IBM Data Science
IBM Data Science – Why Data Science Matters
The New Data Science Experience with AI
IBM Data Science – AI Analytics
IBM Data Science – Why Data Science Matter – IBM is addressing some of the most pressing issues for data scientists. In particular, the Data Science Experience platform allows for team participation on projects through collaboration, accelerates the access to data by providing clean datasets, and incorporates data visualization, which allows data scientists to more effectively share their insights.
A Collaborate Data Environment – Collaboration on data science projects is critical because they often involve a team of users to develop a complete analysis. This team can consist of data scientists, application developers, data engineers, and business people. The reason for the reliance on teams is largely because data science is a relatively new area of expertise and there are few people trained specifically to be data scientists. As a result, they primarily come from two main backgrounds; computer science or statistics. A data scientist is naturally better at one of these topics and may need some support on the other. For support on data management aspects, data engineers are often pulled into a project as well. In addition, projects almost always require input from business people to facilitate getting to the actual business need of the problem. With this in mind, IBM has created a platform that supports ongoing communication and collaboration. This will help data scientists to function and operate as more of a collective team, which will ultimately allow users to perform more advanced and accurate analyses by combining their areas of expertise.
Accelerated Data Access – Data scientists report that they often spend 70-80 percent of their time on data preparation and, being a high-priced employee, this is clearly not an effective use of their time. Some organizations tackle this by deciding to hire a data engineer to manage the data preparation aspect. However, not every organisation can afford an additional hire and data scientists often have to wear the data engineer hat!
Source: (Nucleus Research Report 2018)