DATA SCIENCE PLATFORM MARKET ANALYSIS

Market Concentration and Competitive Landscape

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Growing Volume of Data from Various Sources

With proliferation of smartphones, sensors, IoT devices and digital services, huge amount of data is now being generated on an unprecedented scale from a wide variety of sources. Business operations are becoming highly digital as organizations adopt technologies like cloud, mobile apps and digital processes. This continuous digitization of human activity and enterprise functions leads to growth in enterprise data over the past decade across structured, unstructured and multi-structured formats.

Rise of new data types like graphs, geospatial, genomic and other specialized forms of data that are being collected. Problems around data storage, processing and extracting useful insights are further compounded by the different velocities at which data streams in - from real-time streams to batch processing of archived data. The varied and massive volumes of data pose tremendous challenges for tech infrastructure and human capabilities when it comes to data management, analytics and science. Traditional tools are unable to handle these sizes, varieties and velocities of modern data ecosystems.

This has accelerated the need amongst organizations to invest in unified, scalable and collaborative data science platforms. Platforms that can ingest and process high volumes of data from multiple internal and external sources, and also support both streaming and historical analysis use cases. These provide the ability to glean knowledge from massive and diverse datasets much more effectively than isolated point tools. This boosts adoption of data science platforms as the primary infrastructure for data management as well as advanced, enterprise-grade data analytics at scale.