Subex Blog

Gartner listed Subex as a representative vendor for multi-persona Data Science and Machine Learning (DSML) platforms in its recently published ‘Market Guide for Multipersona Data Science and Machine Learning Platforms’ report.

The representative vendors were evaluated on technical parameters such as: data access, exploration, visualization, model development, and advanced analytics. Other aspects such as user interface modalities, collaboration, infrastructure, performance, and scalability were also considered.

About the report

The Market Guide is the latest report published by Gartner in the area of Data Science and Machine platforms, replacing the Magic Quadrant for the same category. The report highlights the rising relevance of data science and machine learning due to data democratization and looks into the rising prominence of AI and data science as organizations start executing their AI strategies. It provides an in-depth view of the DSML market, key recommendations for data and analytics leaders, and touches upon the questions addressed by Data Science and Machine Learning platforms. Key takeaways are:

• DSML platforms offer comprehensive analytics and business intelligence coverage through descriptive, prescriptive, and predictive insights.
• These are evolving into a multi-disciplinary approach by enabling meaningful collaboration between advanced data scientists, citizen data scientists, business leaders, and enterprise teams.
• Strong governance is needed, considering the prominent role Data Science and Machine Learning will play in automated decision-making.

The Significance of DSML platforms

The rate at which data is generated requires high computing and intelligent processing power to make sense of information at a speed that can deliver value to businesses. Right now, organizations use several siloed applications to peer into different datasets (that seem most relevant to the specific function) and get insights. However, the power of data lies in its gestalt, and this is why enterprises need a centralized and powerful platform that ingests diverse, unstructured data in an automated manner. Furthermore, as technology investments in 5G, IoT, AR/VR, etc., continue to grow, organizations turn to AI models to handle exploding data volumes. However, moving from data democratization to AI orchestration is a task typically done by advanced data scientists, who are in short supply.

Yet, AI and data science are in high demand. Gartner predicts that the AI and data science market will exceed US $10 billion by 2025. Early adopters of AI are already running pilot programs while those still in the planning phases want simpler implementation methods. Thus, the onus falls on Data Science and Machine Learning platforms to drive this growth.

Data Science and Machine Learning platforms, in their no-code automation way, allow business users with good digital understanding to double up as citizen data scientists and start using AI/ML models for business needs. AI-driven decision analytics coupled with strong orchestration makes AI accessible and scalable across business units and organizational levels. In a nutshell, Data Science and Machine Learning platforms help organizations keen on implementing AI to create a useable talent pool, demonstrate early wins, and scale and federate AI-led initiatives.

The underlying lever of Data Science and Machine Learning platforms is that they augment user support through data democratization. What sets such platforms apart is their ability to deliver and scale enterprise AI through well-governed, risk-proofed, and responsible AI/ML models powered by data science. They empower organizations by:

• Providing access to many user groups that may be skilled with digital technology and can now create models that use data science, analytics, and intelligence.
• Automating AI pipelines in a user-friendly and no-code way for improved productivity and efficiency.
• Accelerating time to value through pre-built use cases and models that are performant, scalable, and secure, thereby increasing adoption.

How to make better decisions with AI through HyperSense

HyperSense AI is a cloud-native and SaaS-based platform that democratizes and orchestrates AI across the entire data value chain. Through HyperSense AI, business users can easily unify data from disparate sources, automate tedious and complex data science processes, and convert data into insights through auto visualization. These insights can be translated across organizational hierarchies so leaders can make the best decisions for their teams based on real-time, reliable data.

With a host of pre-built use cases, HyperSense AI is composable and extremely reusable. The platform has in-built AutoML to automate many data science workflows and MLOps to foster impactful collaboration between enterprise teams.

The Gartner feature comes on the heels of Subex being named a representative vendor in Gartner’s Market Guide on AI in CSP Customer and Business Operations through HyperSense AI.