Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Globally, teams in risk, procurement, and compliance are under pressure to manage geopolitical risks and business risks. Third-party risks are impacted by the complexity of domestic and international businesses, as well as complex and diverse regulations. It is crucial that companies proactively manage third-party relationships. This cutting-edge platform, powered by D&B Data Cloud's 520M+ Global Business Records with 2B+ annual updates for third-party risks, is an AI-powered solution that mitigates and monitors counterparty risk on a continual basis. D&B Risk Analytics uses best-in class risk data, including alerts for high-risk purchases and match points of more than a billion. This helps to drive informed decisions. Intelligent workflows allow for quick and thorough screening. Receive alerts on key business indicators.
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Clindata Cloud
Clindata Cloud gathers pre-clinical, clinical, and Risk Metric data from various sources and provides the clinical operations teams with data sets that are ready for submission, along with analytics and alerts for risk-based monitoring. It integrates and harmonizes study data from different origins into a unified data model, ensuring that the incoming data is validated for factors such as completeness, accuracy, integrity, and consistency while also raising alerts for any anomalies or risk indicators. Data is standardized according to CDISC data standards to reduce noise and facilitate the creation of ready-to-submit data sets in real-time, allowing for ongoing validation and analysis. Additionally, it produces real-time analytics based on the standardized data, ensuring timely insights for clinical decision-making. This comprehensive approach enhances the efficiency and reliability of clinical operations.
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CluePoints
CluePoints offers a cloud-based platform that utilizes AI for risk-based quality management and oversight of clinical data, employing sophisticated techniques like machine learning and deep learning to enhance the reliability, precision, and safety of data and processes in clinical trials. This platform stands out with its capability for real-time anomaly detection and centralized statistical monitoring, effectively spotting outliers and data risks that conventional methods may overlook, thereby empowering teams to proactively address risks and expedite the resolution of issues while adhering to FDA, EMA, and ICH standards. Additionally, CluePoints features tailored solutions including Risk-Based Quality Management (RBQM) for timely risk identification, Medical & Safety Review (MSR) for efficient review and query management, Intelligent Medical Coding for automated clinical coding suggestions, and Intelligent Query Detection (IQD) to facilitate the detection of discrepancies, along with tools like the Site Profile & Oversight Tool (SPOT) designed for dynamic site monitoring to ensure optimal oversight throughout the trial process. These advanced features collectively contribute to improving the overall efficiency and effectiveness of clinical trials.
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