Beyond Hadoop and NoSQL: AI, Machine Learning, and Deep Learning
Watch this video to learn what modern data platforms allow organizations -- and the people within them -- to do that they couldn’t do before.
Data analysis has moved from static reporting to accessing various data sources by multiple users beyond domain experts and data scientists. In addition, organizations are developing or adopting data-driven applications with embedded analytics. Many of these operational applications rely on the technologies that support AI, such as machine learning and deep learning. In addition, rules and recommendation engines enable systems of engagement, such as customer support, that feature automated, data-driven decision-making, fundamentally changing the way people live and interact with technology.
With the emergence of modern data platforms, obviously, a lot of focus has been on the data platforms themselves, Hadoop and NoSQL. Really, though, fundamentally, the interesting thing is what you can do with that data and what that enables businesses to do that they couldn't do before.
The Expanding Field of Vision
And the way we think about that is actually in terms of sort of an expanding field of vision. So, you think about, traditionally, organizations obviously have been analyzing their data for many years, but they've done that based on data from their enterprise applications, and they've done that through IT professionals creating reports for business decision makers.
What has changed is that field of business expanded. We now see organizations taking data from multiple data sources - from log data, from the internet of things, from mobile applications, from chatbots and also enabling access to that from multiple users, so data scientists, business users, and also data driven applications.
And the way that's being enabled is through multiple approaches to analytics - so, artificial intelligence and machine learning.
AI, Machine Learning and Deep Learning
And it's worth noting perhaps the differences between AI, ML and deep learning. We see that AI is fundamentally about using systems that can think and act like humans. Within that machine learning is about algorithms that can learn and improve without necessarily being programmed to do so. And then deep learning is about mimicking the human brain by multi-layered neural networks.
If we look at the way in which organizations are taking advantage of these technologies, we see a lot of focus on systems of engagement. Think about a retail environment. Traditionally, a customer would have interacted with an employee within a retail store and get suggestions and advice. Today, in an online retail environment, all that has to be done by chatbots and digital assistants.
Now, what enables that is the systems of intelligence - so, rules engines, recommendations engines, machine learning, artificial intelligence and the automation of data-driven decision-making via operational applications. And it's this infusion of human and artificial intelligence into applications and services that promises to deliver and democratize pervasive intelligence that will actually fundamentally change the way in which we live, work, shop and communicate with each other.