International Institute for Analytics

Every so often a phrase emerges from the Word Cloud to achieve capital importance, the sort of thing that authors and pundits can dine out on years to come (well, we do have to eat too, you know). At present that phrase is digital transformation.

As we reported in Strata + Hadoop World 2017 – Big Data and Analytics Developments from the Heart of Silicon Valley, O’Reilly’s Strata Conference already has a heavy focus on machine learning and AI. What makes O’Reilly AI unique, versus Strata, is its exclusive focus on AI and the inclusion of more cutting-edge AI research topics that have huge potential, but are further from commercialization. The objective for this report is to summarize the common themes and key trends emphasized at O’Reilly AI into an easy-to-read guide that can serve as both a general reference and a resource for planning AI initiatives. With this in mind, the report is organized into seven sections.

When it comes to creating a more data-and-analytics-driven workforce, many companies make the mistake of conflating analytics training with data adoption. While training is indeed critical, having an adoption plan in place is even more essential.

Inquiry:

How can we approach structuring our digital transformation so they can get some real traction? We are a very analytically siloed organization, and struggle with data transparency/access across the enterprise. We are working to figure out how our analytics initiatives fit within the larger organization, and how to engage everyone, CIO and business units, in our digital transformation.

• How can we approach structuring our digital transformation in a way to get some real traction and change the culture?
• How can I best communicate why it’s necessary to directly align our analytics initiatives with the business?

The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.

Thanks to data analytics and machine learning, we are now discovering that the exact words teachers use to give students feedback is among several factors that directly influence whether a student succeeds or fails academically. And furthermore, whether she stays in school or drops out.

Inquiry:

We’re considering integrating Caboodle and would like to learn from other healthcare organizations using Caboodle if it is their core enterprise data warehouse (EDW), or if other solutions have been supplement to meet their needs.

• What role does Caboodle play in your overall enterprise data strategy?
• Does Caboodle operate as your core enterprise warehouse, or are supplemental solutions being used?
• What data are you collecting in Caboodle as opposed to supplemental systems?

As much as any industry today, healthcare sits at the intersection of both technological and societal change. Web, mobile, cloud, and data technologies are being applied to myriad patient-level applications to disrupt traditional patient care methods, and the very way that hospitals operate and compete. Emerging technologies leveraging the Internet of Things (IoT), particularly in the wearables category, will most certainly shift the role of care and wellness from provider to patient. Recent research from the International Institute for Analytics (IIA) has now quantified the significant gap in maturity between all healthcare segments and most other industries. But the research also reveals a discreet set of steps healthcare providers can follow to improve capabilities and move up the analytics maturity curve.

The ever-increasing role of technology in the modern marketplace has made transactions quicker and more convenient than ever for both businesses and consumers. Unfortunately, it’s also invited a greater risk of payment fraud and other cybercrime. Fraudsters have access to a variety of sophisticated attacks that can cause tremendous harm in a very short period of time – a reality that requires advanced tools capable of rapidly predicting, detecting and responding to suspicious activity and adapting to a constantly evolving digital landscape. Perhaps no such tool is more powerful than machine learning, and businesses are increasingly turning to this technology to guard themselves against cyberattacks.

Inquiry:

We are considering having our analysts use the same set of tools and are considering MicroStrategy because it is readily accessible. What should we take into consideration when selecting analytics tools to support the business?