DataBuck
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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TRACTIAN
Tractian is the Industrial Copilot for maintenance and reliability, combining hardware and software solutions to monitor asset performance, manage industrial operations, and implement predictive maintenance strategies. Its AI-driven platform empowers businesses to prevent unplanned equipment downtime and boost production output. The company is headquartered in Atlanta, GA, and extends its presence globally with offices in Mexico City and Sao Paulo. Learn more at tractian.com.
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Concentio
The analysis of data from diverse IoT sources, such as sensors and devices, facilitates predictive and prescriptive insights that empower users to address potential anomalies in real time. Concentio® IoT Doctor effectively processes data from various IoT endpoints, notifying users of any faulty incoming data to ensure that issues are resolved before the data is utilized for further analytical purposes. Additionally, the Concentio® Production Line Fault Prediction tool leverages AI to conduct predictive assessments of production line components by analyzing IoT data, videos, and images. Meanwhile, Concentio® Optimal Asset Management scrutinizes incoming information from a network of utility service assets, allowing users to schedule timely maintenance and ultimately reduce capital expenditures by informing strategic asset replacement decisions. This comprehensive approach not only enhances operational efficiency but also significantly contributes to improved asset longevity and performance.
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Pulse
Pulse serves as a perpetual AI-driven business intelligence analyst that transforms disorganized and fragmented data into intuitive, interactive dashboards and insights through simple conversational queries. It integrates effortlessly with various data sources, including CSV, Excel, Google Sheets, Google Analytics, Shopify, and API endpoints, with plans to incorporate databases shortly. The platform automatically processes your data by ingesting, cleaning, structuring, and analyzing it without the need for manual intervention. Within moments, users can create tailored dashboards featuring charts, tables, and key performance indicators; pose follow-up inquiries regarding trends, anomalies, and overall performance; and apply filters for a more in-depth exploration of the data. Visual elements update in real-time as the underlying data evolves, while integrated anomaly detection identifies unexpected changes and automated insights keep you informed of fluctuating metrics. Additionally, everything is housed within a singular, easily queryable workspace, eliminating the hassle of managing multiple tools or tidying up spreadsheets, all fortified by robust enterprise-grade encryption, detailed access controls, and adherence to GDPR and CCPA regulations. This ensures that users can focus on deriving actionable insights without the distraction of technical complications.
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