can already boast domain knowledge and be the perfect candidate for these tasks.

Some jobs are becoming more and more hybrid. If MS Office was the bare minimum, now they ask you for more and more requirements.

A fresh graduate is already struggling, imagine in the era of data how this was amplified.

New SEOs or people just moving to this field should understand that it is a large and heterogeneous field.

There is no proper and superior way to solve a problem, you can approach it from different angles.

Data are one more angle to consider now.

A single script (even if simple) can save a lot of time in organizations. Automation is a process and you have to start gradually.

I build small tools to save time and never do it again manually. It paid off in the long run, needless to say.

It's all about efficiency.

I'm spending some time every day learning valuable skills. I can just stop and accept what I've learned, I am still better off in terms of benefits.

This is the practical mindset I encourage the shyest to adopt. Start with quick wins and build on them.

Some SEOs have always been using coding to simplify tasks. One example is one book called SEO Warrior (2009) that showed some Perl scripts to crawl websites.

Nowadays you need few lines of code and fewer headaches.

The same applies to Javascript, even though I am not its biggest fan. This is something that has been around for quite some time.

Python and AI had a sudden boom in popularity years ago and that had repercussions on SEO.

Excel and Google Sheets are limited tools. They're extremely powerful in the right hands but well, how many good Excel users are out there?

The learning curve tends to get steeper, at least Python becomes easier later on.

SQL is the real data skill to master. Storing SEO data in databases is highly effective and safer than using CSVs on your laptop.

Here is an example of wrong Excel usage:

bbc.com/news/technolog…

SEO data are already inaccurate and super vague. The last thing you want is to lose them.

In this case, you need some help from other people but knowing the basics is great for communication.

The main benefits of data skills are:

- Saving time in the medium/long term
- Improve understanding of data systems
- Increased awareness of SEO tools problems
- Save financial resources by building your solutions
- Create your workflow

As you can see, the focus is on saving and independence from third-party solutions.

The only sacrifice you have to make is time in the initial steps. One year is more than enough, some people claim 6 months are fine. There is no clear answer.

I think that one year of your life is a reasonable trade-off. It's not 1 full year, I mean some hours after work or even one per day, every day.

Python and SQL already solve most of your problems so there is no need to exaggerate.

Many people are clueless as to why the data disciplines have boomed.

Sure, there is some misinformation behind the AI hype but there are good reasons for that.

The most recent NLP models are crazy and can build powerful solutions. Just think of the semantic clustering tools.

Years ago it wasn't possible to achieve today's results. 10 years ago we weren't even close to what we could achieve now.

You can run powerful models on your laptop, this wasn't an option years ago. Most of us didn't even know what they were.

SEO is not the only industry affected by the rise of new technologies. Digital Marketing as a whole was hit by data and AI, even though I have to say that few companies make good use of them.

This caused the birth of new problems such as ethics in AI and how to fight bias.

Training a machine with "racist" or offensive data leads to creating biased models. Shift this paradigm into SEO and imagine training a model with bad keyword data.

You will get terrible results.

Value-adding tasks don't change. Then why waste time on what doesn't add value? Just automate it once.

Data analysis is different but again, you can create your solution and recycle it. Then take action based on it.

Semantic SEO and other fields do not require coding. There are many solutions on the market that do it for you.

Some people prefer to focus on data and may want their tools or something different. To each their own.

Positioning yourself in this new niche of "data" SEO is much easier. Why compete in a saturated market when you can offer unique value?

Defend your competitive advantage or build one if you don't know how to market yourself.

Data skills will be more common in the future. Don't wait to plant a tree, do it now.

If you are going to do it, just start asap and focus on other areas of your career.

I prefer taking fast action rather than postponing.

Working with data brings other problems. At the same time, you get a different perspective on problems.

Why are most of the correlation studies useless? This is one example.

You start to notice some patterns that you didn't see before.

When people say that keyword count is useless it's not because they blindly trust Google.

It's just a terrible method for ranking pages, that's it. A billionaire company has no interest in using outdated algorithms.

Many can argue that Google's algos are not perfect. That's right, having good models for every language and being capable of understanding all the nuances is hard.

Many old tricks don't work anymore though and this is visible.

On the contrary, some SERPs are populated by copied content. This is not feasible for super competitive terms in good markets.

AI-generated content needs editing and you cannot ignore what your users want.

New models are just trying to improve the understanding of our language and to combine different information. The Knowledge Graph is a tremendous help for structuring data.

We are starting to see the beginning of a more data-centric era.

To sum up, there are people that want to get more confident with data.

SEO is one example of an industry affected by the AI hype. Including data skills works, you save time and resources in the long run.

If you enjoyed my content, like and retweet this thread. Let's spread some data love and help people to discover this relatively new trend.