Guy, a recognized industry thought leader, is the president of SmartSense, IoT solutions for the enterprise. In recent years, artificial intelligence (AI) and prescriptive analytics have been woven into the fabric of business operations across multiple verticals, ranging from retail and grocery to healthcare, pharmaceutical and more. Today, there is high demand for continuous streams of data from the physical world. IoT's accurate sensing capabilities coupled with its capacity to generate vast data resources make it a natural partner to the disciplines of pragmatic AI and prescriptive analytics. The ability of these businesses to integrate these related disciplines has the potential to transform their operations and increase profit margins.

Summary: A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it's all the same thing. A little reminiscence, or for those of you who are only recently data scientists, a little history lesson. Our profession of finding the signal in the data, be that supervised or unsupervised got underway in the 90s. In the last 20 years we've been called by a variety of names. It's not at all clear that as those names changed that any clarity was added.

In the last few years, we have seen an unprecedented data detonation across global enterprises. Businesses have grown increasingly aware of the fact that hard data is the panacea. Data is helping enterprises make faster and more accurate decisions, by eliminating the biases and knowledge-gaps that have plagued enterprise decision-making for decades. This understanding and awareness has rapidly translated into major global corporations across industries giving huge importance and allocating massive investments for infrastructure that can help capture, store and leverage data resources. In the business landscape of today, we are seeing more and more processes getting systematized and robotized, environments and equipment getting more sensorized – with digital IT at the heart of it all.

Summary: True prescriptive analytics requires the use of real optimization techniques that very few applications actually use. Here's a refresher on optimization with examples of where and how they're best used. Predictive analytics and optimization have gone hand in hand since the very beginning. But in 2014 some erudite journal decided we needed another phrase for this combo and it became Prescriptive Analytics, theoretically differentiating what could happen (predictive) from what should happen (prescriptive) through the application of optimization. Originally I felt strongly that this was a distinction without a difference and only served to confuse our customers who were having a hard enough time five years ago understanding why they should even be doing predictive.

Artificial Intelligence mimics human beings, the human mind and the way the human mind learns through cognition, environmental observation and feedback. AI programs can perform all cognitive functions as thinking, rationalizing, reasoning, intuition, etc. It can include the whole gamut of what the human mind or human intelligence can be, all that it can do and more. In contrast to the typical android or humanoid AI robots, seen in science fiction, AI will find more use cases in machines, systems and software, used in all the industries. We are already witnessing the use of robots in manufacturing and also in predicting when machines will fail, need maintenance, repair, etc. Popular use-cases of AI range from supply chain management to sales, marketing and even automated intelligent customer service.

Click to learn more about author Kimberly Nevala. Artificial Intelligence (AI) provides exciting new opportunities for exploiting data and information. But is AI a revolution or natural evolution of existing BI/Analytics use cases? Certainly some of the core techniques that power AI (advanced Machine Learning and Deep Learning in particular) represent a fundamental shift in algorithmic programming. The application of AI, however, answers a need previous analytic generations could not.

The last decade has seen a tremendous proliferation of Big Data. And Big Data Analytics, that is inadvertently tied to powerful Machine Learning technology. Fundamentally, Big data is just large amounts of data -- large in terms of volume, variety, veracity as well as velocity. This makes Big Data especially complex for traditional analytical methods to master, decrypt and/or simplify. Machine Learning, on the other hand, is an advanced application of AI that automates analytical model building.

Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn't that what all analytics should be about? A hearty "yes" to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term "prescriptive" as the goal of all these analytics -- to make more effective decisions -- rather than a specific analytical technique.

I said, and I wrote, that machine learning and predictive analytics were almost the same. Of course, I also put optimization as the queen of all analytics technologies as it yields best business value. What else would you expect from someone who spent nearly 3 decades in working in optimization? No wonder this view became popular in the optimization community... First, let me reassure readers about my mental health: I still think that optimization is best for computing optimal decisions. I started thinking there was an issue when I met customers willing to use machine learning to solve all the business problems they have.

I said, and I wrote, that machine learning and predictive analytics were almost the same. Of course, I also put optimization as the queen of all analytics technologies as it yields best business value. What else would you expect from someone who spent nearly 3 decades in working in optimization? No wonder this view became popular in the optimization community... First, let me reassure readers about my mental health: I still think that optimization is best for computing optimal decisions. I started thinking there was an issue when I met customers willing to use machine learning to solve all the business problems they have.