Mastering Data Analytics: Essential Tools Every Analyst Should Know About

Did you know India is expected to witness around 11 million job openings for data analysts by 2026?

With the rise of the use of data, there is a high demand for data analysts. Whether you're just starting out or have been working for some time now, you need to be well-versed and aware of analytics software.

However, with countless tools available, it can be overwhelming to choose which one best fits your work. Fortunately, there are some top-notch tools that have, time and again, proven to be invaluable for data analysts. This article will cover the 10 best data analytics tools every analyst must know about.

GrowthJockey is here to support your analytics efforts and offer data-driven insights to redefine your business growth.

1. Power BI

Microsoft Power BI is one of the robust business tools used by data analysts. With its comprehensive suite of data visualisation features, the tool is great for data analysts. You can bring together data from different sources for analysis. Once that's done, you can present your analysis through reports, dashboards, and visualisation.

Power BI's main selling point is its great data connectivity - it operates effortlessly with SQL servers, text files, and cloud sources like Facebook Analytics and Google.

What's even better is that it goes beyond the basics. Power BI supports machine learning models and other AI-data-driven analysis features. Plus, it works well with other Microsoft tools. So, if you're already using Microsoft tools, this analytics tool would be a seamless addition.

Among Microsoft Power BI's many benefits, some notable ones are:

• No speed or memory limitations to worry about.

• Access data on almost any device within or outside your organisation.

• Customisability using Python and R.

• Import, filter, and translate data with ease using Power Query.

2. Tableau

Tableau is one of the excellent data analytics tools. It's all about making data visualisation and analysis simple and interactive for users. If you're not a coding expert - no worries. Tableau is designed to be beginner friendly.

This tool features a smart drag-and-drop interface that enables analysts to quickly and rather easily analyse data, create interactive dashboards, and acquire insightful visualisations. Even better, it features a variety of visualisations, including charts, graphs, and maps. It can run on-premises or on-cloud.

Notably, whether you're looking to spot trends, discover patterns, or uncover correlations, Tableau offers the means. It's ideal for handling large data sets, complex calculations, blending data, and powering up your analysis efforts.

3. Metabase

Metabase is an open-source tool that allows powerful data visualisation, querying, and instrumentation. It enables you to ask questions about your data and shows answers in formats that make it easy to understand - whether that's a detailed table or bar chart.

What's more, users can easily connect to a range of databases and explore and analyse data from different sources with a single interface. Metabase offers visibility into data assets and supports enforcing policies regarding data governance.

Some positives of using the tool for data analytics include:

• Watch key business metrics such as user growth, conversion rates, churn rates, and more. Automatically share the reports to your team or yourself or link them to your internal dashboards.

• Create shared reports and dashboards to encourage data-driven decision-making and discussions in your organisation.

• Effectively manage user permission and access control to make certain that sensitive data is accessed by authorised personnel.

4. Python

Python is well-known to data analysts because of its ease of use, versatility, and wide range of remarkable uses. Any data science task you want to achieve can be done with Python. This is mainly owing to its rich and vast ecosystem of libraries suited to a variety of analytics tasks.

For instance, you have Panda and NumPy libraries that are excellent for streamlining highly computational tasks. Libraries like Matplotlib are great for data visualisation and reporting.

You can even automate repetitive data processing tasks with Python, saving energy and time. Being an open-source programming language, it's free to use and constantly improved by a huge community of developers.

With that said, Python's popularity is because of its simplicity. Even individuals new to programming can grasp the language rather quickly and easily perform data analytics operations.

5. Looker Studio

Looker Studio redefines analytics with a user-friendly interface and advanced visualisation features. It converts complex sets of data into easy-to-understand reports. The tools allow the integration of data from different sources into a single platform.

Looker supports over 800 data sources, including third-party services such as LinkedIn and Facebook Ads. This vast compatibility ensures comprehensive reporting.

In addition to that, Looker Studio reports are dynamic, updating automatically with new data from these sources. Users can enhance their reports using tables, graphics, and charts. They can make them aesthetically appealing with customisation options such as colours, text, and branding elements.

The tool allows users to share reports with team members or clients with control access levels. This means you can decide who views or edits the reports, ensuring security and collaboration.

6. Google Analytics

Google Analytics is one of the most popular data analytics tools for marketing and SEO. It allows you to track website performance and collect insights on visitors. It can help companies determine top sources of user traffic and find where their maximum site visitors are from. Plus, it can gauge the success of marketing initiatives and campaigns.

Even better, you can track whether you have achieved your business goals and trends in user engagement.

7. Microsoft Excel

A classic tool in the world of data analytics, Microsoft Excel is the go-to tool for both seasoned analysts and less technical individuals. While it's worldwide known as the best spreadsheet software, Excel combines robust analysis capabilities with a user-friendly interface. It also easily integrates with the Microsoft Power BI stack.

Some major benefits that Excel offers to users are:

• A range of collaboration and data-sharing features, including connecting to various data sources and sharing workbooks with the cloud.

• Enables sorting and filtering large amounts of data to spot patterns and trends.

• Build pivot tables and charts to summarise data and present it to decision-makers.

• Streamlines data manipulation and crafts compelling visualisation with graphs and charts.

8. Metricool

Founded in 2016, Metricool started with the purpose of providing a simple, reasonable, and comprehensive solution for managing and analysing social media. Now, it has transformed into one of the best tools used in data analytics for digital marketing.

Metricool can be used by businesses, marketers, and advertisers worldwide to manage, analyse, and track the success of their digital content. These include everything from website blogs to social media posts and ads. It supports all social media channels and offers insights on follower growth, website traffic, and post engagement.

Apart from analytics, the platform provides various features such as content creation, social media scheduling, reporting, and audience engagement tools. Metricool is a paid service but has different pricing plans depending on the features and social media accounts you want to handle.

9. MS Clarity

Microsoft Clarity is a behavioural analytics software that allows you to analyse how users interact with your website. It features dashboards, session replays, and heatmaps that highlight what areas of your website get the highest and least engagement.

Plus, it has features like dead clicks and rage clicks that simplify analysing issues that cause poor performance and confusion.

Clarity complements Google Analytics to help you optimise your website performance and content for better conversions and traffic. If you find an underperforming page in Google Analytics, you can use Clarity to learn about the page's design flaw.

10. Google Sheets

You can use Google Sheets easily if you know how to work with Microsoft Excel. Both are first spreadsheet apps and offer help with analytics, so they have a lot of overlapping features.

However, Google Sheets was designed with collaboration in mind. So, it makes it easy to collaborate in real-time, share worksheets, and control edit access compared to Excel. What's more, any changes made to a spreadsheet can be tracked by the revision history.

The tool basically offers the "lite" version of most Excel analysis features. So, it's better suited for new analysts who are just starting out with data analytics.

Final Words

The selection of the right data analytics tools not only shapes insights but also data-driven decision-making. Each tool discussed here provides unique features, ensuring data analysts are well-equipped to successfully navigate the data landscape.

If you're just starting out in your analyst journey, learn data analysis with Python and use tools like Google Sheets for basic analysis.

Once you've understood the basics, you can use Microsoft Excel, Google Analytics, and Looker Studio to dive deeper. Although Tableau and Microsoft BI are user-friendly, they're better suited for advanced analytics and business intelligence.

With that said, if you want to learn about website analytics, stick to Clarity, while for social media, opt for Metricool. Metabase is a beginner-friendly business analytics tool.

If you're interested in learning more about data analytics, GrowthJockey is here for you. We utilise robust analytics tools to turn your data into meaningful insights. With us, you're able to make better decisions and proactively adapt to industry changes.

FAQs

1. What is data visualisation?

It refers to presenting data and information using visual elements such as graphs, charts, or maps. This makes it easier for decision-makers to identify trends, understand relationships, and spot outliers in their data.

2. Do you need to learn programming to use data analytics tools?

Data analysts need a certain level of programming knowledge, but they don't need a highly advanced understanding. That said, many data analytics tools like Microsoft Excel, Power BI, Looker, and more can be used easily by those with little or no programming knowledge.

3. What is the future scope of data analytics?

The future scope of data analytics in India looks promising as technology advances and data becomes more easily accessible. It's also because of a surge in Internet users and the need for data-driven decision-making to lower operating costs and enhance returns.