# Gartner‘s 2020 Magic Quadrant for Data Science and Machine Learning Platforms – The Leaders, Visionaries, and Key Trends - Canonical: https://33rdsquare.com/gartners-2020-magic-quadrant-for-data-science-and-machine-learning-tools-check-out-the-new-leaders/ - Published: 2024-09-03 - Author: Jordan Brown - Categories: [Artificial Intelligence & Machine Learning & ChatGPT](https://33rdsquare.com/category/tech/ai/) --- ## Why the Gartner Magic Quadrant Matters for Data Science and Machine Learning For organizations looking to adopt or upgrade their data science and machine learning capabilities, the sheer number of platform options can be overwhelming. The market has exploded in recent years with a mix of established analytics companies, major cloud providers, hot startups, and open source tools all vying for market share. To help make sense of it all, the Gartner Magic Quadrant has become the go-to resource. Released every year, the influential report maps out the relative position of the leading commercial data science and machine learning platforms. Gartner evaluates each company on two key dimensions: 1. **Completeness of Vision** – Gartner analysts assess the vendor‘s view of how the market will evolve and their ability to shape or disrupt it with their strategy. 2. **Ability to Execute** – This dimension looks at the vendor‘s proven track record in delivering and supporting their platform for customers. Based on these criteria, Gartner places each company into one of four quadrants: - **Leaders** execute well against their current vision and are well positioned for tomorrow. - **Visionaries** understand where the market is going or have a vision for changing market rules, but do not yet execute well. - **Niche Players** focus successfully on a small segment, or are unfocused and do not out-innovate or outperform others. - **Challengers** execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction. With this context in mind, let‘s dive into the key insights from the 2020 Magic Quadrant for Data Science and Machine Learning Platforms. ## The Big Movers in 2020 – Welcome Alteryx and Dataiku to the Leaders Quadrant The headline story coming out of this year‘s report was the changing of the guard in the Leaders quadrant. Tech giants IBM and Microsoft remained entrenched as Leaders, but were joined by two fast-rising challengers – Alteryx and Dataiku. Alteryx made an impressive leap all the way from the Challengers quadrant in 2019 to become a Leader in 2020. The company provides an end-to-end platform for analytics and data science that makes it easier for analysts and citizen data scientists to build machine learning models and advanced analytics. Dataiku also made major strides, jumping from a Visionary last year to a Leader in the latest Magic Quadrant. Dataiku‘s platform focuses on collaborative data science and machine learning in enterprise environments. The ascension of Alteryx and Dataiku into the Leaders quadrant reflects the growing adoption and importance of data science platforms that cater to not just expert data scientists and machine learning engineers, but the wider business user and analyst community as well. Gartner predicts that by 2023, 50% of data scientist activities will be automated, increasing the productivity and speed of artificial intelligence and machine learning experiments. The other big mover in 2020 was Databricks, which leapt from a Visionary in 2019 to a Leader this year. Databricks has gained major traction with its unified analytics platform built around Apache Spark. It provides a collaborative workspace for data engineering, data science, and machine learning. ## A Closer Look at the Capabilities of the Leading Platforms While all of the Leaders in the Magic Quadrant have excellent capabilities, there are some key differences to consider when evaluating which platform is the best fit for your organization‘s data science and machine learning needs. ### SAS Viya SAS is one of the most established vendors in the analytics space, and their Viya platform reflects decades of experience working with large enterprises. Viya provides a visual interface for data prep, machine learning, model management, and deployment that is intuitive for business analysts. But it also offers APIs and support for programmatic access in languages like Python and R for more advanced users. One of SAS‘s key differentiators is its strength in data management and ability to handle massive datasets. Viya can seamlessly process data in any format across on-premises and cloud systems. SAS also has one of the most comprehensive set of algorithms for different machine learning techniques, from basic regression to deep learning and natural language processing. ### IBM Watson Studio IBM Watson Studio is part of the company‘s broader Watson AI platform. Watson Studio provides an integrated environment for data scientists, developers, and subject matter experts to collaboratively build, train, and deploy machine learning models. Some key capabilities of Watson Studio include: - AutoAI for automating data prep, model development, and feature engineering - Flexible hybrid cloud deployment options across IBM Cloud, AWS, Azure, Google Cloud, and private cloud - Collaboration features like shared projects and asset versioning - Deep learning tools like Neural Network Modeler and Watson Studio Deep Learning - Extensive open source integration, including native Jupyter notebooks Watson Studio is a good fit for organizations standardized on IBM‘s cloud and data platforms. But it also offers flexibility to integrate with other clouds and on-premises resources. The platform‘s collaboration features also make it well-suited for large enterprises with distributed teams of data scientists, analysts, and developers. ### Microsoft Azure Machine Learning Microsoft has become an increasingly powerful player in the data science and machine learning platform market. Azure Machine Learning is a cloud-based platform with end-to-end capabilities for model development, deployment, and management. Some of Azure Machine Learning‘s core features include: - Automated machine learning (AutoML) to simplify model development and tuning - Drag-and-drop visual interface for building machine learning pipelines - Support for popular open source tools and frameworks like PyTorch, TensorFlow, scikit-learn, and more - Integration with Azure Synapse Analytics and Azure Databricks for big data processing - DevOps capabilities for versioning, CI/CD, and monitoring of machine learning models One of Microsoft‘s key strengths is its comprehensive cloud ecosystem. For companies already using Azure for infrastructure, databases, and other cloud services, using Azure Machine Learning ensures seamless integration and reduces complexity. ### Google Cloud AI Platform Google has a vast suite of powerful pre-trained APIs as part of its Cloud AI Platform, as well as an end-to-end platform for developing custom machine learning models called AI Platform. Google Cloud AI and machine learning platform capabilities include: - AutoML for training high-accuracy models with minimal effort and expertise - Built-in tools for data labeling, feature engineering, and model evaluation - AI Hub for sharing ML pipelines and model artifacts across teams and GCP projects - Explainable AI tools for understanding model predictions - Integration with BigQuery, Dataproc, Dataflow, and other GCP big data services - Kubeflow-based pipeline tools for portable model training and serving Google‘s emphasis on open source frameworks and pre-built APIs sets it apart. The company has also made major investments in AI fairness, transparency, and security a key part of its platform strategy. ### Alteryx APA Platform As a Leader in the 2020 Magic Quadrant, Alteryx offers an end-to-end platform for self-service data science and machine learning. The company‘s Analytic Process Automation (APA) platform aims to empower citizen data scientists to leverage advanced analytics and machine learning alongside data analysts and business users. Key capabilities of the Alteryx APA platform include: - Intuitive drag-and-drop interface for building analytics workflows and machine learning pipelines - Automated insight generation and machine learning model recommendations - Support for text mining, image recognition, and more with computer vision and NLP tools - Code-free data prep and blending from multiple sources - Scalable in-database and in-memory processing for large datasets - Built-in scheduling and sharing of analytics workflows Alteryx makes it easy for business analysts to enhance their work with data science and machine learning. But it is also flexible and extensible for data science experts who want to layer in more advanced techniques and custom code. ### Dataiku DSS Dataiku is another Leader in Gartner‘s Magic Quadrant focused on empowering cross-functional teams to collaboratively build and deploy machine learning models. Dataiku Data Science Studio (DSS) is the company‘s end-to-end platform for analytics and AI. Core components and capabilities of DSS include: - Collaborative workflows for coders and non-coders to work in the same interface - AutoML and visual machine learning for jumpstarting model development - Centralized data catalog and repository for sharing and reusing datasets, code, and models - Pre-built integrations with hundreds of data sources and enterprise systems - Automated model deployment and monitoring across batch, streaming, and real-time use cases - Enterprise-grade security, governance, and MLOps features Dataiku has invested heavily in creating a unified platform designed for cross-team collaboration in large enterprise environments. DSS aims to break down the silos between data engineers, data scientists, analysts, and business users to accelerate machine learning projects. ## The Expanding Ecosystem of Data Science and Machine Learning Platforms While the Leaders quadrant captures a lot of the buzz, the overall data science and machine learning platform ecosystem is large and growing. In addition to Leaders, Gartner identifies Challengers, Visionaries, and Niche Players that are all carving out their place in the market. ![Bar chart showing data science and machine learning platform market share](https://33rdsquare.com/datasci-ml-market-share.png) _Figure 1: Data Science and Machine Learning Platform Market Share. Source: IDC, 2020_ According to IDC, the global data science and machine learning platforms market reached $3.93 billion in revenue in 2019, up 22.2% from the previous year. The market is expected to continue growing rapidly through 2024, driven by accelerating enterprise adoption of AI and machine learning. ### Key Trends Shaping the Future of Data Science and Machine Learning Platforms Looking ahead, there are several key technology and market trends I expect will shape data science and machine learning platforms in the coming years: 1. **Continued push towards automation and ease of use** – While expert data scientists are not going away, the emphasis on citizen data scientists and automated machine learning will only increase. Vendors that can streamline the most tedious parts of the machine learning workflow and make it accessible to a wider range of users will have an advantage. 2. **Shift from model building to model operationalization** – Developing a high-performing model is only one part of the equation. The bigger challenge for many organizations is deploying, monitoring, and governing models in production environments. Expect to see data science platforms build out their MLOps capabilities to support full lifecycle model management. 3. **Emergence of industry-specific solution accelerators** – As machine learning becomes more widely adopted, we‘ll see the rise of pre-built solutions for common industry use cases such as demand forecasting, predictive maintenance, fraud detection, and more. Data science platforms that can offer these accelerators will help customers speed up time-to-value. 4. **Importance of responsible and trustworthy AI development** – With the growing impact of AI on society, developing machine learning systems that are fair, ethical, and transparent is critical. Data science platforms will need to bake in capabilities for bias detection, model explainability, and audit trails to support responsible AI practices. 5. **Tighter integration between data engineering and data science** – Machine learning models are only as good as the data that feeds them. The lines between data engineering and data science will continue to blur, and the platforms that can natively support both will have an advantage. This is the thesis behind Databricks‘ vision for unified data analytics. ## The Bottom Line for Organizations Embarking on Data Science and Machine Learning Ultimately, the choice of data science and machine learning platform depends on a range of factors, including your team‘s skills, use cases, data infrastructure, and more. But regardless of which platform you choose, the key principles for success remain the same: - Invest in the right skills and organize cross-functional teams to combine domain knowledge with data science and engineering expertise - Start with a clear business problem and use case before jumping into model development - Put the right processes in place for responsible data collection, integration, and governance - Plan your model deployment and monitoring strategy upfront to avoid getting stuck in experimentation mode - Measure, iterate, and scale successful use cases to drive continuous business impact While the data science and machine learning platform market will continue to evolve at a rapid pace, one thing is clear – AI adoption is accelerating and organizations that invest now in building the right capabilities will gain a significant competitive advantage. --- Source: [Gartner‘s 2020 Magic Quadrant for Data Science and Machine Learning Platforms – The Leaders, Visionaries, and Key Trends](https://33rdsquare.com/gartners-2020-magic-quadrant-for-data-science-and-machine-learning-tools-check-out-the-new-leaders/)