AI Articles and Analysis about Data management

There are several companies claiming to offer AI solutions to healthcare companies, as we've explored extensively in our past reports. In this particular report, we focus on AI as it pertains to working with electronic health records (EHRs) and electronic medical records (EMRs). AI vendors offer solutions to hospitals and clinics with a variety of functions.

Episode Summary: This week, we're going to be talking about the defense sector. We interview Ryan Welsh, CEO of Kyndi, a company working on explainable AI. We focus specifically on the unique data challenges of the defense industry, as well as the general use case of AI in defense writ large. Many of the challenges that the defense sector has to deal with transfer to other spaces and sectors. Business leaders that deal with extremely disjointed text information, what is sometimes called "dark data," and information in various languages or different dialects, will be able to resonate with some of the unique challenges talked about in this episode, and maybe even gain some insights for how to handle them.

This article was originally written as part of an in-depth AI report sponsored by Iron Mountain, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Thought Leadership Services page.

International Data Corporation (IDC) reported in their  Worldwide Semiannual Big Data and Analytics Spending Guide that global investment in big data and business analytics (BDA) will grow from $130.1 billion in 2016 to more than $203 billion in 2020. In a previous report, we covered machine learning in the finance sector, and in this report, we dive deeper into big data solutions and data management platforms for financial institutions. The companies in this report all claim to help financial institutions with at least one of the following:

Government interest in AI has picked up in recent years, and many government officials are starting to ask the same questions business executives were asking two or three years ago. Governments and large NGOs are starting to invest in AI, spending budget and time on pilot programs for various AI applications and discussions with people in the field on the future implications of the technology.

When it comes to planning an AI initiative, a business will need to determine the method by which to acquire the data necessary to meet their objectives. Data is essential when it comes to succeeding with AI. An effective AI strategy is built on top of data that is specific to the business problem a company is trying to solve.

Modern AI and machine learning software require large sets of data in order to train its algorithms to make judgments, make predictions, and take actions. Data is a critical part of bringing artificial intelligence to life in different industry sectors. The applications we’ve highlighted below involve organizing historical and real-time data from existing businesses in the retail sector from which data scientists can build machine learning models.

Episode Summary: For business leaders who are thinking about integrating AI into their company or who are just in the very beginning of that journey, this may be a useful episode of the podcast.