2023’s Top 10 Analytics Tools

Introduction:

We live in the age of data. A single keystroke on the keyboard, searching items on Google, and the real-time data captured by the Apple watch can all lead to data generation. According to Forbes, each person generates around 1.7 megabytes of new data every single second of the day. We now store an estimated 44 trillion gigabytes of data every single year. That’s an immense amount of raw information that needs to be analyzed and transformed.

Here are a few other eye-opening big data-related statistics:

• 70% of the data in today’s world is user-generated (Baseline).
• The data analytics market is currently worth around $274 billion (SiliconANGLE).
• By 2026, data/analytics-related spending is expected to reach $3.4 trillion (IDC).
• The US accounts for 51% of the data/analytics solutions market (IDC).

As you can see, data analytics is here to stay. It’s not going anywhere. Business leaders and decision-makers must leverage the influx of data and achieve a competitive advantage.

Raw data is not particularly valuable, usable, or accessible. It offers little in the way of insight because it is unparsed, unsorted, and uncleansed. Businesses need to analyze and parse the information to truly leverage that incredible amount of information. Only then can it be used to drive decisions and actions.

Of course, you’ll discover a wealth of analytics tools available today, but they’re not all created equal. How do you tell the worthwhile options from those you should avoid? We’ve done the legwork for you. Below, you’ll find our top 10 tools for mastering data analytics in 2023.

Top 10 Analytics Tools of 2023

#1. SAS

SAS is a well-known, established analytics solution. At heart, it’s a command-driven software package widely used by companies across most industries for customized advanced statistical analysis and data visualization. SAS virtual analytics software offers visual analytics, as well, and it’s frequently used in applications as wide-ranging as weather forecasting and business intelligence. SAS also offers specific solutions tailored for fraud, IoT needs, marketing, and risk management.

For those interested in SAS, a free trial allows you to put it through its paces before you make a final decision. Note that SAS and Microsoft are working every day to improve the offering and best connectivity between SAS Viya and Microsoft’s Azure environment to bring the following to users: 

• Extend SAS models
• Deploy SAS and open-source models
• Seamlessly connect your SAS Viya and Microsoft environments

#2. Python

Python is a well-known, open-source programming language. It’s used for managing data and general data wrangling needs, and it’s one of the most powerful data analysis tools available. It offers quite a few built-in features to make data analysis easier. Given its flexibility, lightweight nature, and open-source format, it’s an excellent choice for working with complex data within almost any industry, as its ease of use and simple syntax make python easy to learn even if you don’t have a background in engineering. It’s the first choice for all ML Scientists. Want to learn Python? Don’t worry. You can learn it quickly with Rolai. 

You’ll discover plenty of other reasons to appreciate Python. For instance, it’s a simple syntax language, which makes it easy to learn. It’s also scalable and very flexible, which is why Python is often used by data scientists, ML scientists, statisticians, and mathematicians. Of course, the availability of existing libraries is also an advantage, saving you time in your development.

#3. FineReport

FineReport bills itself as “BI reporting and dashboard software.” It’s used by almost 20,000 businesses worldwide and offers options for managers, builders, and IT staff. One high point of this analytics tool is its ability to monitor trends and create customized dashboards that offer access to insights at a glance. It’s also powerful enough for data analytics professionals but easy to learn so that non-experts can make use of it, too.

Specific solutions are available for IoT, finance, and sales.

#4. Power BI

If you want a data analytics tool powered by a recognizable name, check out Power BI from Microsoft. This tool promises to help you “do more with less using an end-to-end BI platform to create a single source of truth, uncover more powerful insights, and translate them into impact.” You’ll find multiple products available, including Power BI Desktop, Pro, Premium, Mobile, Embedded, and Report Server, so it’s easy to create a customized solution to fit any needs, from Fortune 500 companies to new startups.

Strong points here include data visualization, live dashboards, and the ability to share your insights across the organization with the click of a button. Power BI makes it possible to turn insights into action and benefit from real-time analytics streaming. It also integrates with Excel, which makes it one of the most potent BI tools on the market.

#5. RapidMiner

If you’re looking for a software package specifically for data text mining and uncovering patterns within your data, RapidMiner could be just what you need. Note that Altair has now acquired RapidMiner, which amplifies the package’s already considerable capabilities. You’ll discover an artificial intelligence framework that offers flexible, scalable AI solutions, data modeling, validation, and automation. It provides AI assessment, deployment, and configuration, enablement and certification, co-development implementation, assesses and grow, and other functionalities. You can use Rapidminer Studio to enjoy the ease of use and a lightweight application that’s not tied to in-house servers.

RapidMiner is used in a wide range of industries, including manufacturing, financial services, healthcare, automotive, retail/eCommerce, insurance, life sciences, and energy. However, there is no free trial.

#6. Microsoft Excel

One of the world’s most widely used software tools, Microsoft Excel is a spreadsheet program that offers multiple uses for data science. One of those is as an analytics tool. While basic, it works well for many small and medium businesses. You can run basic queries and pivot tables, create charts and graphs, and it offers features like acquiring data, data analysis, data visualization, and data publishing. Excel also learns your patterns and helps you organize your data to save time. New chart and graph styles help you display your data, and it connects easily with Power BI, allowing you to build analog spreadsheets digitally with a camera click.

If you dig beneath the surface, you’ll discover even more capabilities. Plus, because it’s familiar to many people, the learning curve is less than what you’ll find with some other data analysis tools out there.

#7. Tableau

Self-described as “the world’s leading analytics platform,” Tableau brings a lot to the table (including a free trial). It’s versatile and powerful, allowing you to take data from virtually any system and transform it, surfacing critical insights easily and quickly. Tableau excels at creating a data culture across your organization and changing how teams approach it by turning data into actionable insights. Note that Salesforce acquired Tableau in 2019, and it now dovetails with Salesforce’s already significant capabilities.

Not only is Tableau a data analytics suite, but it also offers business intelligence capabilities. You can visualize, analyze, and dig deep to understand your data, simplify raw data into worksheets, maps, and charts, and make it available to anyone who needs it. It also connects with spreadsheets, databases, data on Hadoop, and more.

#8. R

For businesses on a budget, and those that don’t want to be tied to a particular platform, there is R. This is an open-source solution for statistical computing, like statistical modeling, visualization, and analysis. While it’s best suited for exploratory data analysis (EDA), it also works and assists with various other needs, including data manipulation with packages like plyr, dplyr, and tidyr. R includes:

• Data handling and storage capabilities
• Collection of operators
• Collection of intermediate tools
• Graphical facilities for data analysis
• User-defined functionalities
• Perfect output results

R operates on all three major computing platforms, including UNIX, Windows, and macOS. It’s open-source and easy to use.

#9. Apache Spark

Described as “a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters, ” Apache Spark is a powerful suite of tools suitable for enterprise-level organizations and SMBs. It is a unified analytics engine for large-scale data processing – an optimized engine that supports the execution of graphs. Over 80% of Fortune 500 companies use it today (Apache).

It’s used primarily for big data and machine learning today, and this high-performance tool works for batch and streaming data. From adaptive query execution to ANSI SQL and handling both structured and unstructured data, Apache Spark is a multifunctional tool that delivers plenty of capabilities. It can also operate with Scala, Python, R, and SQL while providing access to complex data sources. Because it integrates so well with other programs, optimizing your entire technology stack and doing more with your data is easy.

#10. SQL

Finally, we have SQL, or Structured Language Query, no part of Microsoft Azure. MS promises that SQL is a tool that allows you to “migrate, modernize, and innovate on the modern SQL family of cloud databases” while enjoying a consistent, unified experience across your technology stack.

It’s designed to allow access to and manipulation across databases and allows you to retrieve data analyses with ease. Data audits are also easier to conduct in SQL than using a spreadsheet tool like Excel. SQL is used to communicate with data, and its primary function is to create, modify, and read. Note that you will need to choose the right SQL tool for your needs, with options ranging from MySQL to Microsoft Access and PostgreSQL.

Leveraging Data to Make Informed Decisions: Data Analytics Tools

Data is the lifeblood of your business. However, you need more than just access to raw, unparsed information. There’s good news if you’re drowning in a sea of information that feels useless or overwhelming. Data analytics tools are widely accessible, and they’re increasingly easy to use. Many have a very low learning curve and some, like Excel, have considerable familiarity given their established use in office settings.

With the right data analytics tool, it’s possible to make informed decisions, identify audience segments, refine product offerings, and out-compete others within your industry. However, it goes beyond simply running a profitable business. Analytics is shaping the world and informing businesses, nonprofit organizations, and even government agency actions.

As we create more data every day, you can expect data analytics to continue evolving and growing. New uses for data are being discovered daily, and new tools are being developed to harness that information, surface insights, and uncover patterns. Data analytics has incredible implications for virtually every area of human life, from recommending products to customers to uncovering the causes of natural processes in the world around us.

In terms of the immediate and longer-term future, there are some pretty defined trends for data analytics. More and more, you can expect these tools to focus on data freshness. Ultimately, the goal is to enable real-time analysis and insight surfacing, which would enable drastically improved decision-making with no lag.

It’s an exciting time, certainly. What is your favorite analytics tool? Let us know in the comments below!