Data Analyst Tools

We don’t need to tell you that data analysis is big business. From tech companies to government to universities and even hospitals—companies and institutions are collecting, storing and analyzing data like never before. Data analysis has become an indispensable expertise in the modern business world.

As a result, demand for qualified data analysts is growing by the day. It’s no easy job, but luckily, data analysts have plenty of options to choose from when it comes to specialized tools designed to make their work easier. With a little know-how, choosing the right tools to suit your business or industry can make the responsibilities of data analysis seem that much less daunting.

We'll take you through the key factors to consider before you select the data analysis tools to suit your career goals. We’ve also compiled a list of some of the most popular and efficient tools currently at your disposal.

Preparing for Data Analysis

In order to select the right tools for the type of data analysis you’ll be doing, you have to have a few important things in order first:

• You’ll need to have a clear understanding of the nature of data that will be examined.
• You’ll also need to be across the data integration needs of your organization.
• Your data sources should be located in a central data warehouse, so that they can be sourced easily from one location for analytical purposes.
• You’ll need a clear picture of the data governance and security provisions.
• If you happen to share the data between various teams at work, then you should also control access and permissions to protect that data.

Selecting the Right Tools for Data Analysis

Once you have centralized your data, you can get down to the business of analyzing it for business insights, which you will then use to plan strategies that will help grow your business. But how do you know which one fits your business to a T?

First, you’ll need to develop a list of your business needs and gauge the technical know-how of the staff who intend to use these tools. Is your data being dealt with by highly-trained data scientists and analysts, or by employees with limited technical knowledge? Or both?

If your team is less experienced, then a simple, intuitive interface would be a wise choice. If they’re at the other end of the spectrum, then you could make use of a platform that has provisions for an interactive interface for making iterations on code. You should also ascertain if the visualization tools of the platform satisfy your organization's needs.

The modeling abilities of the tool also need to be analyzed. Some platforms are advanced enough to carry out data modeling automatically. If that doesn’t fit the bill, and you insist on using homegrown modeling, it'll have to be a platform that uses SQL to model your data before you analyze it.

Last but not the least, the pricing and licensing fee of the platform needs to be researched. Most of the options available are free, while some choose to charge a fee or subscription. And just because a platform requires you to pay doesn’t mean that it’s the best option—some of the free tools pack quite a substantial punch.

The Tools Data Analysts Swear By

Let’s take a look at some of the most powerful and user friendly data analysis tools currently available.

• Microsoft Power BI: Microsoft Power BI is one of the best business insight platforms for data analysts. It can support a broad range of data sources, and allows you to easily group assorted reports and dashboards into an app for easier distribution. It also syncs up well with Azure Machine Learning and even accommodates machine learning models.
• Sisense: Sisense is a platform that aids in the processing and visualization of almost all your business data. It's quite popular among more technical developers and data analysts who are no doubt enamored by the arsenal of drag-and-drop tools and dashboards that facilitate interaction and collaboration. The software In-Chip technology utilizes CPU caching instead of a slower RAM, which optimizes computation to speeds up to 100 times faster than usual.
• SAP BusinessObjects: SAP BusinessObjects provides a range of business insight functions to sift through your data. Whether it’s reporting, discovery, or complicated analysis like self-service predictive analysis of any sort, they have a solution for you. It also couples well with the Microsoft Office suite, so you can quickly transition between the two at your convenience.
• Jupyter Notebook: Jupyter Notebook is one of the most versatile business insights platforms out there, as it supports up to 40 programming languages and allows you to create reports from live code. The best part is that it’s a free, open-source application that works on your desktop or even the browser window once installed using the Anaconda platform or Python’s package manager. Originally, it was programmed using Python, but its huge developer community has now been using other languages as well.
• Thoughtspot: Thoughtspot is another intelligent data analytics platform that has a range of reports and allows for natural language searches to help you explore and discover insights in your business data. It has an AI system known as SpotIQ that will automatically unearth insights and patterns that wouldn’t easily come to light otherwise. It can also help to break data silos by allowing you to automatically join tables sourced from different data banks.
• TIBCO Spotfire: TIBCO Spotfire is great for its intuitive range of point-and-click tools that help in building predictive analytical models. They’ve done well to provide a natural language search and easy to use AI-based data insight tools. It’s also a great visualization aid, as it can help you get your reports to work on both mobile and desktop applications.
• SAS Business Intelligence: SAS Business Intelligence empowers your collaborative efforts by helping you make your business insights available on mobile applications. It also provides a range of self-service analysis tools to help you glean business insights. It can be quite versatile. The only downside is the fact that it’s quite expensive compared to most of its competitors.
• Tableau: Tableau is a stellar business intelligence platform that is well known for its visualization abilities and the ease with which all your insightful reports can be shared on both desktop or mobile platforms. Though lacking in support when it comes to advanced SQL queries, Tableau makes up for it by translating visual components and drag-and-drop dashboards into efficient back-end queries, minimizing end-user performance optimization.
• Qlik: Qlik is a great business insight platform that is a pioneer in self-service data analysis. It can be utilized for data, both on the cloud or locally saved. It’s popular because it can be used by both tech professionals and beginners—it’s elementary to use and facilitates data exploration and discovery. The best part is that you can customize a broad range of charts with either embedded SQL or drag-and-drop modules.
• Excel: Trusty old Microsoft Excel is still one of the most widely used tools to manipulate data and glean insights. Since Microsoft has put so much research and development into it over the years, it’s unbeatable when it comes to manipulating spreadsheets, helping with a simple analysis, and analytic workflows. Unfortunately, it’s not much good for big data; it’s limited to around 1 million rows, and also fails when it comes to supporting versioning and collaboration.

Become A Data Analyst with Thinkful

If you're looking to get your foot in the big data door, try our online Data Analytics program: an accelerated online program of classes, mentorship, and professional guidance designed to get you a new career.

We also have an intensive Data Science course, is where you’ll learn the basics of Python, gathering and presenting data to find real insights from your data, learn SQL for querying, as well as how to design and run your own experiments to gather new data. You’ll also learn the basics of both supervised and unsupervised machine learning, covering everything from the classics to cutting-edge techniques. You’ll even get to know sci-kit learn, the most widely used modeling and machine learning package in Python.

Your financial status shouldn’t hold you back from a new career. Enroll now and you won’t see a tuition bill until you’re earning $40,000 a year. Our team is available now to answer any questions.