ACCELERATING ENTERPRISE GROWTH WITH DATA SCIENCE PLATFORM Posted on : Oct 17 - 2020

Collaboration between data scientists, IT, business analysts & developers drive organisation’s success

The focus on business outcomes has taken on a technological twist. Organisations relying on emerging trends in technology have a sole motive, ‘To drive the company towards growth.’ As the embrace of innovation continues, it takes a step further for advanced systems to be employed in routine work.

Earlier, it was okay for data scientists to get dragged into vague tasks or time-consuming experimentation with a variety of open-source tools in the name of innovation. The collaboration was often an afterthought or extremely difficult to achieve across the enterprise. Deployment of models in the enterprise was considered as a rarely achieved step. However, the table has turned today. Not accomplishing these tasks and acquiring a data science driven outcome has a greater cost of loss than it did previously. Henceforth, now is the best time to consider a data science platform for improving enterprises.

Focus of an enterprise data platform

Since the invasion of technology in the working landscape, data science, machine learning and AI has fragmented competitiveness in the field. Gartner defines a data science and machine learning platform as a cohesive software application that offers a mix of basic building blocks essential for creating many kinds of data science solutions and incorporating such solutions into business processes, surrounding infrastructure and products.

Remarkably, the primary users of data science and machine learning platform are people specialized in certain fields such as data scientists, data engineers, citizen data scientists and machine learning engineers. Data science platform works to minimize their job while bringing up the company’s revenue.

Here are some of the aims of data science platform,

• Data science platforms make data scientists more productive by aiding them to deliver models faster with less error.

• It makes the job easy for data scientists to work with larger volumes and varieties of data.

• These platforms deliver trusted and enterprise-grade AU that is bias-free, audible and reproducible. View More