How AI and ML Can Drive Better Business Outcomes

Emerging technologies like artificial intelligence (AI) and machine learning (ML) are becoming a significant factor in everything from advanced data analytics to autonomous vehicles. These technologies are also helping organizations manage and optimize the massive amounts of data they take in daily. Every enterprise is dealing with massive corporate data stores, and they are realizing there is tremendous value contained within that data.

Advanced AI and ML applications have the potential to revolutionize the very nature of enterprise operations. AI and ML can help organizations make sense of massive amounts of structured and unstructured data, drive better business decisions and improve business outcomes.

“When you take [data analytics] further, it becomes full data science where you’re using ML. That’s where lot of companies are now. They’ve been collecting data for 10 years and mostly using it to answer questions they know,” says Myles Brown, Senior Cloud and DevOps Advisor for ExitCertified. “Now, there’s this whole discipline that says we don’t even know what we don’t know. We’re going to look for patterns and we’ll find new information we weren’t even looking for.”

Industry researcher McKinsey discovered 82 percent of enterprises adopting ML and AI experienced a financial return from those investments. It also revealed that 23 percent of North American enterprises have ML driving at least one function within the organization.

AI and ML are certainly driving this new level of data analytics for the purpose of improving business outcomes. Enterprises are generating unprecedented levels of value from their corporate data stores. “Now we have new techniques, and basically it’s a bet against the future. I don’t know what I am going to want to do with this in the future, so I’m going to hold onto it,” says Brown. “That’s where data analytics comes in. The best way to predict the future is to look at the past. We have all this data, now let’s look at what kinds of patterns we find.”

And since these analytics efforts are digging into massive amounts of data, many ML tools are cloud-based. Each major provider brings different strengths and different levels of complexity to ML. “Look at Amazon and Azure. They both say, ‘We’ll give you three layers of ML,’” says Brown. “‘We can help decipher it and make it easier to understand so you can still learn from your data.’ They’ve already trained the model.”

Although Google doesn’t hold the majority of the market share, it does bring strong analytics capabilities to the table. “If you’re into ML or AI, it’s probably best to run on the Google cloud,” says Brown. “We’ve seen this again and again in organizations saying they’ve collected lot of data and they need to do something with it. And they often prefer Google’s analytics options.”

Making the best use of those data stores and fully deriving the value hidden within that data requires up-to-date training on the breadth of AI and ML technologies. These courses are here to help you get started:

• EMC: Advanced Methods in Data Science and Big Data Analytics
• From Data to Insights with Google Cloud
• Google Cloud Big Data and Machine Learning Fundamentals
• Introduction to Machine Learning Models using IBM SPSS Modeler
• Machine Learning with TensorFlow on Google Cloud
• SAS AI and Machine Learning
• SAP: Fundamentals of Data Science
• Using Oracle Machine Learning with Autonomous Database