Big data is the zeitgeist of the 21st century. The sheer volume of data available to businesses, government agencies, educational institutions, and consumers is virtually limitless compared to the days when computers were the size of computer science labs.

The eruption of technological innovations over the last 30 years has demanded the constant creation and optimization of data analysis tools to ensure organizations can handle and derive meaning from data. Tools such as data science and machine learning platforms have arisen to meet this demand. 

According to a research study by DataRobot, 86% of businesses describe artificial intelligence (AI) and machine learning (ML) as a business priority, with 42% specifically stating it as their most important IT venture. Machine learning is maturing and is no longer a niche technology for innovative adopters. Therefore, organizations must determine their business needs and pain points surrounding data science and AI to make informed software decisions. A business can ensure that its data science tools are selected with enough information, research, and input from industry peers by using G2 reports that analyze user reviews in real time.

What to consider when buying data science software

R and Python are frequently cited as the most common programming languages for data scientists due to their intuitive nature and robust analytics capabilities. For example, a G2 reviewer of a data science and machine learning platform, said:

"Implementing data science or machine learning can be challenging using programming languages like R or python for a beginner.”

Although the popularity of R and Python are in part driven by them being open-source languages, it also raises concerns about data privacy. Therefore, buyers will likely have questions about a software’s ability to prevent hacks and maintain data integrity. 

If vendors are going to pay for a machine learning software, they will look for robust algorithms that come with the purchase. The bottom line of working with data is the efficiency and effectiveness of algorithms that help the machine learn and make predictions. Similarly, vendors must determine whether the tool and its algorithms can handle enormous data sets. With businesses now having millions (and even billions) of data points, the software must function seamlessly when processing copious amounts of information.

Finally, vendors are also considering the ease of use of data science and machine learning software. An intuitive interface that provides clean and attractive data processing tools is vital, and the ability to integrate with other popular programming tools and machine learning libraries will be influential in the software selection process.

Consider your data first

As the saying goes: garbage in, garbage out. There is a good reason this phrase is used so frequently. It is sage advice. To properly train an algorithm, one must have clean, prepared data. Once the data is squeaky clean, it must be imported into the data science platform. This step is not trivial. One should think about how well it can ingest and wrangle your data.

Things to think about:

• Does your data have inconsistencies?
• Is your data in different formats?
• How many platforms do you need to pull data from?

In our latest G2 Grid® Report for Data Science and Machine Learning Platforms (Winter 2022), we found that Alteryx ranked No. 1 for Data Ingestion and Wrangling.

Consider your talent

Data science talent does not come cheap. With the price for premium data science talent on the rise, no-code and low-code AI platforms are becoming increasingly more popular, allowing businesses to deploy AI without the need for data scientists.

However, the traditional data scientist role is here to stay. With that in mind, consider the makeup of your team and their strengths and weaknesses. For example, think about the programming languages they are proficient at and how that matches up with the platforms being evaluated. In the aforementioned report, reviewers were also asked to score these products based on their programming language support. TIMi Suite was ranked No. 1.

Don’t forget to consider your use case

When thinking about the tool that matches your needs, it is important to determine your needs and use case from the outset. 

What is the end goal of purchasing this software? Will you be using the platform for a computer vision use case, such as training an algorithm to detect issues on a product line? Will you be using it to optimize your advertising based on a customer’s previous browsing behavior?

After asking these questions, one can begin to consider a tool that is a good match. For example, when considering a tool for a computer vision use case, G2’s Grid® Report for Data Science and Machine Learning Platforms | Winter 2022 is a helpful tool. Below, we can see the top products based on their rating for computer vision capabilities.

Looking beyond the G2 Score

At G2, we collect different data points with each review that we receive. In the G2 Grid® Report for Data Science and Machine Learning Platforms (Winter 2022), MATLAB led the charts with the highest G2 score. However, reviewers were not fully satisfied with their product offering. Looking at the Meets Requirement score, they ranked No. 8, with TiMI Suite is coming in at No. 1. 

At G2, we’ve found the following to be some of the critical features when evaluating a data science and machine learning platform, along with the No. 1 ranked product for each given feature.

The G2 score is an important metric when evaluating leaders in a given category. However, if one wants to dig in and find a tool that fits their needs and use case, it is key to look at the feature ratings of the products. With this data in hand, a business can make a smart buying decision and find the right platform.