MATLAB vs Alteryx

MATLAB is a computing and programming tool that combines the power of functions and algorithms with data integration, modeling and visualization for predictive business data analysis. Users perform complex computations on data sets that the platform ingests from a multitude of data sources to glean business-specific metrics. Citing online communities, all users who reviewed support said that the tool is accessible to beginners, while providing enough depth for advanced users, though some said that the coding syntax could be daunting for non-technical users initially. Around 92% of users who reviewed its analytical capabilities said that the platform provides a wide range of built-in packages to provide out-of-the-box data analysis solutions. With its minimal scripting, many users who discussed data processing said that they could simulate complex mathematical functions to visualize complex data models. Reviewing its functionality, many users said that its rich library and design makes it possible to write powerful programs easily. A majority of users who discussed its performance said that the platform consumes a lot of power and space and slows down when performing complex computations, possibly because updates, though frequent, do not include optimization for older features. Many users who reviewed the cost said that individual user licenses are expensive, and buying additional libraries adds to the cost since many of these have interlinking dependencies, though some users said that the platform provides value for money. In summary, MATLAB is a programming solution that leverages machine learning for data collection and complex computations for users to create data models and visualize enterprise metrics for predictive analysis.

Alteryx is a data science solution that leverages the power of AI and ML to blend, parse, transform and visualize big business data to promote self-serve analysis of business metrics. Many users who reviewed data analysis said that the tool performs statistical, spatial and predictive analysis in the same workflow. Most of the users who reviewed data processing said that, with a lightweight ETL tool, the platform has strong data manipulation and modeling efficiencies, though some users said that it can be tricky to use SQL queries. Citing integration with Power BI, Tableau and Python, most of the users said that the tool connects seamlessly to data from databases and files, apps, and third-party data sources, among others, to expand the reach of search-based and AI-driven analytics. Most of the users who discussed ease of use said that the tool is intuitive with drag-and-drop functionality and a well-designed interface, though some users said error handling can be challenging for automated workflows. Most of the users who reviewed support said that online communities are helpful in providing answers to queries. Citing automated workflows, many users said that the tool helps save time, though some users said that these can be overly complex and need improvement. On the flip side, many users who reviewed pricing said that its expensive licenses and add-ons are cost-prohibitive, and cost per core is high for enterprises looking to scale. A majority of users who reviewed its visualization capabilities said that they need to export data to visually stronger applications, such as Tableau or Power BI, to make the reports presentation-worthy. Citing slow runtimes when executing complex workflows, especially with large datasets, many users said that performance-wise, the solution is prone to infrequent crashes. Most of the users who discussed learning said that with documentation not being in sync with latest releases, training is a must to optimally use the tool. Overall, Alteryx is a data science tool that, with its low-code approach and strong data wrangling capabilities, makes the journey from data acquisition to data insights seamless and promotes data literacy across organizations, though it might be better suited for medium- to large-sized organizations.