Data has always been a hot topic in digital marketing. The ability to tailor strategies and understand the customer journey in-depth, made it possible for brands to set up more accurate campaigns while increasing the effectiveness and efficiency of their marketing. Since a big part of these possibilities is fueled by tools and technologies, we are bringing you an overview of the main data visualization tools which will be on the final end of the data analytics process. These tools are widely known throughout the market and very sought after by companies of all shapes and sizes.
An overview of the main data visualization tools
Before data visualization tools existed, much of the data used to guide digital strategies was static and had to be recollected, reanalyzed and revisualized on a seasonal basis. This gave teams a huge amount of manual work, handling of spreadsheets and other tools. As dashboards came into wider popularity, we have the availability of dynamic, customizable and always up-to-date data, which certainly brought the work of data-related professionals to a higher level.
Having one central data visualization with KPIs that are relevant to your main strategy is essential for a data-driven business.
We created a mind map using a self-made ego graph that shows the most popular tools in Google search.
Why use data visualization tools?
In organizations, data is usually available in many formats and technologies, requiring different types of access and further calculations. Having one central data visualization with KPIs that are relevant to your main strategy is essential for a data-driven business.
Having an overview of different types of data that you might have available makes it easier for data enabled professionals in your company to access (who are not necessarily data analyst/scientists, but could be marketing, media, digital, finance or HR, and whose actions depend upon the analysis of different data sources which often use less powerful tools, such as Microsoft Excel or Google Sheets.)
1. Google Data Studio
As the main provider of the most used tools in digital marketing, Google has brought to the market Google Data Studio, which is free to use, with a simple interface and a lot of possibilities. It has been available since 2016. Most of the data that you can get on Google Tools are also available here through native connectors, enabling you to increase exponentially the possibility of uses for your data through multiple platforms. Many features on Google Data Studio allow you to have a custom designed data flow for your agency and customers, which greatly simplifies your daily tasks and data reporting.
Google Data Studio’s data sources
The biggest advantage of Data Studio is that since a lot of the digital channels are owned by Google, you can connect easily to Ads, Analytics, Sheets, BigQuery and so on. The only extensively used tool that’s not available natively on Google Data Studio is Facebook Ads, which you can get through signing up to an extra tool like Supermetrics.
Who is Data Studio for?
Data Studio works best for Digital Marketing agencies and inhouse digital marketing departments. Data Studio is an excellent first step towards the world of data analytics, as you can implement data-driven culture and processes and then later on scale to more complex tools when a higher maturity of a data-driven business culture is reached.
Google Data Studio pros & cons
- Huge availability of connections (Paid and Free)
- Created with digital analytics data in mind
- Belonging in the familiar Google ecosystem
- Limited drilling of data in comparison with other tools
How much does Google Data Studio cost?
Free. May need additional connectors which are paid.
Tableau is a data visualization tool focused on business intelligence, brought by Salesforce and first seen around 2003. Created by Stanford University students, Tableau enables users to get in-depth information, combine multiple columns and build super structured dashboards.
Tableau’s data sources
Tableau offers a wide range of connectors, including Excel, BigQuery and many kinds of SQL variations. Unfortunately it doesn’t have native connectors to a lot of digital marketing platforms, therefore needing additional collection of data through APIs or other methods.
Who is Tableau for?
The Tableau software is very popular with both business users and specialists, due to its ease of use, high capacity, and nice looking visualizations.
Tableau’s pros & cons
- Detailed Analysis and nice looking visuals
- No need to code to get most of the work done
- Allows you to drill down data further than other tools in the market
- It is a paid tool
- If you use the public version, the data can’t be private
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How much does Tableau cost?
The cost is $70 for the individual creator version, which can scale if you are working on a bigger team. There is also a public version which allows you to study the tool and make your own dashboards focused on non commercial use.
3. Power BI
Microsoft’s Power BI is one of the most popular business intelligence tools, because it is a part of Microsoft Suite and has been available since 2015. The interface resembles the unforgettable Microsoft Excel, with many more customizable elements.
Microsoft Power BI’s data sources
Integrations with most Microsoft tools are available for Power BI, providing a good integration with MS Excel and Azure servers. For digital marketing, unfortunately there are no integrated connectors available, therefore you need to collect the data externally.
Who is Power BI for?
Microsoft Power BI is works best for enterprises, business users and specialists. Since Microsoft dominates the corporate world, it tends to be where the tool is more visible and adopted.
Power BI’s pros & cons
- Popularity and familiarity due to belonging on the Microsoft Ecosystem
- Accessible pricing
- Steep learning curve
- No native connectors to widely used digital tools
How much does Power BI cost?
Individual pro lesson starts at €8.40 per user per month
An alternative for the more hands-on programming professionals is the Seaborn or the more classical matplotlib library on Python. If you are working directly with API data, any kind of scrapping or just using Pandas to clean an extensive dataset then you can use Seaborn on the same script that you are working on and therefore make insightful visualizations and statistical analysis.
Which data visualization tool should I use?
There is no right or wrong answer to this question. It will depend on your needs and experience. Power BI wins for availability, familiarity (interface-wise, if you are a Microsoft person), popularity and technical possibilities. Tableau wins for the complex analysis and customization it allows. Google Data Studio wins for its simplicity, integration, familiarity (interface-wise, if you are a Google Person), and price. To each its own.
It also highly depends on the data you have. For example, it’s easy to connect Google tools to Data Studio. When you actually run data warehouse infrastructure, the range of tools gets even bigger (Metabase, Quicksight, Looker, etc.).
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