Data science platforms are on the rise and IBM is leading the way
It’s widely understood that organizations that embed analytics and data science into their operating models bring actionable knowledge into every business decision. Now these same businesses are discovering that a centralized, integrated platform for doing data science work can provide them with a more effective analytics strategy and even offer competitive advantage.
What is a data science platform?
The 2017 Gartner Magic Quadrant for Data Science Platforms defines a data science platform as: “A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solutions, and for incorporating those solutions into business processes, surrounding infrastructure and products.”
This new report, previously known as the Magic Quadrant for Advanced Analytics Platforms, evaluates 16 vendors on their completeness of vision and ability to execute. IBM has emerged as the vendor furthest in vision and highest in execution.
How data science platforms provide value
As organizations move from descriptive analytics to predictive analyticsand prescriptive approaches, they are embracing leading open source technologies such as R, Python and Apache Spark, as well as machine learning and streaming analytics. However, the short supply of people with data science skills is driving many businesses to consider tools that are accessible to non-technical users.
By centralizing the tools and processes needed to integrate and explore all types of data; develop and deploy advanced analytics model; and streamline communication and collaboration, organizations can make data science more accessible to more users. A data science platform can actually give organizations an edge on the competition by enabling both business experts and technical experts to collaborate on solutions to costly problems such as demand forecasting, customer propensity to buy or churn, fraud detection and prevention, and more.
We understand that to be successful, data scientists need an environment that is open, engaging, and fosters collaboration. To that end, the IBM data science portfolio IBM data science portfolio helps advance the practice of data science by providing:
- Access to the open source technologies data scientists know and love
- Enterprise-grade functionality for critical data science projects
- A community that supports them throughout the whole process
- Support for a broad range of data types, including unstructured data *An integrated development environment built to encourage creativity and collaboration
Acknowledging that there are many different types of data science users, the IBM integrated portfolio addresses the broad range of skillsets and preferences. We have included capabilities that make it easier for non-data scientists to build and deploy models visually without coding, along with tools to help experienced data scientists and business users collaborate more effectively and work more productively.
For example, IBM® SPSS® Modeler and IBM SPSS Statistics provide an intuitive, point-and-click experience for users who prefer not to write code, enabling them to easily create machine learning applications through an extensive library of open source extensions. Skilled programmers and coders can use the best of open source and IBM SPSS together for greater power and flexibility. And the IBM Data Science Experience enables data scientists to develop code using their favorite tools, and share projects via notebooks in a cloud-based environment designed to foster creativity and collaboration.
Download the 2017 Gartner Magic Quadrant for Data Science Platforms today to learn why IBM is named a leader in data science and to find out why data science, analytics, and machine learning are the engines of the future.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Originally published at datascience.ibm.com on March 16, 2017 by Christine O’Connor.