Plutoshift Selected to Join Exclusive Sustainability Innovation Program

Plutoshift was born out of the desire to unlock the potential of AI to address energy and water challenges. And since our founding in 2017, sustainability and resource conservation has been a driving force behind our work.

So we were thrilled to have been recently selected for the 100+ Accelerator, a program with the express goal of accelerating sustainability innovation. The program, started in 2018 by AB-Inbev and backed by global leaders Coca-Cola, Colgate-Palmolive, and Unilever, strives to identify solutions for some of the most pressing sustainability challenges of our time. To that end, every year, the 100+ Accelerator team identifies a handful of qualified startups. Those chosen are organizations with innovative solutions and teams that are in the best position to succeed — the ability to scale quickly, make a significant impact on one or more challenge areas, and many like Plutoshift have a demonstrated track record.

This year, Plutoshift was among 36 startups selected out of over 1,300 applicants. It is of course exciting for us to be part of such an exclusive group. But more importantly, it validates the approach Plutoshift has taken to innovation. Every day, we focus on delivering AI and machine-learning solutions where they can make the greatest impact and generate tangible results. We’ve worked with global organizations across industries to get actionable intelligence into the hands of those on the front line of sustainability, and as a result, we have quickly helped them reduce resource consumption and their impact on the environment.

What’s most exciting about this partnership are: 1) it demonstrates that intelligent resource management and mitigating the impact of industry on the world is increasingly simply part of good business, and 2) organizations are more seriously committed to making real progress towards those goals. The challenges outlined by the 100+ Accelerator and the path forward are clearly defined, with a promise “to do business the right way, not the easy way.” Previous cohorts have already shown tremendous impact with solutions such as upcycling grain waste into nutrient-rich food and recycling electric vehicle batteries in China.

As proclaimed on the program site, “No one company can solve today’s sustainability challenges alone.” We wholeheartedly agree and look forward to continuing our work as part of the solution.

To learn more about the 100+ Accelerator program, visit www.100accelerator.com.

To discover how Plutoshift can help your business realize your sustainability goals, request a demo.

8 Security Standards For Safeguarding Customer Data

As a company, Plutoshift has many responsibilities towards our customers, teammates, vendors, and the environment. We manage critical data across many facets of our business. Being accountable for data privacy is at the very top of our priority list.

Our approach to data privacy and protection is straightforward. We are committed to upholding the highest and most internationally recognized privacy standards while maintaining our record of zero data security incidents.

In today’s world, it’s critical that businesses use data to make decisions. Our operational data platform was built for businesses to use their data to monitor physical infrastructure. Your data is the fuel that drives our platform’s engine. Just like how fuel from different companies varies in quality, data from different companies also varies in quality. Your data is unique and it gives your company a specific competitive advantage. We know you can’t afford to lose that edge while pursuing more efficient and effective operations.

That’s why we follow industry-leading security standards for data storage and protection. This encompasses security standards for how customer data is stored within our platform. It also includes user access requirements for things like passwords and administrative controls. Below are 8 ways we protect our customers’ data:

  1. All customer data is stored in a secure cloud container.
  2. Each container is assigned to a customer and there is no sharing of the containers.
  3. Plutoshift hosts the solution in a secure cloud and each solution is unique to the customer.
  4. There is no sharing of data with any other vendors, partners, or third parties.
  5. We engage certified auditors to evaluate our policies and procedures.
  6. User access is tightly controlled by the use of secure passwords and CAPTCHA.
  7. Users are not able to change the models or predictions.
  8. Admin access is granted to only those designated users within the customer’s organization who need to have the ability to provide individual user access and/or delete, demote, and disable other users.

As part of our ongoing commitment to data protection, we will review our policies and practices on a quarterly basis and update our customers on any changes. 

In an increasingly data-first world, we appreciate your trust in Plutoshift to keep your operations running safely and efficiently.

Towards 3Z Podcast: Zero Emissions, Zero Downtime, Zero Waste and Digital Transformation

I was honored to join Albert Vazquez-Agusti on Towards 3Z’s first podcast to talk about zero emissions, zero downtime and zero waste in a world where industrial transformation and energy transition are a must for everyone’s safety and economic development.

During this podcast, you’ll learn:

  • How to deploy an enterprise data platform across several plants belonging to the same company
  • How Covid accelerated the adoption of automation across various workflows
  • How to manage conversations with customers when they are considering CAPEX versus OPEX accounting in enterprise software
  • The need to focus on thoroughly assessing prospective customers to avoid “pilot purgatory”

Click the link below to listen.

https://medium.com/albert-vazquez-agusti/towards-3z-podcast-with-prateek-joshi-from-plutoshift-episode-1-f3eb14767559

The Water Values Podcast: Digital Transformation with Prateek Joshi

CEO Prateek Joshi talks about digital transformation in the water sector. Prateek hits on a number of important and practical points in a wide-ranging discussion on data, AI, and machine learning in the water sector.

In this session, you’ll learn about: 

  • Prateek’s background & how it influenced his arc into the water sector
  • Water-intensive industries and using water data in those industries
  • Prateek’s view on digital transformation
  • How COVID influenced the digital transformation
  • The limitations of human-based decision-making
  • Common challenges for data-centric organizations
  • How to drive organizational behavior change with respect to data usage
  • The difference between AI and machine learning
  • Data quality and verification issues
  • The factors companies look for when selecting an AI system

Click the link below to listen:

https://episodes.castos.com/watervalues/TWV-192-Digital-Transformation-with-Prateek-Joshi.mp3

 

 

Databases, Infrastructure, and Query Runtime

Recently, my team was tasked with making a switch from a combined MySQL and Cassandra infrastructure to one in which all of this data is stored entirely on a PostgreSQL server. This change was partially due to an increased drive to provide necessary and crucial flexibility to our customers, in tandem with the fact that Cassandra was simply not necessary for this particular application, even with the high quantities of data we were receiving. On its face, the mere need for such a change almost looks backwards given how much movement within the tech industry has been made away from SQL databases and towards NoSQL databases. But, in fact, NoSQL — or even hybrid systems — are not always best.

Performance Gain Considerations

In certain applications, one might find that performance gains, hoped to be reaped from NoSQL’s optimizations, may not translate perfectly to production without some forethought. I would personally argue that SQL databases often are preferable (over something like Cassandra) in non-trivial applications, most of all when JOIN operations are required.. Generally speaking, NoSQL databases — certainly Cassandra, among others — do not support JOIN. I will add to this that the vast majority of ORMs (for those who may not be familiar with the term, these are effectively systems of abstracting database relations into typically “object-oriented” style objects within one’s backend code) are built around SQL. Thus, the flexibility and readability that is afforded by these ORMs — at least when operating a database of non-trivial objects —can be a lifesaver for development time, database management, integrity, and readability. Indeed, I would even argue that, for most web applications, it often outweighs the sometimes marginal or even relatively negligible performance increases that a NoSQL database may provide (of course, this is completely dependent on the nature and scale of the data, but that is perhaps a topic for another time).

Cloud Infrastructure

However, none of this matters if the engineer is not paying close attention to their cloud infrastructure and the way that they are actually using their queries in production. In evaluating one engineer’s project, I found they were doing all of their insertion operations individually rather than attempting to batch or bulk insert them (when this was well within the scope of this particular application). It appeared they had been developing with a local setup and then deploying their project to the cloud where their database was running on a separate machine from their server. The end result in this case was rather comical, as once insertions were batched, even in Postgres, they were orders of magnitude faster than the piecemeal NoSQL insertions. They had not considered the simple fact of latency.

How did this original engineer miss this? I do not know, as this particular piece of software was inherited with little background knowledge. But, given that they were testing locally, I can assume that they elected for individual insertions. Making queries in this way can sometimes be less tricky than bulk insertions (which often have all sorts of constraints around them, and require a bit more forethought, especially when it comes to Cassandra). We found the performance was beyond satisfactory. What they did not consider, however, is that the latency between the backend server and a Cassandra (or SQL) server hosted in any sort of distributed system (ie. production). This meant that it didn’t really matter how fast these queries were; the latency between the backend and the database was so much greater than the query runtime, that, in fact, it really didn’t even remotely matter which database was used. So it followed that the real-world performance was actually significantly improved by simply batching insertions in Postgres (though of course, batching is supported in Cassandra — but the change was necessary nonetheless).

The Moral of the Story

In any case, the moral of the story here, in my opinion, is that understanding your own cloud infrastructure is crucial to writing actual performant programs in the real world. As well as the fact that, just because one database may be purported to perform better than another given certain circumstances, without a solid understanding of the environment in which this application is going to be deployed in, one cannot hope to see any appreciable performance gain.

Machine Learning In 20 Words Or Less

I’m often told that Machine Learning sounds complicated – but it doesn’t have to be. If I was asked to explain ML in 20 words or less, this is what it would sound like:

Understand the problem. Clean up the data. Investigate relationships. Engineer the dataset. Build the model. Tune to high performance.

At its core, ML is pretty straightforward. But it does need to follow a process. Here’s a more in-depth breakdown of the stages that can help you turn your data into proactive learnings: 

  • Understand – We can’t improve what we don’t understand, so our solutions are always grounded in a deep understanding of a process and the data related to that process.
  • Clean – The real world is messy, and data is almost never what we’ve been told. To get data ready for both analysis and (eventually) machine learning, we have to clean and process it.
  • Investigate – Before we can teach a machine what is important in a dataset, we have to understand it ourselves. Investigating data is really about driving a deeper understanding of a dataset, its correlations and relationships, identifying patterns, and so on. It’s rare that complex processes have simple solutions, but it’s often relatively simple analysis that sets us on the path of a solution.
  • Engineer – Machines are not smarter than humans; they are just great at fast math. But to learn best, they must be taught in very specific ways. This step is about prepping a dataset to train a model in the best way possible, as well as about bringing new information to the model to give it the best chance of seeing the signal we want.
  • Build & Tune – This is the fun part — creating, testing, and tuning predictive models. This stage includes retraining models as new data becomes available, as well as assessing model performance over and doing maintenance work to make sure the model continues to deliver value.

Don’t let complex terminology overwhelm you when it comes to using ML. All it takes is 20 words and 1 open mind.

Executive guide to assessing your data readiness in 5 steps

Within large companies, data is stored across many systems. If we specifically look at companies with large operations infrastructure, there are many different types of data they have to work with — sensors, inventory, maintenance, financials, and more. In order to perform the operational tasks, this data has to be centralized and piped into different workflows. The data needs to be ready for that! This allows the operations teams to use that information and ensure that the business is running smoothly. How do you assess the data readiness? How do you make sure that the frontline teams are well equipped to perform their tasks? From what we’ve seen, here are 5 items you’d need from data on a daily basis:

1. Ability to access the data
As fundamental as it seems, accessibility has always been a big issue. The data that’s stored across many systems is difficult to access. It’s inside arcane systems that are not friendly to use. In today’s world, anyone in the organization should be able pull the data via a simple API.

2. Centralizing the data
Once we pull the data from different systems, what do we do with it? Operations teams need the data to be centralized so that they can perform their tasks. These tasks usually require data from multiple sources. Centralizing the data and keeping it ready for use is a useful step here.

3. Preprocessing the data in an automated way
To make the data useful after it’s centralized, it has to be preprocessed to make sure it’s ready for different types of transformations. Operations teams need to prepare the data and pipe it into various data workflows. This is usually done manually using Excel spreadsheets. Automating this step will be very helpful so that operations teams can focus on high-impact items.

4. Piping the preprocessed data into different workflows
To put the preprocessed data to use, we need workflows that can transform raw data into useful information. A data workflow usually consists of 5-7 steps of transformation depending on what we’re aiming to achieve. These steps can be done manually using Excel, but it’s not a good use of anyone’s time because they are repetitive computations. Having a set of prebuilt workflows and automating the work of pushing the data through these workflows is a big time-saver. In addition to that, it will lead to a significant increase in accuracy of their work. Machine Learning is very impactful on this front.

5. System of record for the centralized processed data
Operations teams have to frequently access historical information for many reasons. Having a system of record that can store the centralized processed data is very useful. Operations teams need to reference them for various tasks such as internal reporting, knowledge-based tasks, learning, best practices, and more.

Data readiness is critical and lays the foundation for success.

7 roles your company needs to make AI initiatives successful

Modern companies have complex infrastructure with large operational teams. They tend to generate a lot of data across different functional areas of a business. Having this data gives them an advantage that can help drive operational efficiency and directly impact revenue. To extract value from data, they need a tool that can automatically extract information. This comes in the form of an AI solution! Companies launch AI initiatives to put this data into use.

The success of an AI endeavor goes beyond evaluating the accuracy of the AI solution. When you evaluate a new AI solution, you need to be sure you have the right group of people who can make it successful. From our experience, there are 7 roles within a  company that are critical  in driving  ROI and creating  business value using AI:

#1. Leader who owns P&L

A Business Unit Leader is responsible for driving P&L. This person directly benefits from increasing profitability. Within a big company, you need a strong leader who has the tenacity to look for business opportunities and be the authority that can implement new solutions.

#2. Trusted lieutenant who owns the tactical plan

The trusted lieutenant reports to the Leader and knows where the tactical opportunities exist. They drive resources towards these opportunities. This person is good at gathering consensus and making sure everyone stays focused on a given opportunity.

#3. Process Analyst who identifies the data needed

Once the opportunity is found, the Process Analysts identify what data is needed to bring this opportunity to life. They know what data is being collected across various processes and can connect the process knowledge to the data.

#4. Software Engineer who moves the data

Once the data is identified, the Software Engineer extracts the relevant data in a standard format. This person knows how to access the data and enables a smooth data transfer in an automated manner. 

#5. Architect who knows how to integrate

Once the data transfer is done, the Architect figures out the best place for the new solution within their existing infrastructure. Good AI solutions enhance the current architecture without ripping anything out. 

#6. Manager who has a small team

The Manager reports to the Lieutenant and manages a close-knit team. This team is responsible for getting the day-to-day work done. People on this team are the primary users of the new AI solution.

#7. Best Performer on that small team

The Best Performer on the team reports to the Manager. This person can help bring the solution to life given that they are likely a rising star in the organization. This is someone with a lot of drive that the colleagues respect. They can benefit the most from an AI solution due to their busy schedule since it can help them achieve their day-to-day tasks as well as their personal goals. A good AI solution increases the speed and accuracy of their work. The Best Performer ends up being a power user of the AI solution and drives adoption for the organization. 

5 ways in which modern teams manage their operations infrastructure

Companies that produce physical goods have expansive operations infrastructure. This infrastructure is well instrumented, which means they are collecting data continuously. But it is the operations teams’ responsibility to make sure they monitor everything. How do they make sure that it’s running smoothly? How do they look for relevant signals that indicate potential issues? How do they use the data that they are collecting to improve performance? Here’s what we found out about what makes an operations team successful:

#1. Modern teams are proactive

There are many moving pieces within a production facility. Teams can’t just sit around and wait for disasters to happen without having a game plan.  Modern teams use a proactive approach in their strategy that prepares them to handle what could happen in the future. They constantly look for anomalous signals. This helps them take action and make sure their operations run smoothly.

#2. Modern teams know that they can’t use brute force

Operators have to manage a lot of work simultaneously. At any given point of time, there are an overwhelming amount of data coming their way. Modern teams know that they can’t use brute force to manually analyze every single data point. They realize that they need tools that can process the raw data and extract the right information that can be put to use. These tools can highlight the root cause of a problem, which then allows the operator to resolve it quickly. The insights that are derived from these tools are key to getting the job done successfully.

#3. Modern teams are data savvy

Operators need to make sure that the facility meets the production requirements each day. They always rely on data to track their team’s performance and check their progress.  Experience plays an important role in decision making; however, they constantly look at data to support those decisions.  Data empowers operators to focus their time on the most important or critical factors and enables them to do their jobs better.

#4. Modern teams relentlessly prioritize their work 

Operators have a lot of tasks to manage with many alerts and notifications coming their way. Prioritizing tasks makes sure that teams do not stray from the path of achieving success. Teams can avoid wasting time by identifying priorities and organizing their work accordingly. This also helps them avoid duplication. Operators need to use the right tools to help them get to the high priority tasks quickly and efficiently.

#5. Modern teams enhance themselves with automation

Modern operators rely on tools to automate the monitoring work. These tools arm them with information that’s needed to get the work done. Instead of spending hours on putting the data together on a spreadsheet, they use tools that can automate this step for them so that they have this information handy. This is a powerful way in which modern teams keep an eye on their infrastructure. Through automation, they are able to optimize their own efficiency and maximize their time.

4 key learnings from working with companies that drive our economy

At Plutoshift, we work closely with companies that drive our economy such as manufacturers, service providers, warehouse operators, distributors, and logistics providers. As we approach the tail end of 2020, we wanted to know — How are companies managing their work amidst the chaos? Here’s what we learned:

#1. Seemingly disconnected sectors are not as immune to each other as they previously thought

2020 has been cathartic when it comes to thinking about digital transformation. It went from being “nice to have” to “must have”  as businesses try to prop themselves up and innovate as a way of surviving. With the pandemic, we’re seeing more clearly how intertwined and co-dependent various industries are. There’s a chain reaction that happened throughout this pandemic:

People can’t go out as much, so sectors such as travel and restaurants got decimated. People are losing jobs, so they can’t afford to spend money on items outside of non-essential goods. Sectors such as entertainment and luxury goods also got decimated given closures and the sheer fact there is nowhere to go outside of our homes. Companies that service these sectors such as oil & gas and farming are being impacted due to the supply chain disruption. 

These sectors support businesses that make this country thrive, but they are facing strong headwinds due to the chain reaction. The market conditions gave rise to this particular chain reaction and it impacted a specific set of sectors in a strong way.

#2. Companies that provide essential goods are doing well

In turn, we have seen that there are certain sectors that are thriving and less sensitive to the economy. In the middle of all of the pandemic chaos, there was a shortage of products due to the demand going up quickly as people prepared to be sheltered in place. A boom in sales continues for grocery stores, packaged goods companies, and Amazon as consumers are spending money on essentials like food, soap, toilet paper, and canned goods. 

Figuring out how to be essential to your customers is a good way to survive any downturn. We’ve seen many companies reinvent themselves through manufacturing items that are needed including PPE materials.

#3. Ramping up capacity in the physical world is not nearly as easy as ramping up capacity in the cloud-software world

When a cloud-based software needs to provide service to more users, you just spin up more servers on your cloud infrastructure. But it’s very different when it comes to the physical world! The consumer goods companies provide products and services that people use on a daily basis. Now they have to produce more per day in the same facilities with the same number of people. 

This means there are more moving parts in the system now — more units to produce, more resources to consume, more processes to keep track of, more anomalies that they need to anticipate. In order to keep up, they need to monitor everything — prioritize what needs their attention, get to the root cause, and estimate what’s coming up next. It’s very difficult to do all this work manually. They need an automated tool to do all this!

#4. Digital transformation needs to bite-sized

Companies that produce physical goods have realized that they need digital tools to get their work done. These tools help them do a variety of tasks like collect data, produce reports, predict anomalies and monitor the performance of their operations. They’ve realized that automation has become a business imperative and that data plays the central role in that initiative. Since companies nowadays don’t have time to do a 5-year project, they need to pick the processes that can be digitized one by one. More and more company leaders are finding ways to enable their individual work practices and processes which we expect to be a continued trend.