Turning its considerable expertise in analytics to the study of IT operations, IBM has assembled a software package to help system administrators better pinpoint potential problems and performance issues, using many of IBM's tools for business intelligence, machine learning and data mining. "We've applied the IBM investments in analytics to IT," said Matt Ellis, vice president of software development for performance management. The new product, called Predictive Insights, was designed to predict and detect anomalies in the IT environment. Since 2005, IBM has invested US$16 billion in boosting its analytics capabilities, both through acquisition and research and development. Now, the company is applying some of this expertise to helping administrators better manage IT operations.
The technologies are introducing new trends every day. And artificial technology (AI) is one of the emerging products, which already has made the enterprises excited. AI promises to be an effective way of performing routine tasks and can be applied to the various sectors of economies and businesses. Today many industries are adopting and depending on it for their mundane and advanced operations. Improving customer service: Nowadays enterprises are using AI to increase customer engagement and satisfaction.
Predictive advertising is yet another area of marketing that is evolving rapidly thanks to the massive strides in the strain of artificial intelligence called machine learning and wide access to large sets of digital data. In this installment of our MarTech Landscape series, we look at how predictive advertising works and how it's commonly applied. Predictive advertising is a subset of predictive analytics, also covered in our MarTech Landscape series. Predictive analytics uses machine learning to predict future outcomes based on behavioral patterns seen in historical data. Those predictions can be used for any number of purposes: understanding who is likely to pay off a loan, prioritizing leads most likely to close and so on.
Take a dive into any discussion about predictive analytics, and it is likely that you will find the terms machine learning and analytics interchanged regularly. It is understandable given that both are related, but they are not the same thing. However, Sam Underwood, VP of business strategy with Futurety, a data analytics and marketing agency, points out that in practical terms, the two should work together. The main difference between machine learning and data analytics depends on which direction you want to look -- forward or backward. Data analytics in its simplest form is looking back at what was done to find trends that may help you moving forward.
This is a great story about the collision of ecommerce, digital advertising, predictive analytics, and AI in a new digital battleground, the automation and optimization of advertising targeting and spend. Like so many interesting opportunities this new market starts with a pain point and an unmet need. In this case the lag in digital advertising spend, particularly in mobile devices. Mary Meeker in her 2016 Internet Trends report that we featured last week pointed out this trend. On the far right side you can clearly see that while viewers spent 25% of their time on mobile devices, that category represented only 12% of spend.