CI leaders will shift 10% of their budgets to emotion analytics. Emotions are a more important driver of consumer decisions than rational thought and thus are the largest factor in brand energy, customer experience, and marketing effectiveness. But for the past decade, CI professionals have leaned into the precision of big data analytics instead of the traditionally unquantifiable territory of emotion. New techniques change this dynamic: AI-based text analytics tools such as Clarabridge and IBM Watson improve the precision of cruder sentiment analysis tools, while firms such as Nielsen and Realeyes bring biometric and facial analysis methodologies from the lab to the business world. As data analytics becomes commoditized, firms will shift 10% of the insights budget to emotion analytics to pilot new techniques in search of competitive advantage in the "why" behind consumer behavior, not just the "what" that data analytics addresses. Companies will reorganize to ensure CX and CI collaboration.
Artificial intelligence and predictive analytics hold the promise of tackling the data burden and keeping risk predictions agile to external trends -- but they need to be applied strategically to present real value and minimize business risk. Historically, risk analysts have been able to make sense of complex yet structured data. Although nothing in these methodologies are broken per se, the way we use data is transforming irreversibly. For a business discipline that should offer exactly the opposite, it's clear that the standard practice needs to be adjusted to keep predictive analytics accurate. With the majority of all business services and consumer activity now taking place digitally, data is produced in vast, unprecedented volumes that are virtually impossible to neatly organize into structured, linear data sets for interrogation.
In addition to this, the recent'Big Bang' in large datasets across companies, organisation, and government departments has resulted in a large uptake in data mining techniques. So, what is data mining? Simply put, it's the process of discovering trends and insights in high-dimensionality datasets (those with thousands of columns). On the one hand, the high-dimensionality datasets have enabled organisations to solve complex, real-world problems, such as reducing cancer patient waiting time, predicting protein structure associated with COVID-19, and analysing MEG brain imaging scans. However, on the other hand, large datasets can sometimes contain columns with poor-quality data, which can lower the performance of the model -- more isn't always better.
AI-powered technologies have transformed the way organizations do business with the trading partner network. In fact, these solutions have powered up digital transformation across different industries including, retail, manufacturing, financial services, healthcare, and more. Not only artificial intelligence has made a powerful impacted business but also the life of humans. In other words, it has turned out to be pervasive in improving the quality of life. Suffice to say, it plays an important role in unlocking new therapies in life sciences, minimizing risks of fraud in business and delivering personalized customer experiences (CXs).
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Have you ever found a new favourite series on Netflix, picked up groceries curbside at Walmart, or paid for something using Square? That's the power of data in motion in action--giving organisations instant access to the massive amounts of data that is constantly flowing throughout their business. Our cloud-native offering is designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organisation. With Confluent, organisations can create a central nervous system to innovate and win in a digital-first world.
Much has been made about the potential for artificial intelligence to transform the healthcare industry, and for good reason. Sophisticated AI platforms are fueled by data, and healthcare organizations have that in abundance. So why has the industry lagged behind others in terms of AI adoption? All of them, however, will undoubtedly highlight one obstacle in particular: large amounts of unstructured data. Unstructured data is everywhere in clinical settings, often taking the form of nurses' notes, physician transcripts, and other patient information that's usually stored in silos across multiple organizations.
Sharecare is a digital health company that offers an artificial intelligence-powered mobile app for consumers. But it has a strong viewpoint on AI and how it is used. Sharecare believes that while other companies use augmented analytics and AI to understand data with business intelligence tools, they are missing out on the benefits of data fluency and federated AI. By using federated AI and data fluency, Sharecare says it digs deeper to find hidden similarities in the data that business intelligence tools would not be able to detect in health settings. To gain a deeper understanding of data fluency and federated AI,Healthcare IT News sat down with Akshay Sharma, executive vice president of artificial intelligence at Sharecare, for an in-depth interview. Q: What exactly is federated AI, and how is it different from any other form of AI? A: Federated AI, or federated learning, guarantees that the user's data stays on the device.
One of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics, supporting organizations as they work to uncover unique datasets to find novel associations and signals. By supporting greater interoperability and collaboration, data scientists, developers, clinicians and pharmaceutical partners have the opportunity to leverage machine learning to reduce the time it takes to move from insight to discovery, ultimately leading to the right patients receiving the right care, with the right therapeutic at the right time. To get a better understanding of challenges surrounding precision medicine and its future, Healthcare IT News sat down with Taha Kass-Hout, director of machine learning at AWS. Q: You've said that one of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics. Please explain this challenge in detail. A: One of the challenges in developing and utilizing storage, analytics and interpretive methods is the sheer volume of biomedical data that needs to be transformed that often resides on multiple systems and in multiple formats.
The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.
Digital transformation is bringing the world closer and is immensely responsible for driving all activities not only in enterprises but also in the healthcare industry, government sectors and much more. With the rise in competitive pressure, companies are being forced to reduce their overall costs while implementing diverse innovative technologies to be more responsive to customers and competitors. Hence, companies are increasingly taking advantage of technologies like cloud technology, artificial intelligence and predictive analytics to generate better customer value through connected applications, data, and services that optimize for agility and economics. With the cloud as a platform and APIs as building blocks for intelligent enterprise applications, AI is available for more people and organizations than ever before. This opens up more possibilities for AI technologies that can give companies a contentious advantage.