4.8 out of 5 stars
(6 Ratings)
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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4.6 out of 5 stars
(16 Ratings)
SKU Science delivers a fast and intuitive solution for sales forecasting and performance tracking. Implement your demand planning process in as little as two days! Created by seasoned experts, it’s specifically designed for operations managers, S&OP managers, supply chain professionals, and demand planners. With 644 statistical combinations, the platform generates highly accurate and tailored sales forecasts at any level. For even greater precision, AI models can be trained on your unique dataset. Automatically calculated KPIs highlight the most critical items, helping you focus on what matters most for your supply chain and business success. The platform’s operational dashboards refresh every cycle, ensuring efficient activity monitoring and data-driven decision-making. Combining advanced capabilities with ease of use, SKU Science is trusted by clients across manufacturing, food and beverage, healthcare, retail, and e-commerce sectors.
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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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OpenText for Life Sciences
OpenText™ Information Management solutions empower organizations in the life sciences sector to harness data and content insights, enhancing their decision-making and speeding up product development. These tools enable seamless integration, management, and secure sharing of information among individuals, systems, and devices. By utilizing information assets effectively from research and development through to commercialization, organizations can benefit from adaptable cloud-native software that operates in any environment. OpenText for life sciences significantly accelerates the discovery process, facilitating the extraction of actionable insights that foster innovation in the pipeline. Users can convert research papers into electronic lab notebooks through intelligent capture, and uncover valuable insights using text mining techniques. Moreover, the platform allows for the extraction of knowledge hidden within unstructured text of clinical trial reports, study protocols, and findings related to clinical safety and efficiency. It also offers methods to intelligently analyze, categorize, and extract information from clinical trial documents, ultimately minimizing the risk of expensive delays and interruptions in the development process. By effectively utilizing these advanced capabilities, life sciences organizations can significantly enhance their operational efficiency and drive forward their research initiatives.
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