What Does Data Science Need to Be Successful?


There are certain advances that have revolutionized the tech world – personal computing, cell technology, and cloud computing are just some of them.
Now that we have the ability to store massive amounts of data in the cloud and then use it with advanced analytics, we can finally start working towards a machine learning future.
It’s time for data science to shine. Here are some stats:
61% of organizations identify machine learning as the most significant data initiative for the next year
There is $3.5 to 5.8 trillion in potential annual AI-derived business value across 19 industries
Businesses are seeing the potential too. Data science can have great impact in:
Building and enhancing products and services
Enabling new and more efficient operations and processes
Creating new channels and business models
But unfortunately, for many businesses much of that is still in the future. Despite making big investments in data science teams, many are still not seeing the value they expected. Why? 
Data scientists often face difficulty in working efficiently. There are lengthy waits for resources and data. There’s difficulty collaborating with teammates. And there can be long delays of days or weeks to deploy work.
The IT admins face issues too. They often feel a lot of pain because they’re responsible for supporting data science teams.
Developers have difficulty with access to usable machine learning. Business execs don’t see the full ROI. And there’s more.
A big part of the problem is that data science often happens in silos and isn’t well integrated with rest of the enterprise. There’s a movement to bring technologies, data scientists, and the business together to make enterprise data science truly successful. But to do that, you need a full platform. Here are some questions to think about:
What does this platform need? What defines success?
What do business execs need to be successful?
To tackle enterprise data science successfully, companies need a data science platform that addresses all of these issues. And that’s why Oracle is excited about our recent acquisition of DataScience.com.
DataScience.com creates one place for your data science tools, projects, and infrastructure. It organizes work, allowing users to easily access data and computing resources. It also enables users to execute end-to-end model development workflows.
Quite simply, it addresses the need to manage data science teams and projects while providing the flexibility to innovate.
What does this mean, exactly? It means you can now:

Make data science more self-service
Launch sessions instantly with self-service access to the compute, data, and packages you need to get to work quickly on any size analysis.
Collaborate more efficiently
Get more work done faster
Leverage the best of open source machine learning frameworks on a platform tightly integrated with high performance Oracle Cloud Infrastructure
Now Oracle can integrate big data and data science tools all in one place, with a single self-service interface that makes enterprise data science possible—there are more possibilities than ever now.[Source]-https://blogs.oracle.com/bigdata/data-science-successful


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