ERP News


619 0


BIG DATA RESOURCES- Data is everywhere, even the water sector has collected reams of data for decades. It has been only within the last few years that agencies, utilities, consultants and vendors woken up to unlock the hidden potential of data resources to manage maintenance and predict water flow. The move to leverage digital information in the water sector has been drastic with systems, to pair it with historic datasets and additional sensor data for creating customized digital dashboards and applications for water agencies.

Historical information from data coming from water resources can be layered on real-time information for the workforce to take operational decisions on analytics which older workers over the years have relied on their gut to make.

Water providers have been using Microsoft Power BI, to analyse patterns from data sources pulled together including water usage, customer complaints, planned capital expenditures, copper and lead test results and infrastructure age over an interactive site offered by Microsoft Power BI. This system lets operators take a control over the water system from any device over a remote location.


Harnessing Predictive Analytics for Optimisation

Multiple types of data will power predictive analytics to alert operators to monitor water loss and manage problems and analyse condition-based maintenance with water quality all in real time. For example, water resource providers use predictive analytics, to monitor dissolved oxygen in water systems to automatically make adjustments based on weather, temperature and water flows. Pilot studies show that as much as 12% in operational costs can be saved by creating smart water utility solutions suited to water resources.

Predictive Analytics integrates data from several similar assets developing designs or operations to develop efficient systems. Interactive dashboards present this information in a manner that is easily understood and decoded for business intelligence.

Water has become a precious resource with its usage increased at more than twice the rate of the growth of population witnessed in the last century. This is putting cities, water companies and utilities with new challenges to provide a high-quality supply of drinking water while keeping energy use and costs incurred to minimum levels. Recent infrastructure additions like automated meter infrastructure (AMI) have the potential to measure water consumption and provide highly accurate readings. However, employing advanced analytics on the collected data will leverage an additional layer of insight, which will assist both customers and water utilities to gain control of the water network for an effective management of valuable water resources more effectively.


Managing Data Explosion

Big data and analytics technology provides an insight to consumers to harness data explosion coming which arises from multiple resources including data collected from utility meter readings and sensors.

Deploying predictive machine learning algorithms enables utilities and water companies to benefit from early indications of fault detection and abnormal consumption to be well informed beforehand of any leak or water waste, for optimized customer interactions. Giants like IBM have devised algorithms that are based on data mining, machine learning and statistical analysis techniques to study historical patterns, to predict current seasonal demand for consumption of a family, a neighborhood in micro levels and of a city in a macro analysis.

Developed by IBM scientists in Israel, the smart water analytics solutions find and identify problems and patterns through data points and successively differentiate between the excessive use of water and leaks that could result in millions of gallons of water being wasted. Predictive Analytics also provides water utilities with intelligent insights that help resource managers to identify when low or no water use signals a problem.

Read More Here

Article Credit: Analytics Insight

Leave A Reply

Your email address will not be published.