Best BI Tool for SERP Data

Choosing a BI Tool To Work with Traject Data

Discover the best BI tools for SERP data analysis. Learn how to leverage Traject Data's SERP APIs for powerful insights and smarter decision-making.

Posted by Adam Smith | March 11, 2025 | 17 Minute Read

In This Post

Why would an SEO limit themselves to some keywords, when all keywords are available?

Historically, it’s been a matter of resources and capacity. The more data points you introduce, multiplied by the number of keywords you’re tracking, the faster it becomes unmanageable. When using a typical SEO platform to track keyword data, it requires prioritization and selection of a more limited keyword list that leaves off low-traffic queries and details about things like rich results.

That’s where big data, in the raw output form that can be parsed by BI tools, comes in to expand a keyword universe.

We’ll walk through the process of what raw, big keyword data looks like, how it’s given the structure that allows it to be manipulated, and the qualities your business should look for when choosing a BI tool that will allow you to turn data into insights.

Why use Big Data for SEO

SEOs or digital marketers have a finite amount of keywords for their brand that they have the capacity to track. Typically, this is determined by the keywords that are known to drive traffic to a site, with the threshold being a significant number of clicks.

What happens when you have a very niche longtail keyword that only generates a nominal amount of clicks? It’s probably not going to move the needle on your bottom line, so it gets left off the list of priority keywords to track. Then you have a few more very niche keywords that generate just another handful of clicks.

For many businesses, this list of “insignificant” keywords can grow into the thousands, where the aggregate of all those nominal clicks suddenly have a lot more power over your business than you originally expected. For example, listen to Wil Reynolds of Seer Interactive recount his experience with discovering that 300,000 previously ignored keywords with ~1 click each accounted for 92% of his client’s traffic.

The problem then becomes the ability to scale the keyword research process to track exponentially more keywords, as well as more SERP data points, like various rich results.

When the capacity of an SEO tool is limited, it makes sense to prioritize and limit keyword lists or the search features you track. However, moving to big data where the data is in its rawest form—and able to be pumped into any analytic environment—allows businesses to expand their keyword universe to all keywords, not just a “representative” sample size.

To start, you will have to decide what this analytic environment will be, and how you will process and manipulate it without a typical SEO platform. Traject Data can be directly connected to the BI tool that your company likely already uses to make other data-driven decisions. Next we’ll dig into the process the data undergoes so it can be parsed into useful data sets and made ready for that BI tool.

How Traject Data can be used

With more SERP data points now available for tracking, use cases for leveraging the data have increased as well. Here are a few things your business can do with Traject Data:

What does “raw SEO data” look like?

Raw data from the Traject Data SERP API comes in the form of JSON files that describe any given SERP, including the search features, ranked websites, and anything else that appears for that particular query. Imagine you query “teriyaki and wok” and you’re shown this SERP. You’ll notice that result #1 is a standard blue link, not a local pack, a video, or any other type of search feature.

The output from the Traject Data SERP API for this query would look like this:

Underneath “SERP” in the JSON file, each ranking position will be labeled by number and list out the attributes of that ranking position. In the “teriyaki and wok” case, you can see the data for the link, the description, and which types of search features do and do not take up position #1 for that query.

If you were to keep scrolling, the same data would be listed for position #2, #3, and so on for that query’s SERP.

This more comprehensive set of SERP data points allows SEOs to increase their control over all the possible rich results at their disposal. Tracking this data will show rich results they’ve won and lost to competitors, search features that are new to a keyword after Google makes an algorithm update, and other data points that will help them home in on all of their high-return traffic opportunities.

These are some of the most common rich results that we provide data on. You can click through on each to learn more about how you can leverage them.
Organic Results Paid Results Local Pack
Knowledge Panel Image Pack Video Pack
Answer Box Mini Sitelinks Events Pack
Shopping Box News Pack Job Pack
People Also Ask Related Searches More…

How is this data converted into something I can manipulate and draw insights from?

The information in the JSON files is pretty extensive to look through for just one SERP. This raw output would be almost impossible to manipulate without building logic, or using a tool that can sort through it to access what you need and create more relevant datasets.

The Traject Data SERP API JSON files are stored in Amazon S3 or similar. There, the data points from the JSON files are structured and tagged into individual fields so that they are able to be queried, sorted, and more easily analyzed. After the JSON files have been given this structure, using a product like Amazon Athena gives you the ability to query the database and pull the selection that’s interesting to you.

This staging ground and additional ability to organize the data creates a way for previously unmanageable amounts of data to be pulled into subsets that are relevant for a certain report or analysis.

Leveraging a BI tool

The dataset is now ready to be connected to your BI tool which will allow you to visualize it for easy understanding. From there, you can build reports and combine it with your business’s other data, like sales and marketing data.

BI tools are an important cornerstone to this process because the visualizations allow users to discover new insights with the time and expertise (or bias) that would typically go into creating pivot tables to investigate a hypothesis.

With the various tools that are compatible with Traject Data, users can toggle between different types of visualizations much more deftly than building out a new analysis manually each time. Expert level BI users can manipulate the data into more complex visualizations, but beginner level users can still glean a multitude of insights from the visualizations the tool suggests, once the data finishes populating.

Traject Data keyword data is able to connect to all of the most common BI tools on the market. To make a decision on which BI tool to use, consider the required skill level, pricing, and capabilities below.

Compare and contrast BI tools

Tableau

What it is

Tableau’s innovation was completely centered around enabling visual analysis.

Tableau connects and extracts the data stored in various places, and is touted as being able to pull data from just about any platform imaginable. It can extract “simple” databases such as excel or pdf files, to more complex databases like Oracle or AWS.

After ensuring thorough data access, Tableau emphasizes its ease of content discovery by allowing users to organize resources by project, recommending relevant data, allowing ‘go back’ with revision history, and enabling search.

Here’s an example Tableau sales dashboard. You’ll notice that showing the insights is highly visual, and that analytics can be performed almost instantaneously as users change any setting on the dashboard.

Source

Plans and Pricing

Tableau divides their product tiers by implementation and user needs. First, your business will need to decide whether you’d like to host Tableau on-premise, in the public cloud, or have Tableau host your server. Then decide how many of each of the following user types you need:

Tableau’s pricing differs between users of on-premise/public cloud and Tableau hosted, and bills annually. For on-prem, select the number of licenses you need:

Pros

Cons

The bottom line

Tableau is the top of the food chain for BI tools in terms of quality of analytics and visualization. However, for many users, the cumbersome process of piecing together multiple Tableau products and deploying on-prem may be too complex—and too expensive.

Looker

What it is

Looker is the data visualization tool owned by Google. Its main differentiator is its 100% browser-based experience, which eliminates the need for installation and maintenance at the same level as a tool like Tableau. Looker also emphasizes shareability in many of their opening statements, referring to the fact that the browser-based experience allows for link sharing, as opposed to sharing with files.

Like most other BI tools, Looker asserts that any novice can use it and that in-depth SQL knowledge can create more intricate reports, but isn’t strictly necessary. However, there does seem to be a significant investment in publishing lectures, documentation, and learning puzzles to appeal to Looker users across disciplines and learning styles.

With Looker, data analysts can use a language called LookML to create mini-applications that “add efficiency and power to data exploration.” Looker explains that the LookML syntax simplifies the development of powerful models and enhances the capabilities of SQL. According to them, “this language-based approach leads to faster query execution, and optimizes performance along the way.”

Below is an example of a Looker E-commerce dashboard that visualizes different data points from a sample company.

Source

Plans and Pricing

Looker is unique from other BI tools in that it doesn’t publicly share its pricing, reasoning that it’s customizable for small, medium and large businesses. SoftwareConnect estimates the following prices:

Pros

Cons

The bottom line

If you don’t want to install and maintain an on-prem solution, Looker’s hosted platform has data visualization capabilities that rival or exceed other BI tools. You’ll be able to enjoy a report sharing experience that isn’t cumbersome, but watch out for load times and the inability to change the functions of the tool itself.

Domo

What it is

Domo Business Cloud, a low-code data app platform, refers to itself as: “the platform that allows everyone to drive action from data.” As with other top data visualization tools, users can combine data from disparate sources, and according to Domo, without needing duplication or pre-transformation to use.

Domo also includes collaboration tools that let teams communicate directly on visualizations with messages and annotations, in addition to scheduled updates and benchmarking alerts similar to those of other BI tools. There isn’t an on-prem deployment option, but Domo touts the cloud-based solution as a baked-in method to help your business scale indefinitely.

Below is an example of a Domo dashboard that uses Traject Data and shows the breakdown of all the different types of search features that appear in the chosen dataset.

Plans and Pricing

Domo’s editions are primarily divided by number of users, support level, and administrative options. The enterprise edition offers unlimited storage, data sets, personalized data permissions, and advanced admin controls.

Domo previously (until 2019) offered two plans: Professional ($175/user/month) and Enterprise ($250/user/month). They have since removed pricing from their website.

Pros

Cons

The bottom line

If your business favors cloud-based over on-prem and will be using the tool for extensive communication and collaboration while building visualizations, Domo is a great option.

Power BI

What it is

You can expect that Power BI , the data visualization tool owned by Microsoft, will play nicely with other Microsoft products like Azure and Office. In fact, this is one of the first points that the Power BI website confirms.

These other Microsoft products, such as Power Apps, Power Automate, and Power Virtual Agents, that create an “end to end” experience give users a feeling similar to picking and choosing certain Tableau products for a more customized experience, not the singular consistent experience of Looker and Domo.

Power BI connects to most types of on-premise databases, and they have a large and growing list of cloud-based connection options as well. Users report that it also works well for companies that don’t have a data warehouse solution, because Power BI acts as a facilitator that enables data set processing.

It has around 16 different chart types, which is a good, middle-of-the-road amount if you want to represent your data in a variety of formats and visualizations.

Below is an example of a Sales and Marketing Dashboard created with Power BI.

Source

Plans and Pricing

Power BI has 2 plans. The Pro plan is the most inexpensive BI solution out of comparable tools in the market, but the Premium plan can get pricey.

Power BI Pro

Power BI Premium

Pros

Cons

The bottom line

If you don’t have a lot of budget and won’t be using datasets that exceed the size limit, Power BI will give you top-tier analytical capabilities—and may even listen to you on the updates you’d like to see.

Final Thoughts on Choosing a BI Tool for Traject Data

Because Traject Data works with all major BI tools on the market, decide on a tool that fits your business best with the above information in mind.

Once you’ve chosen a BI tool, you’re prepared to follow these 3 steps to begin visualizing the additional SEO data points our SERP API provides:

  1. Pull the JSON files for each keyword you want to track with the Traject Data SERP API.
  2. Use S3 and Athena (for example) to make a database from your JSON files that you can query and filter to create a more relevant dataset.
  3. Connect your dataset to the BI tool that works best for you and your organization.

Complete these steps, and you’ll have visualizations of endless keyword data to inform your SEO strategy and business decisions as a whole.