Objective This study introduces the Alzheimer's Disease Common Data Element Ontology for Clinical Trials (AD-CDO), a lightweight, semantically enriched ontology designed to represent and standardize key eligibility criteria concepts in Alzheimer's disease (AD) clinical trials. Materials and Methods We extracted high-frequency concepts from more than 1,500 AD clinical trials on ClinicalTrials.gov and organized them into seven semantic categories: Disease, Medication, Diagnostic Test, Procedure, Social Determinants of Health, Rating Criteria, and Fertility. Each concept was annotated with standard biomedical vocabularies, including the UMLS, OMOP Standardized Vocabularies, DrugBank, NDC, and NLM VSAC value sets. To balance coverage and manageability, we applied the Jenks Natural Breaks method to identify an optimal set of representative concepts. Results The optimized AD-CDO achieved over 63% coverage of extracted trial concepts while maintaining interpretability and compactness. The ontology effectively captured the most frequent and clinically meaningful entities used in AD eligibility criteria. We demonstrated AD-CDO's practical utility through two use cases: (a) an ontology-driven trial simulation system for formal modeling and virtual execution of clinical trials, and (b) an entity normalization task mapping raw clinical text to ontology-aligned terms, enabling consistency and integration with EHR data. Discussion AD-CDO bridges the gap between broad biomedical ontologies and task-specific trial modeling needs. It supports multiple downstream applications, including phenotyping algorithm development, cohort identification, and structured data integration. Conclusion By harmonizing essential eligibility entities and aligning them with standardized vocabularies, AD-CDO provides a versatile foundation for ontology-driven AD clinical trial research.

Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this study proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. With CDO, a large margin and a small overlap between normal and abnormal DDs are obtained, and the prediction reliability is boosted. Experiments on MVTec2D and MVTec3D show that CDO effectively mitigates the overgeneralization and achieves great anomaly localization performance with real-time computation efficiency. A real-world automotive plastic parts inspection application further demonstrates the capability of the proposed CDO. Code is available on https://github.com/caoyunkang/CDO.

A slide taken from one of the speakers at the CDO Summit in London illustrating business drivers and capabilities and how they related to the CDO job. Apparently, it is a new role born in a lighter form straight after the financial crisis springing from the need to have a central figure to deal with technology, regulation and reporting. Therefore, the CDO is basically the guy who acts as a liaison between the CTO(tech guy) and the CAO/Head of Data Science (data guy) and takes care of data quality and data management. Actually, its final goal is to guarantee that everyone can get access to the right data in virtually no time. In that sense, a CDO is the guy in charge of'democratizing data' within the company. It is not a static role, and it evolved from simply being a facilitator to being a data governor, with the tasks of defining data management policies and business priorities, shaping not only the data strategy, but also the frameworks, procedures, and tools.

My first task as a Chief Data Officer (CDO) is to implement a data strategy. Over the past 15 years, I've learned that an effective data strategy enables the enterprise's business strategy and is critical to elevate the role of a CDO from the backroom to the boardroom. A company's business strategy is its strategic vision to achieve its business goals. Data that can be managed, protected, and monetized effectively will provide insights into how to achieve those goals. A CDO works in collaboration with senior executives to steer a business to its strategic vision through a data strategy.

Artificial intelligence (AI) helped the Air Force automate scheduling for the C-17 Globemaster III transport aircraft and improve scheduling accuracy rates by 92% last year, according to Air Force Chief Data Officer Eileen Vidrine. This is just one example of successes and use cases the Air Force has had as it develops data management strategies that inch the Defense Department as a whole closer to artificial intelligence capacities and closer to its target Joint All-Domain Command and Control (JADC2) initiative. DOD's efforts in AI has only grown in recent years, including establishing its Joint Artificial Intelligence Center (JAIC) in 2018. Coupled with recent announcements, the department is gearing up for further changes to its AI landscape. The Office of the Secretary of Defense (OSD) said Wednesday it is establishing a new Chief Digital and Artificial Intelligence Officer (CDAO) role to streamline AI efforts throughout DOD.

Every year in December and January, NewVantage Partners (NVP) conducts a survey of data and technology executives in large companies primarily located in the U.S. Every year, we (the authors) collaborate in analyzing and interpreting the results. And every year, we wonder why the survey results suggest that certain aspects of the data environment aren't getting better faster, or why they sometimes even become worse. The executives are usually pretty bullish about technology but quite bearish regarding whether their organizations are becoming more data-driven. They also express concerns about the executive roles -- most frequently, the role of chief data officer (CDO) -- that are charged with making their company's data environment better. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations.

In order to compete in the new digital economy, businesses must become increasingly data-driven. Few executives would dispute this objective. Recent events, including the global outbreak of COVID-19, have underscored the critical importance of having reliable data to inform organizational decision-making. Yet companies continue to struggle to operate in a data-driven manner. Even though we are now decades into the age of competing with data, a 2020 NewVantage Partners survey of C-suite executives representing more than 70 Fortune 1000 companies found that only 37.8% of companies have created a data-driven organization.

This is a continuation of Carlos Rivero's interview with Ask the CIO: SLED Edition on June 25. Last week during my interview with Carlos Rivero, the Commonwealth of Virginia's first chief data officer (CDO), I opined that the growing establishment of state CDO positions across the country reminded me somewhat of the creation of the state chief information officer position back in the mid-1990s. It's gradually becoming commonplace as half the states now have a CDO. In addition, the position's placement within the state organizational hierarchy also continues to evolve with about half reporting to the state CIO while the other half are located within the states' "administration secretariat." Rivero's initial task as CDO likewise reminded me of the role that I and other state CIOs faced during the Y2K drill some two decades ago.

After the term "big data" became mainstream, the related concept of a "data lake" also gained currency, applying to repositories of information so large they were reminiscent of vast bodies of water. Not unlike the secret denizens of a mysterious Scottish loch, hidden in the murky depths or threading unobserved among the currents coursing through its farthest volumes, there could be elusive secrets of great value lurking undiscovered. However, according to 2016 Forrester data, nearly 75% of all data retained by an organization is never analyzed or used. I've witnessed this process firsthand, having worked in the data storage industry since 1990 -- at the first dedicated fileserver company (Auspex Systems), and then during the glory days (1995–2007) at NetApp before embarking on a series of startups and a stint at EMC (now Dell). Through it all, I saw compounding data growth on platforms and in circumstances that expanded at astonishing rates to keep up.

The following post was sponsored by Cisco. The opinions, thoughts and observations, however, are completely my own. Last month I wrote on the challenges placing pressure on the CIO and IT leaders to move from an operations-focus to an opportunity-focus. According to ZK Research, 78 percent of today's IT budget is spent on "running the business," leaving very little to invest in innovation. With the mounting pressure to innovate and drive business growth, is it doom and gloom for IT leaders trying to keep pace with moving targets and c-suite expectations?