Data SEO is a scientific approach to search optimization that relies on the analysis and activation of data to make decisions.
But that’s not all it entails.
If you want your organization to succeed in data SEO, there are three distinct specializations you need to develop in addition to SEO knowledge and experience.
These are the skill sets of the data scientist, data analyst, and data engineer.
Whatever your budget, it is possible to improve your SEO with a data-backed approach. In fact, the concepts used by data scientists are becoming increasingly accessible.
Here are the skill sets you need to make data SEO a part of your repertoire.
1. The Data Engineer
Data engineers are the professionals who prepare the company’s foundational big data infrastructure.
They are often software engineers who design, build, integrate data from various resources and manage large amounts of data.
Their main goal is to optimize performance where it comes to the company’s access to its own data.
They frequently use ETL (Extract, Transform and Load) to centralize data, creating large data warehouses that can be used for reporting or analysis.
The main skills and tools can be summarized in the following list:
- Data streaming.
Why Should You Centralize Your Data?
First of all, you don’t have infinite time available. Not only is it a waste of time to juggle between tools, but it is also a waste of information not to be able to combine data from different sources.
Often, you have to combine your data with business data (CRM), finance data, and many other types of data that always come with access and security concerns.
Therefore, it is wise to build your SEO data warehouse by ensuring that your SEO tools allow you to export the data properly.
The data engineer is the most competent person to centralize both unstructured data such as texts and comments, and structured data such as that in databases and APIs.
However, there are many difficulties.
The first difficulty concerns the volume of information.
If you have more than 100,000 pages on your website and a lot of web traffic, weekly crawls and daily logs will quickly take up a lot of space.
This becomes even more complex if you add your CRM data and data on your competitors.
And if the system is not based on the right technologies you can have incomplete, missing, or false data.
There are many traps in addition to the volume of data.
These include currency concerns if you work internationally, where you will have to deal with the exchange rates issued each day by the authoritative financial institution in your country.
They might also include time differences. If you calculate a turnover per day in France and that a part of the turnover takes place in Canada, for example, you have to launch the calculation when it is midnight in Canada and not midnight in France.
These are just a couple of examples, but every business is full of traps.
Next, you have to keep a close eye on the veracity of the data because data can be corrupted quickly:
- An API changes its return parameters and several fields no longer obtain a value.
- A database is no longer updated because the hard disk is full.
No matter what the case, you must quickly detect this type of anomaly and correct it as soon as possible.
Otherwise, the dashboards produced by this data will be erroneous. It’s tedious and time-consuming to launch retroactive scripts to recalculate everything.
If you don’t have a data engineer on your team, you must at least have a manager who verifies the consistency of the data you retrieve from the different SEO tools.
SEO tools now allow you to easily pull the following data, which you need to monitor for variations up or down:
- Analytics data: lost script, tracking error.
- Crawl data: crawl too long, crawl canceled.
- Server log data: missing periods.
- Keyword tools data: adding new keywords.
Communication is key. With good incident management, the whole data chain becomes coherent for use by SEO experts, data analysts, and SEO consultants.
2. The Data Scientist
The data scientist will enrich the data with statistical models, machine learning, or analytical approaches.
Their main mission is to help the company transform the data made available by the data engineers into valuable and exploitable information.
Compared to data analysts (see below), data scientists must have strong programming skills to design new algorithms, as well as good business knowledge.
They must be able to explain, justify and communicate results to non-scientists.
Which Languages Should Be Used & Which Methodology?
The most popular technologies in 2021 for data science are, in the order of popularity:
If you can’t decide on a programming language, I can give you some tips.
First of all, use the most popular language in your company.
If the majority of the developers are using Python, there’s no need to push for R because trying to maintain code in R will double the maintenance cost. This way, you show your ability to adapt.
Then, let the technologies on which you want to deploy your applications guide your choice.
For example, if your team produces its dashboards with Shiny, then R will become your best friend.
After that, note that R and Python are relatively similar if you compare them to C or to Scala. If you’re building your CV, it is ideal to master both.
As far as methodology is concerned, the scientific method prevails and leaves no room for empiricism.
You want to clearly define the context and objectives, then explain the different methods identified and present reproducible results.
Finally, it’s entirely possible that you don’t have the time or the vocation to do data science yourself. In this case, I recommend using a service provider.
Regardless of the agency, the deliverables and criteria for success must be clearly defined with the chosen agency so that there are no unpleasant surprises when using the solution.
Additionally, you may also need to consider data science platforms. The options available to you will vary widely depending on your budget.
3. The Data Analyst
Data analysts are business-oriented data professionals who can query and process data, provide reports, summarize and visualize data.
They know how to leverage existing tools and methods to solve a problem and help people across the company understand specific queries through ad hoc reporting and graphics.
They base their work on the data warehouses of data engineers and the results of the algorithms of data scientists.
Their skills are diverse and can include statistics, data mining, and data visualization.
What Software Should Be Used?
Data Studio is well known in the field of SEO but in business, the market is dominated by Tableau Software, SAP, Microsoft, and IBM.
The recent acquisition of Looker by Google positions it to be among the leaders in the years to come, as well.
Be careful in choosing a data visualization solution.
Data analysts’ ability to quickly adapt to tools brings us back to a “Make or Buy” issue. If you have the budget, proprietary solutions will save you a lot of time.
How to Create Perfect Dashboards
There are many methods but here is the SMART goals framework is easy to remember and can apply here, as well:
- Keep charts specific and simple, as too much information kills the information.
- The y-axis and x-axis must illustrate measurable data.
- A graph should focus on achievable metrics, as there is no point in monitoring metrics that will have no influence on your business. Weather is an excellent example: it has a crucial role on some sites and none on others.
- Dashboards should always have relevant summaries in order to be read quickly and understood. If it takes more than three seconds to understand them, you can improve the end result. First, users may be satisfied with an overview, but then they may need a more granular view of the data by juggling filters.
- The most important data is time, so be sure to track time-based data comparing each day, month, year, etc.
Of course, keep in mind that if data analysts master SQL, they can turn to open source solutions like Metabase or Superset.
Finally, analysts with programming skills will want to look at Shiny for R or Dash for Python.
Data SEO Projects
The world of data SEO has certainly become less obscure.
As for any project, you will either need to surround yourself with the right people to succeed in large-scale data projects or be well-trained in the professional skillsets we covered in this article: data engineering, data analysis, data science.
At this point, you have probably identified weaknesses or strengths within your company while reading this article.
Don’t hesitate to build out on your weak points by recruiting, outsourcing or training.
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All screenshots taken by author, May 2021