Three varieties of knowledge science search engine optimisation groups and the way they work

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When it comes to successful data science for SEO, nothing is more important than having the right team.

Challenges in obtaining and ensuring the consistency of the data as well as in the choice of machine learning models and the associated analyzes all benefit from the fact that team members with different skills work together to solve them.

This article introduces the three main types of teams, who is on them, and how they work.

Let’s open the floor with the loneliest SEO expert in data science – the team of one.

1. The lonely data science SEO professional Science

The one-man team is often a reality in small and large structures. There are many versatile people who can manage both the SEO and data functions themselves.

The lone data science SEO expert can generally be called an SEO expert who decided to take advanced courses in computer science to focus on a more technical side of SEO.

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You are proficient in at least one programming language (such as R or Python) and use machine learning algorithms.

They are closely following Google updates like Rankbrain, BERT, and MUM as Google continues to integrate machine learning and AI into its algorithms.

These professionals need to be skilled in SEO process automation to scale their efforts. This could include:

  • Automatic indexing of new URLs in Bing.
  • Creation of sitemaps with the new URLs for Google.
  • Text generation with GPT models.
  • Anomaly detection in all SEO reports.
  • Long-tail traffic forecast.

In my organization, we share these SEO use cases in the form of a Jupyter notebook. However, it’s easy to automate them with Papermill or DeepNote (which now has an automatic mode for starting Jupyter notebooks regularly) to run on a daily basis.

If you want to mix it up and add to your professional value, there are excellent training courses for SEO enthusiasts to learn data science – and vice versa, for data scientists to learn SEO as well.

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The only limit is your motivation to learn new things.

Some prefer to work alone; Ultimately, it eliminates any red tape or politics that you might (but don’t necessarily have to) find on larger teams.

But as the French proverb says: “It goes faster alone; We’ll go further together. “

Even when projects are completed quickly, they can end up being as successful as they could have been with a wider range of skills and experience.

Now let’s get out of the lonely SEO and move on to teams of two.

2. The Data Science SEO MVT (Minimum Viable Team)

Perhaps you already know MVP as a minimum viable product. This format is often used in agile methodologies where the project starts with a prototype that develops in one to three week iterations.

The MVT is the equivalent for a team. This team structure can help minimize the risks and costs of the project while introducing more diverse perspectives.

It consists of putting together a team of just two members with complementary skills – often an SEO professional who also understands the mechanics of machine learning, and a developer who tests ideas.

The team is formed for a limited period of time; usually about 6 weeks.

For example, when we use content categorization for an e-commerce site, often a person tests a method and implements the most efficient one.

However, an MVT could run more complex tests on multiple models at the same time – for example, keep the categorization that occurs most often and add an image categorization.

This can be done automatically with all existing templates. The current technology makes it possible to achieve 95% of the correct results, from this point the granularity of the results comes into play.

PapersWithCode.com can help you stay up to date with the latest technology in every area (e.g. text generation) and most importantly provides the source code.

For example, OpenAI’s GPT-3 can be used for prescriptive SEO to recommend actions for text summary, text generation and image generation in impressive quality.

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3. The Data Science SEO Task Force

Step back in time with me for a moment and let’s take a look at one of the best collaborations of all time: The A-Team.

Everyone on this iconic team had a specific role and that is why they were brilliant on each of their missions together.

Unfortunately there were no episodes on SEO. But what could your data science SEO task force look like?

You certainly need an SEO professional who works closely with a data scientist and developer. Together, this team will carry out the project, process the data and use the machine learning algorithms.

The SEO professional is best positioned to act as the project manager and handle communication with management and external stakeholders. (In larger companies, there may be dedicated roles for the team manager and the project leader.)

Here are some examples of projects this type of team could be responsible for:

  • Set up an enterprise data warehouse (a ready-to-use data warehouse that brings together business, market share, technology, and content data).
  • Identification and resolution of “zombie” pages.
  • Detection of new queries.
  • Forecast of traffic / profit after certain actions.

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Data SEO Compliance

Of course, teams need tools to maximize their efforts. This brings us to the idea of ​​data SEO compliant software.

I believe there are three principles here that you should carefully adhere to in order to avoid using black box tools that will give you results without explaining their methods and algorithms.

1. Access to documentation that clearly explains the algorithms and parameters of the machine learning model.

2. The ability to reproduce the results yourself on a separate data set to validate the methodology. This does not mean copying software: all challenges lie in the performance, security, reliability and industrialization of machine learning models, not in the model or the methodology itself.

3. The tool must have had a scientific approach by communicating the context, the goals, the methods tested and the final results.

Data SEO is a scientific approach to search optimization that relies on data analysis and the use of data science to make decisions.

Regardless of your budget, it is possible to implement data science methods. The current trend is that concepts used by data scientists are becoming more and more accessible to anyone interested in the field.

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It is now up to you, with the right skills and teams, to take responsibility for your own data science projects. For your data science SEO success!

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