Data Science Innovation in the Enterprise

An introduction to the business value of data science and artificial intelligence

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The Data Science Landscape

Today, more companies than ever before are growing data science teams and building open source-centric tech stacks in order to find measurable value in big data. Those that aren’t risk being left behind.

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50%

The percentage of leading organizations that are currently investing in machine learning as part of their digital transformation 1

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$26B to $39B

The amount of money companies invested in artificial intelligence in 2016 2

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650%

Increase in the number of available data scientist jobs since 2012 3

Today, more companies than ever before are growing data science teams and building open source-centric tech stacks in order to find measurable value in big data. Those that aren’t risk being left behind.

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What is Data Science?

While the definitions of big data terms are constantly changing, artificial intelligence and its subfields have always been an essential part of a data scientist's toolkit.

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Data Science

An interdisciplinary field that combines statistics with computer science concepts like machine learning and artificial intelligence to extract insights from big data. In business, these insights — whether delivered via autonomous integrated systems or in traditional reports — have the potential to fuel innovation and transform decision making.

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Artificial Intelligence

Systems or machines that mimic human intelligence. Often used interchangeably with its subfields, including machine learning and deep learning, artificial intelligence has become a catch-all term for applications that perform complex tasks that once required human input, such as chatting online with customers or playing chess.

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Machine Learning

A branch of artificial intelligence focused on building systems that “learn” — or improve performance — based on the data they consume. Because machine learning models can adapt without being explicitly programmed, they are ideal for quickly transforming large datasets into highly accurate predictions about future outcomes.

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Deep Learning

A machine learning technique loosely based on the structure of a biological nervous system. Data are passed through interconnected layers of “nodes,” each performing an operation before passing the result onto the next node. Typically used in applications that require complex deductions, including facial recognition.

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Where Data Science and Artificial Intelligence Intersect

The overlap between artificial intelligence and data science is a significant one. Nearly everything under the artificial intelligence umbrella can be actively leveraged today by a data scientist to deliver business value.

The only exceptions are applications that don’t require data to function and learn, which are mostly the stuff of science fiction.

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The overlap between artificial intelligence and data science is a significant one. Nearly everything under the artificial intelligence umbrella can be actively leveraged today by a data scientist to deliver business value.

The only exceptions are applications that don’t require data to function and learn, which are mostly the stuff of science fiction.

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Business Applications

Data science is changing the way many companies do business. Highly personalized customer experiences, operational efficiency, and improved employee productivity are just some of the benefits enterprises see when they deploy data science applications in production.

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Recommendation Engines

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Chatbots

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Lifetime Value Models

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Image Recognition

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Speech Recognition

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Fraud Detection

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Dynamic Pricing

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Customer Segmentation

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What the Future Holds

Now is the time for companies to act on plans to deploy data science applications. In the next decade, enterprises that fail to adopt intelligent technologies will experience a dulled competitive edge, among other problems.

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$13 trillion

The potential impact of artificial intelligence on the global economy by 2030 4

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20%

The percentage decrease in cash flow artificial intelligence non-adopters will experience by 2030 5

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72%

The percentage of executives who believe artificial intelligence will be the advantage of the future 6

Now is the time for companies to act on plans to deploy data science applications. In the next decade, enterprises that fail to adopt intelligent technologies will experience a dulled competitive edge, among other problems.

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