Digital transformation is a very prevalent buzzword these days, but what exactly does it mean? According to George Westerman, a principal research scientist with the Sloan MIT Initiative on the Digital Economy, “Digital transformation is when companies use technology to radically change the performance or reach of an enterprise.”
Obtaining accurate data, as well as hiring and expanding data science teams to to leverage insights at the enterprise level, are crucial components of a digital transformation strategy, and this shows no sign of slowing down anytime soon. IDC predicts that revenue from the sales of big data and analytics will increase to more than $187 billion in 2019.
However, even with these insights in mind, many organizations struggle with implementing a digital transformation strategy. In fact, over a third of companies cite IT complexities as the biggest barrier to success. Fortunately, a data science platform can help.
Highlights from Data Science Platforms: Helping IT Drive Digital Transformation
To combat the issues surrounding data silos, many enterprises combine, at regular intervals, the data from each separate store into a data lake, which could include a Hadoop Distributed File System (HFDS), S3, or another Hadoop-compatible file system.
The use of containerization technologies, such as Docker, capture system dependencies in a lightweight, reproducible way that can be shared among teams.
In the DataScience.com Platform, data scientists can deploy a predictive model or code as an API, which eliminates the burden on IT and engineering on a per-project basis.
Using Docker swarm in the DataScience.com Platform allows IT to provision a pool of servers and configure the sizing options from which a data scientist can choose.
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