In this contributed article, Kirti Dewan, VP of Marketing of Fiddler AI, discusses how a central problem with AI remains the same: a lack of model transparency. Many of the AI models businesses deploy today are considered to be opaque, which often leads to issues being detected after decisions have been made and people have already been affected

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

In this special guest feature, Peter Bernard, CEO, Datagration, discusses how oil and gas industry predictive maintenance doesn’t just provide an economic value, it also boosts safety by anticipating unpredicted failures amongst aging infrastructure in many rigs. By applying machine learning (ML) to pumps and compressors, operators can easily detect equipment failure before it happens, greatly reducing downtime and extending the lifespan of machinery. Maintenance strategies across upstream oil and gas require a smarter approach, and ML provides the answer.

In this contributed article, Vrinda Khurjekar – Sr. Director – AMER Business – Searce, believes that companies struggling to improve operational processes need to adopt an AI-driven culture to survive. However, they shouldn’t rush. Instead, they must dedicate time in the design and planning phase to create holistic approaches that will stand the test of time.

Our friends over at Cognyte have a second survey on data fusion and analytics – this time for chief investigators. In their last survey, the 2022 IT leaders in the Data Fusion/Analytics Domain, the company spoke to CIOs and IT executives about their challenges and investment priorities for data fusion.

In this contributed article, Michael Dulin, MD, PhD, Chief Medical Officer, Gray Matter Analytics, suggests that to improve health outcomes and lower cost, the U.S. healthcare system must abandon fee for service models and provide comprehensive, proactive value-based care (VBC). High-quality data and advanced analytics that produce actionable insights into patients’ medical and social needs are an essential building block for this transition. 

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

In this special guest feature, Gary M. Shiffman, PhD, Co-founder and CEO, Consilient, takes a look at Federated Machine Learning, the branch of machine learning that’s sure to be a revolution for FCC professionals by enabling collaboration while preserving privacy. After all, money launderers are humans and therefore display consistent patterns of behavior. Machine learning (ML) technology, at its core, detects patterns across big data.

In this contributed article, Jaidev Amrite, Head of Product for SparkCognition’s Natural Language AI, DeepNLP, shows how a common feature of all successful AI techniques is augmenting SME knowledge and intuition with AI which enables enterprises to focus on high-value decisions. By eschewing classical supervised modeling approaches popular in academia, these techniques focus on rapid utility by providing just-in-time intelligence at the SME’s fingertips.