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Digital β€” Water β€” Climate

🌐 "Digital Transformation"

The Water Values Podcast

Photo by Hitesh Choudhary / Unsplash

Host: Dave McGimpsey
Guest: Prateek Joshi | CEO | Plutoshift
Category: 🌐 Digital

Podcast’s Essential Bites:

[4:33] β€œPlutoshift [combines] AI and water. […] We automate data workflows for operations teams and it's built for companies that have expansive physical infrastructure. […] What we work on [is] anything ranging from auto membranes, wastewater, reuse, electricity consumption and water processes. […] And what we do is we take the data from disparate systems, we centralize it, and then we have built a range of use cases to help them monitor their membranes, cooling towers, boilers, refrigerator site and chemical dosing and wastewater.”

[6:22] β€œFood and beverage, chemicals, power and renewables […] are some of the verticals that we have seen that water is used pretty heavily, either for other parts or the main product [and therefore need efficient data management]. […] If you're a […] beverage company, you need a lot of water to make the product. And then if you are a chemicals company, you use a lot of water to follow your processes. And then you have to take care of tens of millions of gallons of wastewater.”

[7:11] β€œ2020 obviously [has] been a very cathartic year when it comes to thinking about digital transformation. […] In the last decade [companies] started getting very serious about collecting data […], they invested billions of dollars [to] set up the data collection systems. Now that data is just sitting there, it's not doing anything, people still have to use Excel to do the work. […] [With COVID, they then] got to have a tool because [they] can't physically go into the facility every day. […] People realized, transformation or not, we just got to figure out a way to use a digital tool to keep an eye on all of our infrastructure, it's sort of a basic existential need. And that […] really forced companies to not only instrument themselves, but also have rules in place where they use the data to make decisions because they physically can't be there. […] And that thinking of moving from this Excel slash manual decision mode to having a tool that can surface those items for them, [is] a very big shift. And I think that's what we are seeing in the market when it comes to digital transformation.”

[17:24] β€œI think the main challenge is […] to be a data centric org. […] So what it means is all the people who are part of that organization […] should be able to easily access data, quickly, kind of transform that data, and then put it in a form that is useful in making decisions. Now, when you look at many companies, they almost get stuck in step one, meaning getting access to data, […] mostly because it's […] locked in somewhere and it's stored in some weird format. So simply centralizing that data and making it accessible is a big one.”

[25:38] β€œIndustrial companies […] have special teams dedicated to testing out new technologies and […] most of them […] have at least a framework for testing. […] In utilities, it's a little more difficult, because […] they're regulated and […] if you get something wrong, […] it's gonna be pretty scrutinized […]. So because of that, they are a little more risk averse in terms of testing out our new technologies.”

[29:44] β€œOne [use case] is auto membranes [or] reverse osmosis membranes. And [you can find these] anywhere where drinking water […] is involved. […] Usually the goal is to make sure that […] [the purchased] water [is] clean, before you use it for the product. And the operators have to make sure that all the membranes […] do their part [in] cleaning the water. And then they have to figure out how to keep an eye on these membranes. Now, the goal is to […] monitor and predict key metrics for the auto membranes. And the benefits would be […] to reduce electricity consumption, predict clean dates, and increase [the] water recovery rate.”

[32:24] β€œAnother example would be in wastewater, where there are clarifiers […]. The goal is to monitor and predict the key metrics for chemical dosing, meaning for your processes, you use a lot of water and before disposing it, […] you've got to clean it. […] So to clean the water, you need a lot of chemicals. […] So the goal is how do we use data to reduce chemical consumption and yet meet the quality requirements […] and then obviously reduce costs. […] So it's […] a tool that can monitor the performance of the infrastructure and make sure that you monitor the key metrics to detect anomalies, [and] predict upcoming events. […] That drives a lot of value.”

Rating: πŸ’§πŸ’§

πŸŽ™οΈ Full Episode: Apple | Spotify
πŸ•°οΈ 40 min | πŸ—“οΈ 05/18/2021
βœ… Time saved: 38 min