International Institute for Analytics

While there are many advantages to the cloud, it is also necessary to use caution to make sure that the risks of the cloud are mitigated while pursuing the advantages.

As data science and analytics teams continue to feel pressure to deliver more value from analytics, many organizations still struggle with the processes and technology required to deploy models into production and more rapidly make data-driven decisions. When evaluating how to best undertake these activities, organizations should consider an important distinction to determine the best path forward.

Over the years, I’ve seen analytics professionals of all stripes blow their credibility and lessen their impact by falling into a common trap. I have to admit that I fell victim to the same trap early in my career. While our intentions are pure, our analytical minds and approaches can get the best of us and we explain too much. We’ll be better off if we learn to provide less detail and stop talking sooner than we are naturally inclined to.

The analytics landscape has matured, broadened, and totally transformed in recent years. This transformation has been driven by a wide range of factors including big data, the cloud, the increase in processing capability, the decrease in storage and processing costs, and the near-ubiquitous availability of algorithms. As a result, an organization must also totally transform the tools and processes it uses for analytics if it is to recognize its analytics potential.

With the hype surrounding Artificial Intelligence (AI) today, almost everyone in the analytics and data science space has been asked about AI by their business partners. Unfortunately, during these conversations it often becomes apparent that the business person really doesn’t have a clue what AI really is or what AI is best able to solve.

While open source software has been around for decades, its adoption level and breadth of focus have exploded in recent years. However, there is a potential downside to the open source explosion that may be heading our way. Is it possible that the open source movement will backfire on us? This blog will explore that possibility.

This is our 9th annual look forward into the upcoming year, and the annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. As in the past few years, we have augmented our predictions by also providing some specific priorities for leaders to focus on as they attempt to address each prediction. Therefore, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

Artificial Intelligence (AI) today merely has specific intelligence as opposed to generalized intelligence. This means that an AI process can appear quite intelligent within very specific bounds yet fall apart if the context in which the process was built is changed. In this blog I will discuss why adding an awareness of context into an AI process – and dealing with that context – may prove to be the hardest part of succeeding with AI. In fact, handling context may be the Achille’s heel of AI!

It goes without saying that the analytics and data science space is undergoing change at an unprecedented pace. At the same time, many organizations have a lot of people who are still working in the same way using the same skills they have had for years. Organizations certainly need to move beyond those legacy skillsets and the need for a skills update is real. However, I see many organizations assuming that those with legacy skillsets are no longer valuable and must be replaced. This is a mistake. There is a huge difference between a legacy skillset and a legacy mindset. The two are not the same!

Artificial Intelligence (AI) is one of the hottest topics in the analytics space today. Within a two- to three-year span, AI went from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell artificial intelligence a few years ago are now charging full steam ahead in pursuit of AI initiatives. In many ways, this is a good thing. After all, AI is quite powerful and has the ability to drive tremendous value if applied appropriately. However, this attention also has some negative consequences. Most notably, the topic of AI is so full of hype today that many organizations are struggling to separate what is real and achievable from what is pure hype and wishful thinking.