Best Practices for Building an Effective Water Analytics Strategy

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In January, KETOS CEO Meena Sankaran sat down with Gary Wong (OSIsoft) to discuss the water and analytics landscape for 2021. A few notable best practices emerged from their conversation that should be top of mind for water operators and managers across industries.

When building out a water analytics approach, be sure to consider the following.

Seek Autonomous Platforms

While many utilities and businesses still rely on forms of water monitoring that require human intervention. Leveraging technology that can autonomously test and pull data allows users to remove the human element and ensure that testing and monitoring happens at pre-determined and regular intervals.

Find Solutions That Manage Data From One Place

A recent OSIsoft survey of 200 utilities revealed that 61% had issues accessing their data. This should come as a surprise, as many water operators have data dispersed across various devices and in different formats. Implementing a device that can centralize and organize multiple data across devices and collection modes will help water managers access the data they need and act upon it.

Embrace Solutions That Offer Real-Time Monitoring

Historically, water managers and data scientists have used historical data to explain what has happened. While helpful in explaining what happened, this data doesn’t have the power to help operators make on-the-fly decisions that can halt contamination and protect equipment from breakdowns.

Real-time monitoring can help provide actionable insights to:

  • Balance chemical content (ultimately helping operators to use less and save money)
  • Protect equipment from breakages and corrosion
  • Catch contaminants early for faster, more efficient operations
  • Remove the need for time consuming and expensive lab-testing or other human interventions
  • Notify operators of maintenance requirements

Build and Access Systems that Offer Clean, Accurate Data

Many water operators are beginning to dip their toes into machine learning and artificial intelligence to take advantage of prescriptive and predictive analytics that can further streamline operations, lower costs, reduce downtime, and protect equipment.

However, for machine learning and AI to work optimally, systems need clean data at regular intervals to effectively “learn.” Without reliable data, operators won’t get the most out of their ML/AI investment. Therefore, it’s essential to take on platforms and devices to help feed accurate data into the system.

Look for Cloud Solutions That Guarantee Easy Access

One of the big lessons to come out of the COVID-19 epidemic was the benefit of having protocols in place that allowed for remote data capture. Needing to be on-site slows down the process of data collection and capture in many ways – even without a pandemic in play. By implementing cloud-based solutions, water operators don’t have to be on-site to pull and analyze data. They can do it from anywhere – at any time – getting alerts 24/7 to help them better make decisions and understand the water quality related to on-site activities.

Use Technology That Can Support Sustainability

As companies and communities continue to consider water usage through the lens of sustainability, devices that help protect water (and allow organizations to use less of it across processes) are likely to grow in necessity. Whether an organization is treating the water itself or ensuring its safety, finding ways to maximize every drop of water becomes easier with devices that leverage sensors, IoT, machine learning, and AI. These technological advancements can also help companies meet compliance regulations without adding to expensive overhead.

Interested in more insights on water and analytics? You can see Meena and Gary’s KETOS Water Cooler Chat below:

Want to see KETOS in action? Request a demo today.