Predictive maintenance
Gather sensor data from your machines, aggregate data, push intelligence at the edge and perform predictions based on historical data in a central location while complying with increasing regulatory compliances.
Predictive maintenance can help improve business operations and reduce overall costs while increasing the OEE of all machines in a factory, fleet, production line. Some of the most important improvement points are:
Decrease time-to-value, be agile and obey regulatory compliances
With Lentiq, each disparate factory, production site, fleet operation site within a larger organization can process data closer to the source, promote local data science talent by pushing part of the analysis closer to the edge. The central data team can create machine specific models, define best practices in data preprocessing, push models to the edge to be perform predictions on local data. Model retraining can also be performed in the central/remote location using relevant historical information.
A decentralized data lake is the best approach for the IoT industry enabling a microservices-oriented approach that decouples the data processing logic from the computing resource management. Centralized data lakes add an extra overhead to the data collection process as well as analytics, resulting in being extremely expensive and inefficient.
Our platform provides as a service resources, application, data and metadata, notebooks management and helps data teams be independent and focus on data analysis and insight extraction closer to where the data is generated, while also allowing different data pools to communicate data between them. Lentiq empowers users to:
Understand data and perform exploratory analysis
MClean, transform, validate and prepare data for analytics and model training
Benchmark multiple predictive machine learning techniques and tweak the best scoring model
Generate reports, alerts, build data-driven dashboards that can support the decision-making and improve business processes.
