Centralized Data Analytics Platform for Water Utilities
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Solution Overview
Problem
The water and wastewater industry faces challenges such as aging infrastructure, data management issues, and limited adoption of big data techniques, leading to technological stagnation, with only 17% of utilities fully implementing cloud technologies and nearly 50% not using data mining for better system understanding.
Innovation Solution
A centralized, robust, secure data analytics platform that consolidates discrete systems, utilizing IoT capabilities, predictive analytics, and machine learning to improve data processing and visualization, enabling real-time monitoring and predictive insights for decision-making and cost savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized data analytics platform consolidates discrete systems, then data quality and operational efficiency are improved, but device complexity increases
Solution Approach 1:
The patent consolidates multiple discrete data systems (SCADA, GIS, CMMS, billing systems) into a single centralized data analytics platform. This merging of previously separate systems improves data quality by enabling comprehensive data cleaning, integration, and analysis in one unified environment, while the modular architecture manages the inherent complexity through standardized components.
2Productivity
If cloud technologies and big data techniques are fully implemented, then predictive analytics capability is improved, but cost and implementation difficulty increase
Solution Approach 1:
The patent implements preliminary data processing and consolidation steps before applying advanced analytics. The system first ingests and standardizes data from multiple sources, then applies predictive analytics models. This staged approach enables utilities to gradually adopt cloud technologies and big data techniques, managing implementation complexity while building predictive analytics capability incrementally.
3Ease of operation
If data is consolidated from multiple silos into a centralized location, then data accessibility and analysis capability are improved, but data security risks increase
Solution Approach 1:
The centralized data analytics platform acts as a secure intermediary layer between various data sources and users. The system consolidates data from multiple silos into a controlled environment with implemented security measures, providing authorized access to cleaned and integrated data without exposing underlying systems. This mediator approach improves data accessibility while managing security risks through centralized control and protection mechanisms.
Data Source
AI summary
A system comprising: a centralized data acquisition subsystem to receive input data from one or more data silos; a data storage subsystem adapted to process, and store the received input data; a bridge application subsystem to securely ingest the received input data from the centralized data acquisition subsystem into the data storage subsystem; wherein the bridge application subsystem comprises a custom bridge program subscription; a data refining subsystem that is connected to the data storage subsystem and refines the input data acquired by the data storage subsystem, removes abnormal data and stores refined input data after refining; a predictive analytical subsystem is configured to generate output data using the refined input data from the data storage subsystem for processing and computing the input data to create a predictive analysis.