Skill Sets Semalt Uses To Support Data SEO Success For Its Clients

Many business owners make the mistake of assuming that data SEO is limited to data science. In this article, Semalt hopes to show you skill sets from different specializations we use to help organize your organization and teams for your success. 

But first, let's discuss the basics of data SEO. Since data SEO is a strange term to many non-SEO professionals, we will begin by explaining what it means.

What is Data SEO?

Data SEO is a scientific approach to search optimization. In this case, we rely on the analysis and activation of data to make decisions rather than the typical SEO approach. 

However, it goes further. 

Semalt began using data SEO because we wanted our clients to exceed their competition significantly. In order to help our clients succeed in data SEO, we need to develop these three unique specializations in addition to our SEO knowledge and experience. 

These three specializations are:
  • Data scientist 
  • Data analyst
  • Data engineer
The most interesting part of all this is that you can improve your website's SEO with a data-backed approach with whatever budget you have. In fact, our data concepts are becoming increasingly accessible. 

Here is an explanation of what role each of the three elements has to play in making data SEO work for our clients. 

The Data Engineer

Data Engineers are the professionals responsible for preparing the foundational big data infrastructure. We have software engineers who build, design and integrate data from a number of sources. They also manage large amounts of data efficiently. 

Data engineers are responsible for optimizing performance when it comes to your ability to access your website's data. Especially for large companies, data engineers take on the role of legal managers for GDPR or CCPA compliance. It is also common for these individuals to work as security managers. 

Data engineers use Extract, Transform, Load, or ETL to centralize large amounts of data by creating a data warehouse they visit to analyze or report data conveniently. 

Here is a list of the main skills and tools we look for in a data engineer:
  • MapReduce
  • Hadoop
  • Pig 
  • SQL
  • NoSQL
  • Hive 
  • Data streaming 
  • Programming

Why is centralizing data a good idea?

Handling large amounts of data scattered around the place can be time and resource-consuming. Considering that we need to be time conscious, we need your data arranged in a format that makes it easy to handle with little or no space for errors. 

Having to juggle between multiple tools is a waste of time. It is also a waste of information when data from different sources can't be put together in one place. For the entire business to grow, we need to collect data from everything that can impact the business, which includes business data (CRM), financial data, and several other offline data that come with access and security concerns. 

Therefore, the best course of action will be to build an SEO data warehouse for your business by ensuring that all your SEO tools allow us to export data properly. The data engineer is best suited for centralizing structured data that comes in the form of text and comments or databases and APIs. This job comes with its difficulties, so we do not advise you to try this on your own. 

The first hurdle a data engineer has to overcome concerns the volume of information. With a website with over 100,000 pages, a lot of web traffic, daily logs, and weekly crawls will take up a lot of space and time. If we have to add your CRM and the data to your competition, this job gets even more difficult. So if the system isn't based on the right technologies, you may encounter incomplete, false, or missing. These are only, but a few traps data engineers have to overcome when handling large data volumes. 

Having to work internationally also comes with its burdens. Dealing with varying exchange rates issued on a daily basis can complicate things a bit. We also have to consider the time differences of these countries. For example, if we have to launch the turnover per day in Italy and a part of that turnover takes place in the UK. In this case, we have to launch the calculation when it's midnight in the UK and not when it's midnight in Italy. 

The Data scientist

We need data scientists to enrich the data with statistical models, analytical approaches, and machine learning technology. Having a data scientist is critical because they help the company transform the data collected by the data scientist into valuable information. Think of it as the relationship between a gold miner and a goldsmith. Compared to a data analyst, a data scientist must have high-level programming skills to be able to design new and improved algorithms as well as have good business knowledge. 

Data scientists must have the ability to communicate, explain and justify their results to other non-scientists. 

What are the Languages and Methodologies data scientists use?

For a data scientist, here are some of the most popular tools at their disposal for 2021:
  • Java 
  • Python 
  • Scala
  • R.
  • Julia
Our data scientists are selected using a client's preference. If a client wants a java user, we assign a data scientist who specializes in that language. 

If the majority of developers are using Python, we generally wouldn't advise a client to use a programming language like Julia because coding in that language will double the maintenance cost. 

When choosing a language, you should let the technology on which you want to deploy your applications be your compass. 

We limit our explanation of these languages to only situations that require us to define context and objectives clearly. 

The Data Analyst

Data analysts are the business-oriented specialist we add to make the perfect elixir. They are professionals who can query the processed data, visualize and summarize data, and provide reports. 

A data analyst understands how to leverage existing tools and methods to solve problems and help people across the company understand specific queries, ad hoc graphics, and reporting. 

To perform their job effectively, a data analyst must base their work on the data warehouse and the results obtained from data scientists. Their skills are diverse and can include data visualization, data mining, and statistics. 

What software do data analysts use?

One of the most popular software used by data analysts is Data Studio. This is one of the most common apps in our field as SEO pros, but other software like Microsoft, Tableau Software, and IBM are of great importance to a data analyst. Recently, Looker was acquired by Google, which makes it one of the leading software this year. 


In the world of data, SEO continues to become less obscure as time passes. But to succeed with data SEO, you need professionals who understand what is expected of them. This is what Semalt offers. 

Our organization comprises well-trained professionals who have the necessary skillsets mentioned above. If you're reading this, the chances are that you most likely have identified the weaknesses or strengths in your business as you read past each point in this article. If you've made it this far, you should have a clear understanding of what you must do to improve data-wise. 

Do not hesitate to build on your weak points. Give us a call today and have us put your business in the best position for success. 

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