Data Analytics is one of the most in-demand skills across any and every industry.
This article lists Data Analytics as one of the top 10 in-demand tech skills to master in 2021.
The good news is: there are loads of tools available to help you with your data. The bad news is: with so many tools out there, it can be pretty overwhelming to figure out which one is right for you.
So, what data analytics tools should you be using?
Picking the right tool to analyse your data can be tricky. There’s a lot to consider:
- What are your organisation’s business reporting needs?
- Does the tool have the functionality you require?
- Can you afford to pay for a tool, or will you need one that’s free?
Here at Growth Tribe, we know how important the right tools are for getting you where you want to be. And luckily for you, we’ve decided to list 5 of our favourite data analytics tools, what we like about each of them and where you can get hands-on with learning how to use them.
1. Google Data Studio
Google Data Studio is a free tool that allows you to turn your data into clear and impactful dashboards and reports. It’s a classic for a reason.
The best thing about this platform is that it integrates with other Google products like Google Analytics, Google Ads and Google Search Console. This makes Google Data Studio a dream if most of your data sits within the Google Ecosystem. All of your data will sit in the same place without having to spend time migrating external data sets.
New to building dashboards? Not a problem! Data Studio also has a quick start guide that takes you through the fundamentals.
However, if you’re looking to create complex dashboards or require a high level of flexibility and customisation for your reporting, Data Studio may not be for you.
This is because Data Studio lacks some of the most basic functions that are staples in other tools like Tableau.
Nonetheless, this tool is more than suitable for basic data analysis and data reporting.
“The global economy is built on two things: the internal combustion engine and Microsoft Excel. Never forget this.” - Kevin Hector, Twitter
The godfather of data. Microsoft Excel is one of the most popular tools for creating spreadsheets and simple data analysis. Although it’s a basic tool, Excel remains one of the top skills to learn that are important for business.
This tool is usually offered as part of a package with other Microsoft products, but can also be bought separately. It's a great starting point if you’re still new to the world of data analytics.
At Growth Tribe, we like to think of Excel as the Swiss Army Knife of analytics. It connects to many data sources, can perform data manipulation, analysis, and visualisation, on top of the fact that it's a great business tool!
Here’s a cool trick: Did you know that you can create an instant chart on Excel by just selecting any cells in your data range and pressing Alt+F1? Check out this blog to see what other useful functions Excel has to offer.
Excel has a wide range of useful functions. For example, the Pivot Table tool. With pivot tables you can easily analyse and summarise data sets, allowing you to quickly create useful reports without having to learn a bunch of formulas. This is key for people that frequently report on large amounts of data.
This video even explains how to create pivot tables in only 6 minutes!
Excel does have its limitations, however. The software can run slowly when working with large datasets. It also tends to approximate large numbers, which can lead to inaccuracies in your data if you’re not paying attention.
Overall, with years of development on its belt, Excel is reliable and versatile with a wide range of analytical features.
No matter your data analysis needs, Excel succeeds!
Tableau is one of the best data and visual analytics platforms around. It allows you to create reports and share them across multiple devices and platforms.
The team at Tableau put a lot of work into researching what kind of graphs and charts are the most effective, so users have a super optimised experience when it comes to presenting your data.
One of our favourite Tableau tools is Tableau Prep. It’s a handy tool that helps you prepare your data for analysis. It’s super user-friendly and flexible not only in terms of producing custom visualisations, but also in allowing you to share on a large scale in a safe way. You can see it in action here.
When it comes to connecting with other service providers, Tableau can be configured with any of the cloud service providers (AWS, Azure etc) and sit with your infrastructure. This means that no matter which cloud your data sits on, you’ll be sitting on cloud 9 working with Tableau.
But what sets Tableau apart from its competition is its drag and drop interface. This especially comes in handy when you’re preparing reports with multiple data sets, making data visualisation quick and easy!
Sounds great so far, right? But nothing’s perfect.
The biggest frustrations with Tableau are its high cost. There are packages tailored for individuals as well as teams and organisations (with extra add-ons available).
Another pain point is that it doesn’t support custom visual imports. Tableau isn’t a completely open platform, so saving time by importing visuals isn’t really an option. Instead, you’ll have to recreate the visuals yourself.
Despite these drawbacks, top-notch data capabilities make Tableau one of the most important data analytics tools to check out.
💡 This is a good time to mention that we cover both Excel and Tableau in our Data Analytics Immersive Course.
Our course will help you confidently navigate spreadsheets, tell powerful stories and create beautiful visuals with your data.
Python is the programming language of choice for data scientists and technical analysts because it’s easy to learn and free to use. It can be used for everything from data scraping to analysis and reporting.
Wes McKinney’s Python for Data Analysis is filled with practical case studies and gives an in-depth guide to all the ways Python can be used to solve your data analysis problems.
Python is also super versatile. It can integrate with a ton of data visualisation and machine learning third-party packages, in addition to handling analyses on its own.
Over the years, Python has built a vast library of...well...libraries.
These libraries provide access to many types of functionality. For example, Pandas is a software library written for Python that allows you to do everything from importing data from Excel sheets to processing datasets for time-series analysis.
One drawback of using Python is its execution speed. Many argue that Python is much slower than other programming languages. But what it lacks in speed, it makes up for in simplicity and intuitiveness.
If you’re new to programming, Python Wiki gives clear instructions on how to get started.
5. Microsoft Power BI
Power BI is a suite of business data analysis tools that allow you to bring your data to life with live dashboards and reports. It’s still fairly new (released in 2015) and you can consider this newish software to be Microsoft’s answer to Tableau and Data Studio.
It comes in three versions - Desktop (free), Pro and Premium (both priced).
Power BI has amazing data connectivity - being able to work seamlessly with other tools Google Analytics, Facebook Analytics, and of course, Excel (being another Microsoft product). If you’re not convinced, here’s a lengthy list of data sources that Power BI supports.
It also has a wide range of easy-to-use functions. For example, as shown in the screenshot below, instead of learning complicated query language, you can just type simple sentences to get a quick analysis.
This video from Encore shows you how easy it is to create an interactive sales report in just 10 minutes using Power BI.
Power BI isn’t without its disadvantages, though. For one, it’s a point-and-click software where users access the options to edit the dashboard through icons and drop-down menus. This means that creating any type of dashboard will involve a lot of mouse-clicking (RIP fingers). This is where features like Tableua’s drag-and-drop interface are missed.
Lets say, for example, you wanted to change all titles that show the year as 2020 to 2021. If you’re using Power BI, the only way to do this is to manually find each one, clicking to get to the title menu, and changing it.
With programming languages such as Python, however, you could get this done with a quick search-and-replace or by setting a variable that only has to be defined once.
To use any of these data tools to their full potential, you’ll need to take action and level up your own data skills.
In our Data Analytics Immersive Course, our experts will give you the knowledge and skills you need to confidently use data in your role.