More recently, key advancements in computing have opened the door to widespread adoption and have created a new category of products and cloud services – that 451 Research defines as data science platforms and tools – used to enable this advanced form of analytics. The essential difference between a data science platform and a data science tool is that the former includes a range of data management capabilities that are not present in the latter.
The emergence and rapid evolution of data science
has had a significant impact on the analytics sector in recent years. It is spawning a wave of activity, driving innovation in the use of analytics and the development of intelligent applications. It has also triggered a plethora of new analytics products and services for enterprises. Most companies are increasing their investment in data processing, analytics and machine-learning software with a desire to become more data-driven. The rise of the data-driven organization means that enterprises essentially want to gain fresh value from the data they have amassed. Data science is a key discipline to enable this – propelled by experimentation with the goal of generating new business insights and business opportunities from existing and new data sources.
This Technology & Business Insight report on data science is based on a combination of insights and data gathered through direct interviews with each of the vendors mentioned in the report (with a few exceptions) spanning a wide variety of vertical applications and our analysts' deep experience in the data analytics industry. The full report includes:
- An examination of what data science is and why it is significant
- An analysis of data science user personas and use cases
- A definition of Critical Pillars of data science
- An explanation behind the role of Open Source technology in data science