The power of green computing

When performing radiation therapy treatment, accuracy is key. Typically, the process of targeting cancer-affected areas for treatment is painstakingly done by hand. However, integrating a sustainably optimized AI tool into this process can improve accuracy in targeting cancerous regions, save health care workers time, and consume 20% less power to achieve these improved results. This is just one application of sustainable-forward computing that can offer immense improvements to operations across industries while also lowering carbon footprints.

Investments now in green computing can offer innovative outcomes for the future, says chief product sustainability officer and vice president and general manager for Future Platform Strategy and Sustainability at Intel, Jen Huffstetler. But transitioning to sustainable practices can be a formidable challenge for many enterprises. The key, Huffstetler says, is to start small and conduct an audit to understand your energy consumption and identify which areas require the greatest attention. Achieving sustainable computing requires company-wide focus from CIOs to product and manufacturing departments to IT teams.

“It really is going to take every single part of an enterprise to achieve sustainable computing for the future,” says Huffstetler.

Emerging AI tools are on the cutting edge of innovation but often require significant computing power and energy. “As AI technology matures, we’re seeing a clear view of some of its limitations,” says Huffstetler. “These gains have near limitless potential to solve large-scale problems, but they come at a very high price.”

Mitigating this energy consumption while still enabling the potential of AI means carefully optimizing the models, software, and hardware of these AI tools. This optimization comes down to focusing on data quality over quantity when training models, using evolved programming languages, and turning to carbon-aware software.

As AI applications arise in unpredictable real-world environments with energy, cost, and time constraints, new approaches to computing are necessary.

This episode of Business Lab is produced in partnership with Intel.

Full Transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic today is building an AI strategy that’s sustainable, from supercomputers, to supply chain, to silicon chips. The choices made now for green computing and innovation to make a difference for today and the future.

Two words for you: sustainable computing.

My guest is Jen Huffstetler. Jen is the chief product sustainability officer and vice president and general manager for Future Platform Strategy and Sustainability at Intel.

This podcast is produced in partnership with Intel.

Welcome, Jen.

Jen Huffstetler: Thanks for having me, Laurel.

Laurel: Well, Jen, a little bit of a welcome back. You studied chemical engineering at MIT and continue to be involved in the community. So, as an engineer, what led you to Intel and how has that experience helped you see the world as it is now?

Jen: Well, as I was studying chemical engineering, we had lab class requirements, and it so happened that my third lab class was microelectronics processing. That really interested me, both the intricacy and the integration of engineering challenges in building computer chips. It led to an internship at Intel. And I’ve been here ever since.

And what I really love about it is we are always working on the future of compute. This has shaped how I see the world, because it really brings to life how engineers, the technology that is invented can help to advance society, bringing access to education globally, improving healthcare outcomes, as well as helping to shape work overall. As we were able to move to this pandemic world, that was all technology infrastructure that helped to enable the world to continue moving forward while we were facing this pandemic.

Laurel: That’s really great context, Jen. So, energy consumption from data infrastructure is outpacing the overall global energy demand. As a result, IT infrastructure needs to become more energy efficient. So, what are the major challenges that large-scale enterprises are facing when developing sustainability strategies?

Jen: Yeah, when we survey IT leaders[1] , we find that 76% believe that there is a challenge in meeting their energy efficiency goals while increasing performance to meet the needs of the business. In fact, 70% state that sustainability and compute performance are in direct conflict.

So, we don’t believe they have to be in conflict if you’re really truly utilizing the right software, the right hardware, and the right infrastructure design. Making operations more sustainable, it can seem daunting, but what we advise enterprises as they’re embarking on this journey is to really do an audit to survey where the biggest area of impact could be and start there. Not trying to solve everything at once, but really looking at the measurement of energy consumption, for an example in a data center today, and then identifying what’s contributing the most to that so that you can build projects and work to reduce in one area at a time.

And what we like to say is that sustainability, it’s not the domain of any one group at a company. It really is going to take every single part of an enterprise to achieve sustainable computing for the future. That includes of course, the CIOs with these projects to focus on reducing the footprint of their computing profile, but also in design for product and manufacturing companies, making sure that they’re designing and architecting for sustainability, and throughout the overall operations to ensure that everyone is reducing consumption of materials, whether it’s in the factory, the number of flights that a marketing or sales team is taking, and beyond.

Laurel: That’s definitely helpful context. So technologies like AI require significant computing power and energy. So, there’s a couple questions around that. What strategies can be deployed to mitigate AI’s energy consumption while also enabling its potential? And then how can smart investment in hardware help with this?

Jen: This is a great question. Technologies like you mentioned, like AI, they can consume so much energy. It’s estimated that the ChatGPT-3 model consumes 1.28 gigawatt hours of electricity, and that’s the same as the consumption for 120 US homes for a year. So, this is mind-boggling.

But one of the things that we think about for AI is there’s the training component and the inference component. You think about a self-driving car, and you train the model once and then it’s running on up to a hundred million cars, and that’s the inference. And so what we actually are seeing is that 70 to 80% of the energy consumption, or two to three x the amount of power is going to be used running inference as it can be to train the model. So, when we think about what strategies can be employed for reducing the energy consumption, we think about model optimization, software optimization, and hardware optimization, and you can even extend it to data center design.

They’re all important, but starting with model optimization, the first thing that we encourage folks to think about is the data quality versus data quantity. And using smaller data sets to train the model will use significantly less energy. In fact, some studies show that many parameters within a trained neural network can be pruned by as much as 99% to yield a much smaller, a sparser network, and that will lower your energy consumption.

Another thing to consider is tuning the models for a lower accuracy of intake. And an example of this is something we call quantization, and this is a technique to reduce your computational and your memory costs of running inference, and that’s by representing the weights and the activations with lower precision data types, like an 8-bit integer instead of a 32-bit floating point.

So, those are some of the ways that you can improve the model, but you can also improve them and lower their energy costs by looking at domain-specific models. Instead of reinventing the wheel and running these large language models again and again, if you, for example, have already trained a large model to understand language semantics, you can build a smaller one that taps into that larger model’s knowledge base and it will result in similar outputs with much greater energy efficiency. We think about this as orchestrating an ensemble of models. Those are just a couple of the examples. We can get more into the software and hardware optimization as well.

Laurel: Yeah, actually maybe we should stay on that a bit, especially considering how energy intensive AI is. Is there also a significant opportunity for digital optimization with software, as you mentioned? And then you work specifically with product sustainability, so then how can that AI be optimized across product lines for efficiency for software and hardware? Because you’re going to have to think about the entire ecosystem, correct?

Jen: Yeah, that’s right. This is really an area where I think in the beginning of computing technology, you think about the very limited resources that were available and how tightly integrated the coding had to be to the hardware. You think about the older programming languages, assembly languages, they really focused on using the limited resources available in both memory and compute.

Today we’ve evolved to these programming languages that are much more abstracted and less tightly coupled, and so what leaves is a lot of opportunity to improve the software optimization to get better use out of the hardware that you already have that you’re deploying today. This can provide tremendous energy savings, and sometimes it can be just through a single line of code. One example is Modin, an open source library which accelerates Pandas applications, which is a tool that data scientists and engineers utilize in their work. This can accelerate the application by up to 90x and has near infinite scaling from a PC to a cloud. And all of that is just through a single line of code change.

There’s many more optimizations within open source code for Python, Pandas, PyTorch, TensorFlow, and Scikit. This is really important that the data scientists and engineers are ensuring that they’re utilizing the most tightly coupled solution. Another example for machine learning on Scikit is through a patch, or through an Anaconda distribution, you can achieve up to an 8x acceleration in the compute time while consuming eight and a half times less energy and 7x less energy for the memory portions. So, all of this really works together in one system. Computing is a system of hardware and software.

There’s other use cases where when running inference on a CPU, there are accelerators inside that help to accelerate AI workloads directly. We estimate that 65% to 70% of inference is run today on CPUs, so it’s critical to make sure that they’re matching that hardware workload, or the hardware to the workload that you want to run, and make sure that you’re making the most energy-efficient choice in the processor.

The last area around software that we think about is carbon-aware computing or carbon-aware software, and this is a notion that you can run your workload where the grid is the least carbon-intensive. To help enable that, we’ve been partnering with the Green Software Foundation to build something called the Carbon Aware SDK, and this helps you to use the greenest energy solutions and run your workload at the greenest time, or in the greenest locations, or both. So, that’s for example, it’s choosing to run when the wind is blowing or when the sun is shining, and having tools so that you are providing the insights to these software innovators to make greener software decisions. All of these examples are ways to help reduce the carbon emissions of computing when running AI.

Laurel: That’s certainly helpful considering AI has emerged across industries and supply chains as this extremely powerful tool for large-scale business operations. So, you can see why you would need to consider all aspects of this. Could you explain though how AI is being used to improve those kind of business and manufacturing productivity investments for a large-scale enterprise like Intel?

Jen: Yeah. I think Intel is probably not alone in utilizing AI across the entirety of our enterprise. We’re almost two companies. We have a very large global manufacturing operations that is both for the Intel products, which is sort of that second business, but also a foundry for the world’s semiconductor designers to build on our solutions.

When we think of chip design, our teams use AI to do things like IP block placement. So, they are looking at grouping the logic, the different types of IP. And when you place those cells closer together, you’re not only lowering cost and the area of silicon manufacturing that lowers your embodied carbon for a chip, but it also enables a 50% to 30% decrease in the timing or the latency between the communication of those logic blocks, and that accelerates processing. That’ll lower your energy costs as well.

We also utilize AI in our chip testing. We’ve built AI models to help us to optimize what used to be thousands of tests and reducing them by up to 70%. It saves time, cost, and compute resources, which as we’ve talked about, that will also save energy.

In our manufacturing world we use AI and image processing to help us test a 100% of the wafer, detect up to 90% of the failures or more. And we’re doing this in a way that scales across our global network and it helps you to detect patterns that might become future issues. All of this work was previously done with manual methods and it was slow and less precise. So, we’re able to improve our factory output by employing AI and image processing techniques, decreasing defects, lowering the waste, and improving overall factory output.

We as well as many partners that we work with are also employing AI in sales techniques where you can train models to significantly scale your sales activity. We’re able to collect and interpret customer and ecosystem data and translate that into meaningful and actionable insights. One example is autonomous sales motions where we’re able to offer a customer or partner the access to information, and serving that up as they’re considering their next decisions through digital techniques, no human interventions needed. And this can have significant business savings and deliver business value to both Intel and our customers. So, we expect even more use at Intel, touching almost every aspect of our business through the deployment of AI technologies.

Laurel: As you mentioned, there’s lots of opportunities here for efficiencies. So, with AI and emerging technologies, we can see these efficiencies from large data centers to the edge, to where people are using this data for real-time decision making. So, how are you seeing these efficiencies actually in play?

Jen: Yeah, when I look at the many use cases from the edge, to an on-prem enterprise data center, as well as to the hyperscale cloud, you’re going to employ different techniques, right? You’ve got different constraints at the edge, both with latency, often power, and space constraints. Within an enterprise you might be limited by rack power. And the hyperscale, they’re managing a lot of workloads all at once.

So, starting first with the AI workload itself, we talked about some of those solutions to really make sure that you’re optimizing the model for the use case. There’s a lot of talk about these very large language models, over a hundred billion parameters. Every enterprise use case isn’t going to need models of that size. In fact, we expect a large number of enterprise models to be 7 billion parameters, but using those techniques we talked about to focus it on answering the questions that your enterprise needs. When you bring those domain specific models in play, they can run on even a single CPU versus this very large-scale dedicated accelerator clusters. So, that’s something to think about when you’re looking at, what’s the size of the problem I’m trying to solve, where do I need to train it, how do I need to run the inference, and what’s the exact use case? So, that’s the first thing I would take into account.

The second thing is, as energy becomes ever more a constraint across all of those domains, we are looking at new techniques and tools in order to get the most out of the energy that’s available to that data center, to that edge location. Something that we are seeing an increasing growth and expecting it to grow ever more over time is something called liquid cooling. Liquid cooling is useful at edge use cases because it is able to provide a contained solution, where sometimes you’ve got more dust, debris, particles, you think about telco or base stations that are out in very remote locations. So, how can you protect the compute and make it more efficient with the energy that’s available there?

We see the scaling both through enterprise data centers all the way up to large hyperscale deployments because you can reduce the energy consumption by up to 30%, and that’s important when today up to 40% of the energy in a data center is used to keep it cool. So, it’s kind of mind boggling the amount of energy or inefficiency that’s going into driving the compute. And what we’d love to see is a greater ratio of energy to compute, actually delivering compute output versus cooling it. And that’s where liquid cooling comes in.

There’s a couple of techniques there, and they have different applications, as I mentioned. Immersion’s actually one that would be really useful in those environments where it’s very dusty or there’s a lot of pollution at the edge where you’ve got a contained system. We’re also seeing cold plate or direct to chip. It’s already been in use for well over a decade in high performance computing applications, but we’re seeing that scale more significantly in these AI cluster buildouts because many data centers are running into a challenge with the amount of energy they’re able to get from their local utilities. So, to be able to utilize what they have and more efficiently, everyone is considering how am I going to deploy liquid cooling?

Laurel: That’s really interesting. It certainly shows the type of innovation that people are thinking about constantly. So, one of those other parts of innovation is how do you think about this from a leadership perspective? So, what are some of those best practices that can help an enterprise accelerate sustainability with AI?

Jen: Yeah, I think just to summarize what we’ve covered, it’s emphasizing that data quality over quantity, right? The smaller dataset will require less energy. Considering the level of accuracy that you really need for your use case. And again, where can you utilize that INT8 versus those compute intensive FP32 calculations. Leveraging domain-specific models so that you’re really right sizing the model for the task. Balancing your hardware and software from edge to cloud, and within a more heterogeneous AI infrastructure. Making sure that you’re using the computing chip set that’s necessary to meet your specific application needs. And utilizing hardware accelerators where you can to save energy both in the CPU as well. Utilizing open source solutions where there’s these libraries that we’ve talked about, and toolkits, and frameworks that have optimizations to ensure you’re getting the greatest performance from your hardware. And integrating those concepts of carbon-aware software.

Laurel: So, when we think about how to actually do this, Intel is actually a really great example, right? So, Intel’s committed to reaching net zero emissions in its global operations by 2040. And the company’s cumulative emissions over the last decade are nearly 75% lower than what they would’ve been without interim sustainability investments. So, then how can Intel’s tools and products help other enterprises then meet their own sustainability goals? I’m sure you have some use case examples.

Jen: Yeah, this is really the mission I’m on, is how can we help our customers lower their footprint? One of the first things I’ll just touch upon is, because you mentioned our 2040 goals, is that our data center processors are built with 93% renewable electricity. That immediately helps a customer lower their Scope 3 emissions. And that’s part of our journey to get to sustainable compute.

There’s also embedded accelerators within the Xeon processors that can deliver up to 14x better energy efficiency. That’s going to lower your energy consumption in data center no matter where you’ve deployed that compute. And of course, we have newer AI accelerators like Intel Gaudi, and they really are built to maximize the training and inference throughput and efficiency up to 2x over competing solutions. Our oneAPI software helps customers to take advantage of those built-in accelerators with solutions like an analytics toolkit and deep learning neural network software with optimized code.

We take all those assets, and just to give you a couple of customer examples, the first would be SK Telecom. This is the largest mobile operator in South Korea, 27 million subscribers. They were looking to analyze the massive amount of data that they have and really to optimize their end-to-end network AI pipeline. So, we partnered with them, utilizing the hardware and software solutions that we’ve talked about. And by utilizing these techniques, they were able to optimize their legacy GPU based implementation by up to four times, and six times for the deep learning training and inference. And they moved it to just a processor-based cluster. So, this really, it’s just an example where when you start to employ the hardware and the software techniques, and you utilize everything that’s inside the solution in the entire pipeline, how you can tightly couple the solution. And it doesn’t need to be this scaled out dedicated accelerator cluster. So, anyway, that’s one example. We have case studies.

Another one that I really love is with Siemens Healthineers. So, this is a healthcare use case. And you can envision for radiation therapy, you need to really be targeted where you’re going to put the radiation in the body, that it’s just hitting the organs that are being affected by the cancer. This contouring of the organs to target the solution was previously done by hand. And when you bring AI into the workflow, you’re not only saving healthcare workers’ time, of which we know that’s at a premium since there’s labor shortages throughout this industry, that they were able to improve the accuracy, improve the image generation 35 times faster, utilizing 20% less power, and enabling those healthcare workers to attend to the patients.

The last example is an intercom global telecommunication system provider with KDDI, which is Japan’s number one telecom provider. They did a proof of concept on their 5G network using AI to predict the network traffic. By looking at their solutions, they were able to scale back the frequency of the CPUs that were used and even idling them when not needed. And they were able to achieve significant power savings by employing those solutions. These are just ways where you can look at your own use cases, making sure that you’re meeting your customer SLAs or service level agreements, as is very critical in any mobile network, as all of us being consumers of that mobile network agree. We don’t like it when that network’s down. And these customers of ours were able to deploy AI, lower their energy consumption of their compute, while meeting their end use case needs.

Laurel: So Jen, this has been a great conversation, but looking forward, what are some product and technology innovations you’re excited to see emerge in the next three to five years?

Jen: Yeah, outside of the greater adoption of liquid cooling, which we think is foundational for the future of compute. In the field of AI, I’m thinking about new architectures that are being pioneered. There’s some at MIT, as I was talking to some of the professors there, but we also have some in our own labs and pathfinding organizations.

One example is around neuromorphic computing. As AI technology matures, we’re seeing a clear view of some of its limitations. These gains have near limitless potential to solve large-scale problems, but they come at a very high price, as we talked about with the computational power, the amount of data that gets pre-collected, pre-processed, et cetera.

So, some of these emerging AI applications arise in that unpredictable real world environment, and as you talked about some of those edge use cases. There could be power latency or data constraints, and that requires fundamentally new approaches. Neuromorphic computing is one of those, and it represents a fundamental rethinking of computer architecture down to the transistor level. And this is inspired by the form and the function of our human biological neural networks in our brains. It departs from those familiar algorithms and programming abstractions of conventional computing to unlock orders of magnitude gains in efficiency and performance. It can be up to 1,000x. I’ve even seen use cases of 2,500x energy efficiency over traditional compute architectures.

We have the Loihi research processor that incorporates these self-learning capabilities, novel neuro models, and asynchronous spike-based communication. And there is a software community that is working to evolve the use cases together on this processor. It consumes less than a watt of power for a variety of applications. So, it’s that type of innovation that really gets me excited for the future.

Laurel: That’s fantastic, Jen. Thank you so much for joining us on the Business Lab.

Jen: Thank you for having me. It was an honor to be here and share a little bit about what we’re seeing in the world of AI and sustainability.

Laurel: Thank you.

That was Jen Huffstetler, the chief product sustainability officer and vice president and general manager for Future Platform Strategy and Sustainability at Intel, whom I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the Global Director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Decarbonizing your data strategy

Posting just a six-second video on social media uses the same amount of power as boiling 22 gallons of water. This staggering statistic encapsulates just how intertwined data management is with sustainability. And as companies look to become data-driven and to gain insights from vast data streams, it’s also crucial to keep an eye on the environmental cost of those efforts.

The road toward decarbonization is daunting, especially while trying to keep pace with innovation. According to the director of data platform product marketing at Hitachi Vantara, Ian Clatworthy, companies need to take their own initiative when it comes to sustainability measures and just start somewhere.

“When we look at integrating the carbonization goals into budgets, the agenda is crucial. Start where you can actually have some impact,” says Clatworthy.

Making data hardware and infrastructure more sustainable begins with understanding the value of what you’re storing, Clatworthy says. From there, companies can invest in the most data and energy-efficient servers, storage devices, and networking equipment.

“Look at data flows, adopt energy-efficient technologies, and that’s going to really align your data processing capabilities with those goals,” says Clatworthy.

Although many companies have made strict commitments to become emissions-free internally and within their own operations, decarbonizing entirely throughout a supply chain is exceedingly challenging. Clatworthy says that it comes down to transparency. A company can be cognizant of the emissions released in its own operations but down the supply chain to an outside manufacturer or supplier, it may not be as forthcoming about the scope of its footprint.

Data storage technology is always evolving, but the technology needs to be right for your company, he says. The shiniest or newest tool may be faster or more efficient but it’s important to keep in mind its energy consumption and the impact on emissions.

“You need to adopt a multifaceted approach that combines energy-efficient infrastructure, renewable energy sourcing, optimize the data management practices, but commit to that transparency and sustainability reporting,” says Clatworthy. “The environmental concerns will continue to grow, and these trends will play a critical role in shaping the future of data management.”

This episode of Business Lab is produced in partnership with Hitachi Vantara.

Related resources

Making sustainability achievable with data

Hitachi Vantara’s CO2 estimator

Full transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic today is embracing sustainability initiatives in data storage and management, as well as throughout the supply chain. Enterprises need to act now to realize decarbonization goals and meet environmental, social, and corporate governance deadlines.

Two words for you: carbon reduction.

My guest is Ian Clatworthy, who is the director of data platform product marketing at Hitachi Vantara.

This podcast is produced in partnership with Hitachi Vantara.

Welcome, Ian.

Ian Clatworthy: Hey, Laurel, thank you so much for having me.

Laurel: Well, great to have you, and I think we’ll just dive right in. So most enterprises are prioritizing sustainability and reducing their overall carbon footprint as climate changes persist globally. Data management and storage is a critical factor in an enterprise’s carbon emissions. So could you describe the state of data management right now? Is there a prevailing understanding of how to make data centers more energy efficient during this transitional period, or are the methods evolving?

Ian: Great question, Laurel. And many enterprises have indeed been prioritizing sustainability and looking to reduce their carbon footprint. We know now our impact on the environment is much higher than we ever thought it was. We know that data centers contribute more CO2 than the airline industry. That’s massive. So there is this effort to try and consolidate what we’re doing and reduce our carbon emissions. So, therefore, as data centers, we really need to understand the circular economics of the products we’re putting in there for data management. And that really starts at how I’m really falling into the scope one, two, and three emissions. Scope one, stuff I can control, stuff I’m producing. “Am I burning gas to heat my offices? Are my sales team using electric cars or petrol cars?” That’s scope one. Scope two being much more of the “where am I buying my energy from? Is that renewable? Or where’s my data center located?” Data center locality has a huge impact on carbon footprint. A great example of that is what’s the greenest state in the US to hold your data, do you know, Laurel?

Laurel: I don’t, what is it?

Ian: It’s Texas and Austin specifically. They have the highest renewables rate in the United States. So therefore my carbon footprint can be lowered massively by putting my data in Texas. So there are elements like that that really, from Scope 2, can make a difference. But the key one is Scope 3. This is your other emissions. And when we get to that circular economics, it’s well, actually, who’s the vendor you can trust to provide you true transparency on carbon footprint? So I’m not talking about just running it in your data center. I’m talking, well, where have I sourced my metals from? How have I produced it? Have I developed my software in a way that’s sufficient and carbon neutral? How have I shipped it to you? How am I going to recycle the product? So actually I think we very quickly, and a lot of customers get into, is the need to think about advanced cooling techniques or more monitoring and management.

That’s purely the efficiencies of running the box. Absolutely, it needs to happen. But the challenge coming in, and this is where regulations are going to force our hand in some ways, is that they’re going to say, well, you have to have a carbon reduction throughout the supply chain. And from suppliers, like us as Hitachi Vantara, we have to declare what’s the carbon footprint of my product? And a great example of that is we’ve shifted. We do manufacture absolutely in Japan, but again, how do we shift software development, are we doing it closer to the API? Are we doing it closer to the box? Is it efficient? All those things really matter. So these methods and technologies are evolving to meet these challenges. But I think what the enterprises need to do is really open their mind as to what data they’re storing, how they’re storing it, but also where their suppliers are providing it, where they’re storing their data. Data locality matters, but at the end of the day, you’ve got to understand that data, so important.

Laurel: That’s really interesting that the tech industry, well, through data centers are contributing more to greenhouse emissions than the airline industry, which that’s astonishing. So how should leaders integrate decarbonization goals into their budgets and agendas? I mean, you mentioned a bit about data locality, but are there proactive steps they can start thinking about?

Ian: Absolutely. This is also quite overwhelming, and I speak to customers a lot on this. They’re like, where do I start? This isn’t a competitive thing. And we see this a little bit in our industry as like, oh, well, who is more greener than who? It doesn’t matter. We’re doing this as a collective. You’ve got to start somewhere. So making a step, be that as small as it is, is a massive thing, and it shouldn’t be underestimated. When we look at integrating the carbonization goals into budgets, the agenda is crucial. Start where you can actually have some impact. Don’t try and bite off more than you can chew as the saying says. Set some targets. Say, “Well, look, I’m just going to understand what my CO2 footprint is.” First of all, “what am I consuming day-to-day? How much renewable energy sources am I using?” And set some targets around that based upon where you can get to.

Allocate some resources to it. Allow that from financials, human resources, sustainability projects and initiatives across your organizations. This isn’t just IT. Invest in energy efficient technologies. Go and ask your vendors to help you. As I say, it’s not a competitive thing, come and ask people like us and say, “Well, what would you recommend? Where would I start? What’s a good thing?” We’re not here to cram things in your face, we’re here to help you make those steps. And to your point around the airline industry, there are so many stats that are just eye watering that really kind of change thinking. And one that really brings to mind for me is a six second social media video uses the same amount of power as you would use to boil 22 gallons of water, a six second video.

And think about it, that’s because I’m powering the phone, I’m powering the cell mass, I’m powering the data centers, I’m replicating the data centers, presenting that back out. That’s exactly what we’re doing. So this idea of… just start somewhere. Before we get to regulation, and that will happen. That’s a given. But have regular reviews, look at what you’re doing, take a step, understand what you’re doing today, and make it a part of your agenda, of your business’s agenda. Because you know what? I say this isn’t competitive, but reality is, you need to be competitively sustainable to exist in your industry. Customers will choose someone different. That’s why this is really important.

Laurel: To sort of put this into a real stake, how does a company like Hitachi Vantara measure its own consumption of power and emissions? And what’s the company’s approach to reducing its own carbon footprint?

Ian: It’s constantly evolving. It is absolutely. The first thing is constant audits. We have a team dedicated to looking at sustainability and to be key here, Hitachi Vantara is a wholly-owned subsidiary of Hitachi Limited. And Hitachi Limited, our executives are entirely incentivized on our green capabilities. That’s absolutely key. This idea of executives making bonuses because they’ve sold stuff, no. Hitachi executives are bonused if we meet our green goals. So there’s a complete mindshift in us as a company. So that changes everything. The first thing is when we say we want to do energy audits and we’re looking at emissions inventories and data center monitoring, this is key to who we are as a company, and that’s really important. We’ve made a massive shift change as a company, but we’re not talking about that. For customers listening to this podcast, what’s their approach and how can they make that change?

Where we’ve seen value is, look at efficiency improvements, energy efficiency improvements. Now, we’re very biased. We have our own power generation company, which not many people do, but it means that I can go, hey, where’s the best renewable energy at the minute from the grid to be able to supply us in Hitachi Vantara? Equally, we can offset that with carbon offsetting that we’re doing with Hitachi power grids, for example, where they’re investing in green technology and projects that reduce or capture emissions. There’s a lot of thought process, carbon offsetting is a very sensitive subject because a lot of the time it’s, hey, we’re going to plant a tree for everything. It’s outsourced to a third world country and there’s corruption, there’s issues, and those things never happen. There’s issues around that. But again, it’s finding the actions that you are taking, engaging your employees, and getting them involved.

We know that this thought process and the value of sustainability is key to everybody. This is for us as a collective. So get people involved, get them helping, get their ideas together, because they may see something because doing it every day that you just never would. And I think that’s really important as part of your employee resource groups, get them involved. And then longer term, look for where I can get more environmental certifications. We know that pursuing environmental certifications, such as ISO 14001… can we demonstrate regularly that we have this commitment to having our solutions and products certified externally by a third party, it just validates the efforts that we’re doing. And continuous improvement, just make sure you have a cadence of improvement and adjust the strategies accordingly.

Laurel: So as you mentioned, many companies have their own ESG goals and commitments to reducing emissions, but some differentiate between becoming carbon free internally with operations by a certain deadline like 2050, and then they would become carbon free throughout their entire supply chain at some other later point in time. Could you describe why this gap exists and what changes enterprises face when trying to decarbonize their supply chain?

Ian: Well, this ties back into our first comment, and I discussed that. Difference of the circular economics and Scope 3 emissions, and this really is a wide range of indirect emissions. So this isn’t power that I’m buying, this is the power that my suppliers are buying. And it’s really difficult to get transparency, and that’s why someone like Hitachi Vantara has made it our mission, that we make ourselves exceedingly transparent and try and measure that in a way that then we can pass on our customers. So the gap here is, a great example would be, I’m a vendor. I talk about sustainability in regards to how much power I’m saving you daily, but, and here is a but, I manufacture that box in China where it’s coal-fired and I’m using typically high CO2 processes to manufacture the metals and materials I’m using in my storage platform.

I’m not going to tell you that because I don’t want to tell you that. And that’s where it starts to become difficult, because I need that vendor to be open and wanting to share that information with us. By contrast, if I say by comparison, I manufacture in somewhere with more renewables in Asia and I’m manufacturing somewhere where I’m conscious of where my supply chain is in regard to my materials, we’ve done some analysis on what we do versus in Japan versus some of the others in Asia. And we can see the boxes we’re producing have 38% less CO2. Now that’s before I’ve even turned it on. If I put it side-by-side to the one that’s manufactured in that coal-fired environment for power and manufacturing, that’s 38%. The efficiencies of running the box, whatever, when you recycle it, there is still 38% less CO2, because of that Scope 3 emissions of where it’s manufactured, how I am choosing my metals.

So this is why this gap exists, because actually I’m going to be carbon free from my operations. That’s my decision. But carbon free from the supply chain means I need to choose the vendors that are going to be able to help me achieve that. And the vendors themselves need to change to do that. It’s really fascinating. I mean, push your vendors, ask them and say, “Look, I’ve got to get to this. I’ve got to get to this point. I may not have a decision from my executives today, but I need to get there. I need to know where you’re manufacturing. Where are your materials coming from? How are you shipping that? Are you using last mile electric solutions to deliver? Recyclable packaging?” All these things matter in regards to the overall carbon footprint to a product, and also getting to that carbon neutrality from the supply chain to the customers.

Laurel: Well, that transparency certainly helps when choosing a vendor to work with. What are some other kind of tangible changes that companies can invest in to make their hardware and infrastructure more sustainable and environmentally friendly?

Ian: So those sort of changes can really help reduce energy consumption. So we’re getting into the efficiencies of data storage and data management. So to lower the carbon emissions and minimize that impact of IT ops, you really need to understand the data you’ve got, first of all. So understand you could put the latest and greatest storage solution in, but actually if you’re storing stuff that you just don’t use or has no value to your company, what’s the point? You could half what you’re putting in there and save even more. So there’s this element of understanding what you’ve got today, and understanding its value to your business. That’s really key. Once you know that, now you can say, that gives me efficient hardware. I’ve got my data efficient hardware. And also choose stuff that is energy efficient, upgrade to energy efficient servers, storage devices, networking equipment. Look for products with Energy Star ratings or carbon footprint for product ratings.

Continue that journey of virtualization and reducing overall hardware footprint in your data center. The second is cooling. A lot of the cooling we see, and certainly being from the United Kingdom, I don’t necessarily need to cool my data center as much as say someone would need to do in say, Arizona, because the ambient air is typically cooler, but there’s more we can do with liquid cooling. There’s a great article recently of an MSP [managed service provider] in the United Kingdom that took over a, I don’t know what you say in the United States, but a swimming pool, what would you call that? A leisure center?

Laurel: Yeah.

Ian: Yeah, cool. Okay. But they’re actually heating the pool with their data center so they get free cooling and they’re charging people to come in and enjoy the swimming pool. And I was like, this is genius. That’s real social engineering around carbon footprint, and I think it’s going to need more. I mean, that’s a very extreme example, but that clever energy management and temperature management is really exciting, and that essentially results in that kind of greener data center. Look, and I’ve mentioned exciting things there, but really this is all about monitoring, reporting, understanding what you’ve got, making sure that you’re getting your employees engaged. These are the key things that are going to make an impact quickly to your business.

Laurel: And those are sort of the basics that you kind of have to do first, right? Understanding what your data is and where it’s stored. But are there emerging opportunities for data storage and management technologies that can help improve efficiency?

Ian: Do you know what? There’s so much going on at the minute and these innovations are going to help reduce the carbon footprint, but we’ve got to be really careful. There’s technologies that are coming out like, if I, for example, compare NVMe [nonvolatile memory express] as a storage technology to SAS [serial attached storage], where I have SSDs as NVMe or SCM drives as we also have as flash drives, they actually consume a third more power than our traditional SAS SSDs. So when you’re putting in these, hey, I’m going to go all NVMe and it’s all exciting and it’s super fast, that actually could have a massive impact on your carbon footprint. So think about using the right technology that’s right for you. Don’t necessarily tick a blanket box and say, this is going to be everything. No, be more granular on what you actually need and the performance you need for your data center.

And that then moves into the technology piece. You want to look into the data compression deduplication, which is very much a table stakes technology these days. Everybody has something and algorithms to reduce data are fairly common, but you need to use that on more data sets than you do necessarily today. And equally, we need to be able to, from a technology perspective, actively switch from say, inline compression to post-process. So for example, when there’s tons of going on and there’s loads of data storage, I don’t want to have an impact on performance so that when I’m writing data, I’m compressing it in line and I’m dealing with it, amazing. But actually when the array is not busy, I want to be able to switch to post-process, save some power. And the same can be true for the CPUs themselves.

Rather than, say we talk about overclocking over the years, we need to underclock those CPUs. Make them slower, because if we’re making them slower, they’re going to consume less power. But equally, I want to be able to turn them on and make them fast as and when I need that without any impact to my company and my business. So this idea of taking technology that’s inside our solutions today and making them dynamic, making them have the ability to reduce their footprint all without any impact to customers is so, so important. Look, there’s much more than that going on. There’s tons around DNA storage and other things, which really is the next generation. And I think that’s going to again, fundamentally change this conversation entirely. But actually what we’re seeing today is how can we take the technology we’ve got, make it dynamic, make it accessible to align to your data management practices and sustainability goals.

Laurel: And Ian, just quickly, what was that acronym, MBNE?

Ian: Oh, NVMe.

Laurel: Got it, NVMe. And could you explain that, what that is to us?

Ian: Yeah, sure. Non-volatile memory express. This is a language that we use for talking about storage and apologies. We have SAS as well, serial attached storage. This purely is, they’re a language, they’re a way of talking to a type of media. And NVMe is the latest language that we have, but what that means is, that means we can have even faster flash drives. Fantastic. We’re talking with a flash language, but it uses more power. So great, I’ve got something faster. But that could mean that you’ve put the latest technology in and you’ve consolidated. Why is my power consumption higher? So the key things to take just because it’s the latest and greatest technology doesn’t necessarily mean that it’s going to lower your carbon footprint.

Laurel: Well, and I think that’s a good analogy for what we may have in our own homes with dishwashers or washing machines where it’s now this longer ecocycle. Yeah, that makes sense. So in addition to demands for reducing carbon emissions among enterprises, there’s also demands for more immediate and transparent data across to run business applications and power AI and machine learning tools. I mean, we haven’t even touched on that. We know that’s such a great demand. Every other conversation is about generative AI. So how do you meet those demands for greater data access, while then mitigating environmental impacts?

Ian: It presents a challenge. How do we do that while mitigating environmental impacts? I’ve spoken about start somewhere, make small changes, but then we say, well, generative AI comes in and I need to replace all my servers with the latest and greatest, and that’s a huge carbon issue. How do I make those changes sustainable? Well, it’s about employing different strategies and balancing them. What’s right for some might be different for others. An example would be, as I mentioned before, optimize your storage and retrieval, employ advanced data management techniques, understand where you’re storing it and how you’re retrieving it, how you’re tiering data along with compressing data, deduplicating it, caching strategies. Understand your data lifecycle and process of where you are storing it. And I don’t mean actually the data itself. Sorry, I mean, like I say, an application itself. I’m talking about the physical data and how you are moving that through its lifecycle in your data center.

Look at the edge. There’s lots in the edge. Data’s coming in at the edge, and how do we use that to really process data closer to its source? This is very much more efficient and reduces the need for transmitting large volumes of data over long distances. So minimize the network latency and use and energy consumption by doing much more at the edge of where you’re receiving data. Different for customers, depending on the apps they’re using. Different industries mean they need different things, so take that as it applies to you. Look at your cloud computing. A lot of the data centers that the hyperscalers use are, what’s their CO2 impact? What’s their footprint? I don’t think there’s enough clarity there right now. So how can you actually on-premise, let’s say, I’m in the Nordics and I have a data center there. Well, that’s 100% renewables.

So I have a huge savings by saying, let’s say I’m not going to run it in the public cloud, because I can run it on-prem. So again, that balance, understanding where what you have is really key. But finally, understand that AI element. What can the AI and machine learning algorithms do to dramatically allocate resources based upon workload demand? Make it so that you only utilize it when you need it rather than actually taking resources and then sitting on it. And that balance, that need for kind of immediate and transparent data access with that environmental sustainability in mind delivers a real holistic approach. So look at data flows, adopt energy-efficient technologies, and that’s going to really align your data processing capabilities with those goals.

Laurel: So thinking ahead, what are some of those trends in relation to data management that you’re thinking about and anticipate enterprises will approach to reduce their own carbon footprints while still being able to deploy all these advanced technologies and innovations?

Ian: Gosh, yeah. That’s such a large question. Let me try and summarize. I think there’s so many trends in data management and sustainability is almost like a cloud that’s hanging over them because people think, “Oh, how am I going to do this and meet those goals?” It shouldn’t be seen like that. The focus should be on sustainability and how do we align our strategies to that. So again, start somewhere. Understand the power that you’re using. Start with some level of reporting, understand what you’re using. And there’s going to be more coming from vendors to provide software to give you more metrics and reporting. That’s really key. Stakeholders will expect clear information on carbon emissions and energy usage. That’s absolutely key. The second has to be renewable energy procurement. So make sure that you are investing in renewable energy sources. IT typically doesn’t have control over where power is provided, but that includes onsite generation too.

They may have backup generators. Are they generators or are they using PDUs [power distribution unit]? So power purchase agreements, renewable energy credits, and how you’re actually using that for backup power as well really, really matter. And again, just that element of energy efficient hardware. As you’re looking to invest, ask the difficult question, well, where are you producing this? Where is it manufactured? How are you shipping it? These are really, really difficult questions. And oh, by the way, I want to see how you’re doing that. Actually, I want to see you certified externally to meet those goals. And all that sort of collaboration around supply chain sustainability will really help. Understanding how you’re sourcing and responsibly manufacturing becomes integral to that data management strategy. And finally, just really innovation and research, the ongoing innovation and research and how we’re using technologies within data solutions to actively and dynamically turn on features and turn off features with complete transparency to you as a customer.

I think that’s so important. So you need to adopt a multifaceted approach that combines energy-efficient infrastructure, renewable energy sourcing, optimize the data management practices, but commit to that transparency and sustainability reporting. The environmental concerns will continue to grow, and these trends will play a critical role in shaping the future of data management.

Laurel: Well, Ian, this has been a fantastic conversation on the Business Lab. Thank you so much for joining us.

Ian: Appreciate it. Thanks a lot, Laurel.

Laurel: That was Ian Clatworthy, who is the director of data platform product marketing at Hitachi Vantara who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the global director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

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