The Need for Standardization in Sustainability Reporting

In the world of sustainability, having flexibility to report on various aspects of an organization's environmental and social impact is crucial. However, this flexibility can sometimes lead to inconsistencies and comparability issues when it comes to comparing one company's performance against another. The lack of standardization in sustainability reporting has been a long-standing issue, with many organizations relying on voluntary frameworks and regulations that vary from industry to industry.

The importance of standardization cannot be overstated. If every organization were to create its own framework for measuring sustainability, it would be challenging to compare their performance. For example, if two companies were to report their earnings in different ways, it would be difficult to determine which company is doing better. Similarly, if there were no standardized way of reporting on environmental impact, it would be hard to track progress and measure success.

To address this issue, governments have introduced regulations and guidelines that provide a framework for sustainability reporting. These frameworks provide a common language and set of metrics that organizations can use to report their sustainability performance. For instance, the Corporate Sustainability Disclosure (CSDR) is a framework that provides guidance on what information companies should disclose about their environmental and social impact.

The need for standardization in sustainability reporting has been identified by many organizations as a top priority. In fact, some companies have made commitments to achieve 100% renewable energy powering of their data centers by 2025. This commitment is not only good business practice but also a critical step towards reducing carbon emissions and mitigating climate change.

The role of technology in addressing this issue cannot be overstated. The use of artificial intelligence (AI) and machine learning (ML) algorithms has the potential to transform the way sustainability data is collected, analyzed, and reported. For instance, AI-powered tools can help identify trends and patterns in large datasets, providing valuable insights that can inform business decisions.

One company that is leveraging technology to address this issue is Microsoft. The company has made a commitment to power all its data centers with renewable energy by 2025. To achieve this goal, Microsoft has developed an AI-powered tool called Sustainability Manager. This tool uses ML algorithms to analyze large datasets and provide insights on sustainability performance.

Another benefit of using AI in sustainability reporting is the ability to ask questions of existing data sets. For instance, a company can use a tool like Co-Pilot to generate information from its existing data set. This allows companies to identify trends and patterns that they may not have noticed otherwise.

The development of ESG (Environmental, Social, and Governance) data models has also been instrumental in addressing the need for standardization in sustainability reporting. These data models provide a common language and set of metrics that organizations can use to report their sustainability performance. The ESG data model is one such example that Microsoft has developed to help companies generate high-quality ESG reports.

The growth of sustainable consumption and production is a top priority for many organizations. As consumers become more environmentally conscious, they are demanding that companies demonstrate their commitment to sustainability. In response, companies are investing heavily in sustainability reporting and disclosure practices.

Large Language Models (LLMs) and Generative AI are also being used to address the need for standardization in sustainability reporting. LLMs can help analyze large datasets and provide insights on sustainability performance. They can also generate reports from existing data sets, making it easier for companies to identify trends and patterns that they may not have noticed otherwise.

The role of LLMs in sustainability reporting cannot be overstated. These models can help organizations answer questions about their sustainability performance and generate high-quality reports. They are particularly useful when dealing with unstructured data, which is often the case in sustainability reporting.

In conclusion, the need for standardization in sustainability reporting has been identified by many organizations as a top priority. The use of technology, including AI and LLMs, can help address this issue by providing insights on sustainability performance and generating high-quality reports. Companies like Microsoft are already leveraging these technologies to achieve their sustainability goals.

Sustainability Data Solutions on Fabric is another initiative that aims to provide companies with the tools they need to generate high-quality ESG reports. This solution provides a framework for sustainability reporting and disclosure practices, making it easier for organizations to measure and report their environmental and social impact.

The benefits of using Sustainability Data Solutions on Fabric cannot be overstated. These solutions can help companies identify trends and patterns in their sustainability performance, providing valuable insights that can inform business decisions. They also provide a common language and set of metrics that organizations can use to report their sustainability performance, making it easier to compare their performance with other companies.

In the future, we can expect to see more emphasis on standardization in sustainability reporting. The growth of sustainable consumption and production is a top priority for many organizations, and companies will need to demonstrate their commitment to sustainability in order to attract customers and investors.

Overall, the need for standardization in sustainability reporting has been identified by many organizations as a top priority. The use of technology, including AI and LLMs, can help address this issue by providing insights on sustainability performance and generating high-quality reports. As companies continue to invest in sustainability reporting and disclosure practices, we can expect to see more emphasis on standardization in the future.

"WEBVTTKind: captionsLanguage: enthe result from a kpi perspective was incredible I mean they went down to a reduction of less than 1% of food waste and from a starting point of an average what we're seeing in their industry which is about a 15 to 40% um food waste all right everyone welcome to another episode of the tmall AI podcast I am of course your host Sam charington and today I'm joined by Andres ravinet Andres is sustainability Global black belt for data and AI at Microsoft before we get going be sure to take a moment to hit that subscribe button wherever you're listening to Today's Show Andes welcome to the podcast thank you Sam thank you I'm excited to have you on and for our conversation let's start out by having you share a little bit about your role at Microsoft sure sure thanks and also thanks for pronouncing the name correctly I know it can be difficult uh yeah so I'm a sustainability uh Global black belt gbb at uh Microsoft that focuses on our Solutions under our Microsoft for cloud for sustainability platform and uh yeah the gbb is a very uh funny name that we have at Microsoft that really just helps customers understand that we're a d Technical and Industry expert within a specific either a solution area or areas and specific Industries and so what I do really is my day-to-day is supporting customers on their sustainability Journey as it relates to leveraging our different data Andi Solutions especially under our Cloud for sustainability awesome awesome and when you talk about sustainab as it pertains to your role and what you're seeing at customers what all does that entail sustainability is a is a lot it's it's it's it's like a multifaceted concept I think there are different definitions I think generally we like to think about it as the concept of addressing the needs of the present for everyone without compromising future generations and the needs that will be met then you know what really that means is just really everyone from an individual to families to communities cities countries and organizations trying to understand the impact that they have uh from three perspectives environmental care social well-being and economic growth and trying to create a balance through practices or strategies to adhere to those and and set commitments and practices in place to kind of address that and what's your background did you have you always worked in sustainability yeah I have quite the Jack of all trades background so I'll start from the kind of from the now I'm actually new to role not to Microsoft but to this role um I think it's been like three months already um but I've been with Microsoft for six years and in that time I worked for a small part of the organization called the Microsoft Technology Center as an technical architect a cross solution technical architect and so what we did was um we met with hundreds of customers a year in our New York facility where we would just help them ideate strategize and then build solution architectures around various different solutions that we have in helping them address their business objectives and challenges and since the release of our platform and our commitments I was immediately drawn to being able to talk to this because I've I've I've had a passion in this area for a long time and so it was just so surprised one on our ambitious commitments and two that we actually came out with a platform that we can help customers leverage and so for the last two years I've been speaking to customers about that in our MTC locations and virtually around the world uh since we had a bit of an interruption there I finally was able to move into a role thankfully at Microsoft that is dedicated to this space so now I spend my entire time just talking to customers in this space and before then then very much of a technical background in Building Solutions around virtualization uh Cloud technology cyber security Consulting it actually have served me well in having like a multi- solution or multifunctional architectural conversation and I've just been able to use that same kind of drive and system thinking into sustainability and how to kind of look at that holistically for a customer and how to think about solution architectures for customers when you think about sustainability what are some of the the big threats that sustainability as a field of practice seeks to address I mean climate change is is one but I'm imagining it's one of a number that folks are trying to address with their sustainability initiatives so when I when I think of these big threats I I immediately think of like the large ones that are interestingly enough interconnected and so it's things like climate change and its impact on the increase to extreme weather events to the collapse of biodiversity and the impact to us and as well as the shortages of Natural Resources right and just to give you some examples of like some of the gravity in terms of some of the facts related to around this right it recent Studies have found that you know our negative contribution to the environment is a large contributing factor to the extreme weather events and what's unfortunate it is that you know when we think of transitioning we have a this idea of a just transition to kind of moving towards sustainable practices those who are were the least in contributing to this are the most affected by these sorts of events and and these risks the other one is in terms terms of wildlife population we're seeing a significant decrease in Wildlife population especially in South America South America seeing like a 94% drop in Wildlife population and and that again has a domino effect to the domino effect right in terms of the ecosystem you know if you think of like the pollinator population as an example bees for one right that's being affected dramatically and when you think of what happens when the decrease of population in pollination begins you know we see a direct effect to the global food uh Supply 75% of our Global food supply is reliant on pollinators so these are large large grave sort of facts that we have to deal with and so that kind of sets the stage you know for a lot of strategies that the world or organizations governments right are looking to populate from reducing greenhouse gas emissions and moving towards renewable energy sources to adopting systems thinking to like the idea of the interconnectedness between nature to protecting habitats and ecosystems and to moving towards more regenerative and sustainable practices around Industries like fishing mining Agriculture and so at a techn level right when you when we think about trying to address these it's really not a new strategy it's collecting data and the fact that there's a lack of data right and then having to kind of bring that in right do some analytics on it it's kind of that old adage of right like I can't manage it if I don't measure it right and so right now you know we're seeing that as a big big gap in the industry is for or for anyone for anyone trying to set commitments is collecting data to understand what they can do and how to monitor progress towards those commitments let's dig into some examples of how AI can be uh applied in some of the diverse areas that you just spoke about one example uh that's a early warning system early warning for all can you tell us a little bit about that initiative and what it's aiming to accomplish yeah the United Nations put together this initiative called the early warning for all and the goal was or at least their their commitment was they're looking to protect all humans on Earth from hazardous weather events by 2027 and so to help they started ideating well what can we do with the data that's available out there in the world as it relates to what could help in being able to advise uh communities around upcoming extreme weather events and so a couple thoughts were well how can we use satellite imagery meteorological data as well as real-time data streams like social media and put that together and so we worked with an organization called seeds which stands for sustainability environmental and economical development Society H to help build an AI model called Sunny lives and the sunny lives model was uh trained manually by a number of data scientists on 50,000 images of rooftops and the idea was how can we build a model that one can identify the different rooftops of specific buildings and categorize them into seven types and then as well correlated to different geographical factors like bodies of water or elevation and so with the result of that they were able to use this model in being able to personalize the advisories to specific governments and specific populations of people or communities of people on upcoming events as well as helping nonprofits and government agencies essentially disseminate resources ahead of time when we when they detected an impacting event and it was timely there that by identifying or classifying these different rooftops they can characterize the the building practices in general and how susceptible they are to different levels of storms this was necessary because of a lack of urban development in a lot of these areas and two it was also just to ident so to your point it's a yes and as well as identifying where there are communities of people living versus where there has not been and so just understanding like distribution of communities of people in certain areas so it turns out it was timely right after after piloting it and putting it into production in 2021 Cyclone Yas was nearing the eastern states of India and with the model they were able to identify the populations at at risk or the communities at risk and be able to send personal advisories immediately to over a thousand families and and and helping government agency as well as distribute resources appropriately and efficiently like into those different areas so it turned out to be a great project and it's still in place today and and really helping the areas that that need it right as I mentioned you know those are the ones that are most affected by extreme weather events that's interesting you mentioned that there was some custom model develop Vel mment involved here was this project a research project or research in partnership with other organizations or was it more a case of organizations using off-the-shelf tooling to to create that model it was definitely not off-the-shelf it was a model that had to be created by a partnership between that seeds organization and ourselves there was no model at the time that would help in this specific use case so it was very much a partnership where we supported them from a technology perspective and a Consulting perspective on what technologies that could be leveraged and then helping scale it you also mentioned one related to food waste can you talk a little bit about that use case yeah food waste large large problem so pervasive issue right now affecting both not only the environment but the economy right un reporting 931 million tons of waste of food annually right that amounts to about 177% of all food available to Consumers you know and so lots of organizations are thinking about you know how can we help address this due to that that that sort of two- faceted result right and it's all across the supply chain for food an entrepreneur years ago who was trying to tackle this in restaurants and hotels and the approach was like putting a camera over the um the trash can and using that to develop reporting that fed to management about how food was used and wasted so that they could do things like tweak portion size and ordering and stuff like that it's such a complicated issue because there's so many hands passing right food right when you think of the supply chain and every hop something has to take last I checked the the majority of food waste results in in before it is received by grocery stores and so yeah grocery stores have the ability in starting to take measures like using either image or video recognition and being able to tie that to some process that can hopefully you know optimize their purchasing process but on the supply chain side they're trying to understand what they can do in sort of demand forecasting optimizing their processes and that has been traditionally something that hasn't been done in that industry and so a lot of organizations are taking different approaches on how to address that yeah there all these different you know when you think of a value chain for a specific food item there's arguably countless of of companies involved from the beginning to the end of the life cycle of food and so lots of organizations are trying to understand what they can do on their part but as well as you know interesting enough we are seeing more of requirements for customers Upstream right so if you think of a grocery store a lot of grocery stores are starting to dictate data related to kind of the processes that they have of you know what their waste profile is um what their environmental emission data is what their uh code of conduct is you know from an ethics and social responsibility perspective so we're seeing a lot of that from a requirement standpoint at the at the private level but also at the government level we're seeing regulations sort of dictate the um disclosure of information like that I was going to ask about the specific food waste project that that you mentioned we have a customer called LMK group they're a Nordic food kit delivery company and when you think of that food waste problem we're talking about right there is a player throughout that entire supply chain they were looking to understand you know what can we do from a demand forecasting side a supply chain optimization side and how to start thinking about personalizing customer experiences these aren't new you know strategies right I think they are new in terms of the outcome right so they're thinking about it from the sustainability lens versus yes arguably optimizing efficiency right in their processes the end result is great because if I could reduce waste I mean that's that's better for the world the thought was okay so how do we develop machine learning models that can essentially predict customer orders with accuracy right and so what they did was one tried to build a model that allowed them to bring in uh not only historical data but real-time data to forecast demand the goal was to help them provide suppliers precise predictions right you know up to 10 weeks in advanc you know when when they started in this process you know the challenges you know are not dissimilar to other use cases right you know I need to integrate data in disparate sources and different formats I need to um build machine learning models on some platform that can ideally scale it we help them with our Azure machine learning platform to help build this model that allowed them to bring in that data and allow them to bring in different factors like seasonal changes and cons and consumer preferences and again bringing from other external sources to essentially correlate this data they were successful in building this machine learning model they used a variety of different algorithms from time series analysis to regression models to essentially help in forecasting the future demand the result from a kpi perspective was incredible I mean they went down to a reduction of less than 1% of food waste and from a starting point of what from a starting point of an average what we're seeing in their industry which is about a 15 to 40% um food waste at least in their part of the supply chain right um as I mentioned the majority of this is is further Downstream in terms of the impact of food waste but you know from a pattern perspective in terms of you know how AI can be leveraged here the the challenges are seem to be the same except I'm finding that customers that are in the realm or in the responsibility of sustainability with an organization we're finding that this is new to them new to them that they can apply AI as a tool to address these issues or the the challenges themselves are new what aspects are are new so what's new to them is what can be done with AI and machine learning models or what needs to be done um with the leveraging of AI Technologies and machine learning models um so tendentially we're finding a lot of cross collaboration between this new aspect of a bu this new part of the business and technology teams it sounds like the technical process here was first identifying this Challenge and then the application of you know what I might call traditional data integration techniques to kind of get all the information about their supply chain in the right place and then imagining like traditional ml models as opposed to you know anything fancy yeah sounds like kind of a bread and butter type of problem that that you know I think of it as like unsexy AI sometimes like it almost feels table Stakes yeah yeah right it's just a new yeah business objective new outcome it's Ai and and these models have been so publicly available now you know it's such an interesting time to have access to all of this and really easily deploy this and so thinking about well how do I build one from scratch you know and do I even need to build one from scratch right and how do I get started like what are these platforms that we can use to get started here and what are these data sets that I can leverage there's so much available out there you know I look forward to where this becomes as a mature Ai and ml use case as it is for other aspects of a business like from Financial operations right to market trends to other things which arguably is sexier it's it's really nice to start seeing this part play a role here want to make sure we cover really interesting use case that is focused on conservation particular in the Amazon can you tell us a little bit about that one yeah yeah that one I'm I'm I'm pretty passionate about it's it's a pretty cool use case you know kind of to set the stage when you think of the Amazon rainforest it's the largest rainforest in the world it spans nine countries and you know not many people know this but you know it's absolutely crucial to keeping the balance of the ecosystem it is one of the largest carbon removal entities in the world it it as well it's kind of like what you were referring to earlier just the interconnectedness of yeah all of these systems and that is a a huge piece that plays a significant role globally yeah it I mean you know on the bleeding edge side a bit off topic but a lot a lot of tech companies like ours are investing in other companies who are looking at Carbon removable technology because it's needed but we've got like free Carbon REM removable technology right now in the form of biology you know and and it's at risk and on top of it being one of the largest carbon dioxide removers in the world it also dictates weather patterns and so as as it depreciates or as it's infected impacted you know that dictates the weather patterns around the world and this is not a new problem right deforestation but it has been increasing and so what we did was collaborate with our research teams and a number of different universities and agencies uh down in South America to put together a project called project guacamaya and and bakaya means MAA like the bird MAA in Spanish and so the idea was okay well how can we start addressing the detection of deforestation trends that are illegal and so the idea was can we start using satellite imagery analysis camera trap Imaging analysis as well as bioacoustic analysis to essentially monitor the rainforce S Health and biodiversity and so again from a challenge perspective it's a very interesting one because it's such a vast geographical area right and we're having to start implementing realtime monitoring and then on top of that the complexity of analyzing multimodal data right so you think of imagery from a photo perspective imagery from a satellite perspective now we're talking bioacoustics right how do I put that all together and so on the satellite front right we all started leveraging the planet Labs high resolution satellite imagery and what we did was we worked through those photos on a daily basis to identify signs of deforestation and illegal Mining and the AI model that we built helped track the changes over time and then detect specifically unauthorized roads and the reason to detect unauthorized roads is because that typically often preceded deforestation activities on the camera trap analysis side right we had C there were camera traps across the rainforest that were able to take photos on detection of movement and so thankfully in that scenario there was a model that could help here called the mega detector and what it did is it helped filter and classify images of what they called bioindicators which is essentially a a way of saying you know species whose presence or absence might indicate an ecological change right and then lastly on the bioacoustic monitoring side which I thought was pretty fascinating the idea of capturing sound specifically animal sounds like birds and differentiate between the different species to identify either migration of species away from a specific um area or the introduc the introduction of an invasive species into an area all this being put together into an open source model now so now not only can they leverage it for the initiatives in the Amazon rainforest but now the hope is that it's going to be um adopted by other parts of the world with ecosystems like that that could be affected that could leverage such a model the model created on the bioacoustic side is an interesting one we'll drop a link to the paper into the show notes the end result is this contrastive language Audio pre-training model that is a multimod model that aligns the audio with textual information to help enhance the process of identifying particular sounds uh really interesting uh on the research front as well as the practical application here you know we we we should and we have kind of taken it for granted that this is something addressing sustainability is something organizations should do but are there particular drivers that are prompting organizations like Microsoft and the customers that you work with to pay attention to these things now I know government initiatives for example are a big part of that you know how do you think about the the drivers yeah I mean the first one I was going to mention was the first one you mentioned right I think that's one of the biggest drivers uh unfortunately that's that's sometimes what it takes is kind of strict government regulations to have people move towards progress so yes we are seeing stricter government regulations as well as industry related agencies that have regulations we are starting to see Rumblings of that here in the US with a regulation out in California and one coming from the SEC these are all looking to essentially collect or require collection of environmental data uh specifically around scoped emissions from a terminology perspective we have different types of emissions right you can think of carbon emissions you can think of the use of water to waste to biodiversity loss emission data for scope one is really the direct emission of an organization direct operations scope two is emissions related to their utilities so things like consuming electricity gas heat Steam and so forth scope three is the most complicated one there are two aspects to scope 3 scope three from a downstream perspective is everyone in your supply chain all the emission that they emit on your behalf is your scope three emissions then there's Upstream if you create a product and the use of that product as an example Xbox right every time someone uses an Xbox that's scope 3 Upstream emission and so how do I start considering collecting that data and reporting that that is by far the most difficult one but yes so we're definitely seeing stricter regulation from governments but as well as consumer demand shifting so we are seeing more and more customers uh looking to do business with those who are actually being responsible as it relates to sustainable business practices and that is why a lot of people are starting to publicize their commitments around sustainability from an operational perspective right this also is about cost savings right so cost Savings in terms of reducing waste and energy consumption it's also a smart risk strategy right you know addressing issues like climate change and resource scarcity the other is really around like Corporate social responsibility doing good right making sure that you're contributing to the social good and environmental stewardship right Microsoft specifically like thankfully we are in a position to do more to help with climate change um and we believe we should I mean it's built into our mission statement our mission statement is to empower every person and organization on the planet to achieve more very different from our original putting a a PC on every desk on any on every home right but also like the reality is you know we do better as a business when economies around the world are growing and doing well so we don't do well if our customers are not doing well um and I'd also be remiss to to kind of not also bring up our commitments in this space uh as well right we have been in this space for quite a while now we became carbon neutral in 2018 and a few years ago we set the most ambitious commitments by 2030 we are looking to be carbon negative zero waste and water positive by 2050 we are looking to remove all historical emissions since the beginning of Microsoft in 1975 and so these are Big commitments are part have enough positive impact that undoes essentially the historical emissions yeah through a combination of reducing our environmental footprint and through the invest into carbon removal agreements yeah and so lots of drivers nowadays far less are those doing for the sake of doing good more starting to do more because they're being forced to do more from either the government perspective or consumer demand perspective along those lines I recently spoke with one of your colleagues lauron benois in a conversation around Ai and power and energy and one of the key topics that came up was the growing need for power to drive data center growth and a big part of that was driven by AI workloads how does you know Microsoft being kind of central to this growing use of power around AI get reconciled with a drive towards sustainability and being responsible in using resources this this is a massive challenge that that's in front of us right the idea of having to decarbonize the delivery of AI to the global economy and we are seeing the reality of those of that through our numbers uh we've just recently released our sustainability report for 20123 and this has been one of our challenges and we are committed to a full stack end to end approach to tackling this problem right this is this is really a once- in a-lifetime opportunity for us and it's arguably the biggest bet the company has made we have outlined a thorough approach but just to give you some insight on you know some of the strategies that we have in mind right I think firstly it's about improving the Energy Efficiency of AI itself right so the code and chips that complement the centers right so we can do more with less right designing AI models right to run more efficiently exploring small language models like arar models as well as hopefully building a momentum for green software engineering principles that can be applied to First and thirdparty AI applications another strategy that we have implemented is how can we continue to optimize our data centers right to essentially minimize the use of energy and water and part of this is and is is you know comes from the the idea of like well what are those upfront design choices that we have to make to figure this out and so ideally it's about building as few data centers as possible to meet the demand and then operate them as sustainably as possible our data center team like really can come in from an Innovative perspective because they asked the big questions like can we cool data centers without using water yes right so we are able to use EV evaporative cooling uh technology in temperate climate data centers right this it's called ad arotic uh Cooling and using air cooled chillers in warmer regions right this hopefully eliminating the use of water from cooling this is the exact same strategy we are using to move in building all of our new AI data centers moving forward another example of kind of what we do with our what we're doing with our data centers is kind of reimagining not only as consumers of resources but kind of integral into the ecosystem so an example could be what we've done in Sweden and Finland with our First sustainab Data Centers where we're actually recycling the waste heat to provide the community of that area heating during the winter what we can't reduce we have to remove as I mentioned for carbon we need to remove carbon in terms of water we need to replenish water so as much as possible we are coming up with tactics and strategies and putting together an Investments on on uh initiatives and companies that are focused on on carbon negative Technologies and water positive Technologies so in talking about the kind of the scope of the challenge for an organization and the degree to which it's highly interconnected maybe we can jump into the groupo bimbo use case as an example of how an organization tackles the reporting challenges associated iated with ESG compliance yeah and and also to set the stage right you know ESG a big acronym being thrown around this idea of environmental social and governance I get asked a lot like what the difference is between ESG and sustainability and so as I mentioned earlier sustainability is this broader practice right of meeting the needs of the present without impacting the future ESG right is really about how do I evaluate an organization based on specific metrics around environmental data social data and governance data a lot of quantitative and qualitative data this is mainly dictated by regulation so help them asking for disclosure of this sort of data to evaluate the profile of the organization to investors right understanding from their point of view what their their commitments and impact and progress towards them are and so we're seeing very much organizations start to think about okay what do I need to do to one Define commitments that I can publicize and then start tracking my progress towards those commitments and at an atomic level it's really about okay well I need to collect environmental data I need to connect collect Social and governance data which is typically in HR systems Finance systems Erp systems right and so how do I bring that together how do I put that ideally in a model of sorts something that can understand an ESG relationship and then how do I build Downstream uh reports and dashboards that can help not only my internal stakeholders but external St stakeholders allowing me to then help in my disclosure in my auditing of specific regulations so groupo bimbo is a great example because they set some pretty ambitious sustainability goals and for those that aren't familiar with group they're like one of the largest Bakery manufacturers in the world they're probably most well-known to us here at least in the US verar Lee and timans you see them at the end of the aisle in groceries you know you try to bypass them but sometimes you can't help it and so uh it started off with again not a new use case they're they have a supply chain I'm looking to reduce costs optimize my production cycle and so lots of iot and OT opportunity here to collect that data in parallel the foresight was this is great that this initiative is moving forward to do this to collect this sort of data now let's also leverage it to start reporting on the environmental data so they were able to use our Azure Cloud to host the iot services necessary the data Lakes necessary and the data Factory Pipelines necess necessary to essentially allow for the ingestion of the data and dumping it into a Lakehouse that allowed them to carry out an elt process and extract load and transform process using these pipelines and then leveraging our Microsoft cloud for sustainability solution called Microsoft sustainability manager to take the environmental data and take it through the necessary emission calculations and then reporting it so helping them build not only customer report reps but helping them build reports for disclosure we are seeing a lot of companies with the same Challenge and desire is how do I collect all of this together bring it into a single model that can understand the relationships between these different data points do the necessary calculation and then be able to address my specific needs around reporting and dashboarding that's one of the biggest Investments we've made in our Cloud for sustainability it's not only the Sol solutions that accompany it from a data n perspective but the underlying ESG data model that we have that can help a customer out of the box not having to build their own ESG data model we've actually had customers come up to us with pre-built ESD data models and understand the Monumental effort that they went through to create them when we already had it and I may be overloading this but I'm thinking like a knowledge graph or some kind of graph of the relationships between servers that roll up into racks that roll up into Data Centers that roll up into you know overall it you've got products that have suppliers that have suppliers and you know just kind of all of the relationships between the various things that go into an overall sustainability profile is that the idea spot on I mean the relationships like when you think of tying together environmental data with like social data you know uh uh being able to track an emission for a specific facility and what business unit is it tied to and who are their employees that are in that facility do they drive to work to that facility there's so many different relationships you know between all of these different categories that people are looking to get insight on and be able to report from a voluntary perspective but also from a reg regulatory perspective MH it does seem like they're would need to be some standardization like it's great to have the flexibility to report on the various ways that you might see the problem but it's I'm imagining something analogous to Gap reporting and like Financial Services like if everybody chose their own way of figuring out earnings then comparing what one company's doing against another isn't all that helpful there's a similar situation for sustainability I'm imagining that's what the government regulations are about not just setting metrics and benchmarks but also standardizing at least a few of the key numbers that everyone should be thinking about yeah I mean in perfect world we'd have fewer because the way I think about it is every time a regulation comes out or some voluntary framework is being looked at to report to you're needing to build a specific schema that is mapped to the different Frameworks and so great we helped with the ESG data model right that's great we need help you're saying the problem is that there are too many such standards it's been a problem for different Industries security is also a an example of one of those there's just like hundreds and hundreds of different types of Frameworks and regulations that you have to map to right and so not a new problem a new problem to this industry again why we're trying to help with these outof the-box models the ESG data model is one we have another model that you can help then transform it to for the csrd that'll then help you report towards that specific framework this is with the help of our new data solution within that cloud for sustainability and that's called sustainability Data Solutions on fabric this is hopefully addressing the need uh for customers on putting together an ESG data state for them that can give them the way the Insight they need from an analytical perspective Ive on this data but as well as helping them through out of the boox disclosure reports be able to report to those different agencies as well as build their own customer reports and so what we've talked about thus far is again back to the the basic table Stakes that you mentioned earlier kind of traditional data pipelines and data analytics is there also a role for llms generative Ai and some of the newer technologies that are coming online large language models definitely being a a space here that is is a need and a place where we are investing in you know especially with the fact that in in the industry of sustainability we're having to not only read data uh in unstructured forms but also structured and so it's great that now we can kind of build these co-pilots to Target these structured data repositories and be able to ask questions on it and then generate information from it so very much seeing more and more in that space and then as well as what we've seen traditionally you know with the ideas of AI so things like anomaly detection outliers Trends analysis we have a capability in sustainability manager where we call it intelligent insights that we can take your existing data and you can give it some parameters in terms of helping you understand future States and future models and able to map that out for you so it's pretty exciting for this space to finally see something every demo we give where we're showing customers on how to create these reports from scratch using co-pilot to you know asking questions on existing data sets has been eye openening any parting thoughts in terms of where you see this all going or what you're most excited about I think what I'm most excited about is the increase in conversation I'm having with customers around this space whether it's because they have to or they want to or combination of both it makes me feel optimistic that customers are wanting to have this conversation with us or in general right with any other solution right that they're looking to identify how they can set commitments measure to measure progress towards them do good I think that this conflicting nature of AI and environmental consumption will be a very hot topic and I'm glad it is because we we are tackling it head first it is a top priority for us and you know if if history has told us anything with with Microsoft putting big bets on things like we'll do we'll do all right we're unwavering to our commitments as an example by 2025 the end of 20202 all of our data centers will be running on renewable energy energy and that's a big deal that's a big deal for us that's a big deal for the community and it sets the stage this is table Stakes for any Cloud company today here are the commitments here's what you should strive for and hopefully customers understand that so I see a lot of progress here but not without its challenges so I feel like there'll be bumps along the road but I'm optimistic that we're working together towards the same outcome awesome awesome well Andres thanks so much for taking the time to share a bit about what you're working on Sam thanks for having me really appreciate it thank youthe result from a kpi perspective was incredible I mean they went down to a reduction of less than 1% of food waste and from a starting point of an average what we're seeing in their industry which is about a 15 to 40% um food waste all right everyone welcome to another episode of the tmall AI podcast I am of course your host Sam charington and today I'm joined by Andres ravinet Andres is sustainability Global black belt for data and AI at Microsoft before we get going be sure to take a moment to hit that subscribe button wherever you're listening to Today's Show Andes welcome to the podcast thank you Sam thank you I'm excited to have you on and for our conversation let's start out by having you share a little bit about your role at Microsoft sure sure thanks and also thanks for pronouncing the name correctly I know it can be difficult uh yeah so I'm a sustainability uh Global black belt gbb at uh Microsoft that focuses on our Solutions under our Microsoft for cloud for sustainability platform and uh yeah the gbb is a very uh funny name that we have at Microsoft that really just helps customers understand that we're a d Technical and Industry expert within a specific either a solution area or areas and specific Industries and so what I do really is my day-to-day is supporting customers on their sustainability Journey as it relates to leveraging our different data Andi Solutions especially under our Cloud for sustainability awesome awesome and when you talk about sustainab as it pertains to your role and what you're seeing at customers what all does that entail sustainability is a is a lot it's it's it's it's like a multifaceted concept I think there are different definitions I think generally we like to think about it as the concept of addressing the needs of the present for everyone without compromising future generations and the needs that will be met then you know what really that means is just really everyone from an individual to families to communities cities countries and organizations trying to understand the impact that they have uh from three perspectives environmental care social well-being and economic growth and trying to create a balance through practices or strategies to adhere to those and and set commitments and practices in place to kind of address that and what's your background did you have you always worked in sustainability yeah I have quite the Jack of all trades background so I'll start from the kind of from the now I'm actually new to role not to Microsoft but to this role um I think it's been like three months already um but I've been with Microsoft for six years and in that time I worked for a small part of the organization called the Microsoft Technology Center as an technical architect a cross solution technical architect and so what we did was um we met with hundreds of customers a year in our New York facility where we would just help them ideate strategize and then build solution architectures around various different solutions that we have in helping them address their business objectives and challenges and since the release of our platform and our commitments I was immediately drawn to being able to talk to this because I've I've I've had a passion in this area for a long time and so it was just so surprised one on our ambitious commitments and two that we actually came out with a platform that we can help customers leverage and so for the last two years I've been speaking to customers about that in our MTC locations and virtually around the world uh since we had a bit of an interruption there I finally was able to move into a role thankfully at Microsoft that is dedicated to this space so now I spend my entire time just talking to customers in this space and before then then very much of a technical background in Building Solutions around virtualization uh Cloud technology cyber security Consulting it actually have served me well in having like a multi- solution or multifunctional architectural conversation and I've just been able to use that same kind of drive and system thinking into sustainability and how to kind of look at that holistically for a customer and how to think about solution architectures for customers when you think about sustainability what are some of the the big threats that sustainability as a field of practice seeks to address I mean climate change is is one but I'm imagining it's one of a number that folks are trying to address with their sustainability initiatives so when I when I think of these big threats I I immediately think of like the large ones that are interestingly enough interconnected and so it's things like climate change and its impact on the increase to extreme weather events to the collapse of biodiversity and the impact to us and as well as the shortages of Natural Resources right and just to give you some examples of like some of the gravity in terms of some of the facts related to around this right it recent Studies have found that you know our negative contribution to the environment is a large contributing factor to the extreme weather events and what's unfortunate it is that you know when we think of transitioning we have a this idea of a just transition to kind of moving towards sustainable practices those who are were the least in contributing to this are the most affected by these sorts of events and and these risks the other one is in terms terms of wildlife population we're seeing a significant decrease in Wildlife population especially in South America South America seeing like a 94% drop in Wildlife population and and that again has a domino effect to the domino effect right in terms of the ecosystem you know if you think of like the pollinator population as an example bees for one right that's being affected dramatically and when you think of what happens when the decrease of population in pollination begins you know we see a direct effect to the global food uh Supply 75% of our Global food supply is reliant on pollinators so these are large large grave sort of facts that we have to deal with and so that kind of sets the stage you know for a lot of strategies that the world or organizations governments right are looking to populate from reducing greenhouse gas emissions and moving towards renewable energy sources to adopting systems thinking to like the idea of the interconnectedness between nature to protecting habitats and ecosystems and to moving towards more regenerative and sustainable practices around Industries like fishing mining Agriculture and so at a techn level right when you when we think about trying to address these it's really not a new strategy it's collecting data and the fact that there's a lack of data right and then having to kind of bring that in right do some analytics on it it's kind of that old adage of right like I can't manage it if I don't measure it right and so right now you know we're seeing that as a big big gap in the industry is for or for anyone for anyone trying to set commitments is collecting data to understand what they can do and how to monitor progress towards those commitments let's dig into some examples of how AI can be uh applied in some of the diverse areas that you just spoke about one example uh that's a early warning system early warning for all can you tell us a little bit about that initiative and what it's aiming to accomplish yeah the United Nations put together this initiative called the early warning for all and the goal was or at least their their commitment was they're looking to protect all humans on Earth from hazardous weather events by 2027 and so to help they started ideating well what can we do with the data that's available out there in the world as it relates to what could help in being able to advise uh communities around upcoming extreme weather events and so a couple thoughts were well how can we use satellite imagery meteorological data as well as real-time data streams like social media and put that together and so we worked with an organization called seeds which stands for sustainability environmental and economical development Society H to help build an AI model called Sunny lives and the sunny lives model was uh trained manually by a number of data scientists on 50,000 images of rooftops and the idea was how can we build a model that one can identify the different rooftops of specific buildings and categorize them into seven types and then as well correlated to different geographical factors like bodies of water or elevation and so with the result of that they were able to use this model in being able to personalize the advisories to specific governments and specific populations of people or communities of people on upcoming events as well as helping nonprofits and government agencies essentially disseminate resources ahead of time when we when they detected an impacting event and it was timely there that by identifying or classifying these different rooftops they can characterize the the building practices in general and how susceptible they are to different levels of storms this was necessary because of a lack of urban development in a lot of these areas and two it was also just to ident so to your point it's a yes and as well as identifying where there are communities of people living versus where there has not been and so just understanding like distribution of communities of people in certain areas so it turns out it was timely right after after piloting it and putting it into production in 2021 Cyclone Yas was nearing the eastern states of India and with the model they were able to identify the populations at at risk or the communities at risk and be able to send personal advisories immediately to over a thousand families and and and helping government agency as well as distribute resources appropriately and efficiently like into those different areas so it turned out to be a great project and it's still in place today and and really helping the areas that that need it right as I mentioned you know those are the ones that are most affected by extreme weather events that's interesting you mentioned that there was some custom model develop Vel mment involved here was this project a research project or research in partnership with other organizations or was it more a case of organizations using off-the-shelf tooling to to create that model it was definitely not off-the-shelf it was a model that had to be created by a partnership between that seeds organization and ourselves there was no model at the time that would help in this specific use case so it was very much a partnership where we supported them from a technology perspective and a Consulting perspective on what technologies that could be leveraged and then helping scale it you also mentioned one related to food waste can you talk a little bit about that use case yeah food waste large large problem so pervasive issue right now affecting both not only the environment but the economy right un reporting 931 million tons of waste of food annually right that amounts to about 177% of all food available to Consumers you know and so lots of organizations are thinking about you know how can we help address this due to that that that sort of two- faceted result right and it's all across the supply chain for food an entrepreneur years ago who was trying to tackle this in restaurants and hotels and the approach was like putting a camera over the um the trash can and using that to develop reporting that fed to management about how food was used and wasted so that they could do things like tweak portion size and ordering and stuff like that it's such a complicated issue because there's so many hands passing right food right when you think of the supply chain and every hop something has to take last I checked the the majority of food waste results in in before it is received by grocery stores and so yeah grocery stores have the ability in starting to take measures like using either image or video recognition and being able to tie that to some process that can hopefully you know optimize their purchasing process but on the supply chain side they're trying to understand what they can do in sort of demand forecasting optimizing their processes and that has been traditionally something that hasn't been done in that industry and so a lot of organizations are taking different approaches on how to address that yeah there all these different you know when you think of a value chain for a specific food item there's arguably countless of of companies involved from the beginning to the end of the life cycle of food and so lots of organizations are trying to understand what they can do on their part but as well as you know interesting enough we are seeing more of requirements for customers Upstream right so if you think of a grocery store a lot of grocery stores are starting to dictate data related to kind of the processes that they have of you know what their waste profile is um what their environmental emission data is what their uh code of conduct is you know from an ethics and social responsibility perspective so we're seeing a lot of that from a requirement standpoint at the at the private level but also at the government level we're seeing regulations sort of dictate the um disclosure of information like that I was going to ask about the specific food waste project that that you mentioned we have a customer called LMK group they're a Nordic food kit delivery company and when you think of that food waste problem we're talking about right there is a player throughout that entire supply chain they were looking to understand you know what can we do from a demand forecasting side a supply chain optimization side and how to start thinking about personalizing customer experiences these aren't new you know strategies right I think they are new in terms of the outcome right so they're thinking about it from the sustainability lens versus yes arguably optimizing efficiency right in their processes the end result is great because if I could reduce waste I mean that's that's better for the world the thought was okay so how do we develop machine learning models that can essentially predict customer orders with accuracy right and so what they did was one tried to build a model that allowed them to bring in uh not only historical data but real-time data to forecast demand the goal was to help them provide suppliers precise predictions right you know up to 10 weeks in advanc you know when when they started in this process you know the challenges you know are not dissimilar to other use cases right you know I need to integrate data in disparate sources and different formats I need to um build machine learning models on some platform that can ideally scale it we help them with our Azure machine learning platform to help build this model that allowed them to bring in that data and allow them to bring in different factors like seasonal changes and cons and consumer preferences and again bringing from other external sources to essentially correlate this data they were successful in building this machine learning model they used a variety of different algorithms from time series analysis to regression models to essentially help in forecasting the future demand the result from a kpi perspective was incredible I mean they went down to a reduction of less than 1% of food waste and from a starting point of what from a starting point of an average what we're seeing in their industry which is about a 15 to 40% um food waste at least in their part of the supply chain right um as I mentioned the majority of this is is further Downstream in terms of the impact of food waste but you know from a pattern perspective in terms of you know how AI can be leveraged here the the challenges are seem to be the same except I'm finding that customers that are in the realm or in the responsibility of sustainability with an organization we're finding that this is new to them new to them that they can apply AI as a tool to address these issues or the the challenges themselves are new what aspects are are new so what's new to them is what can be done with AI and machine learning models or what needs to be done um with the leveraging of AI Technologies and machine learning models um so tendentially we're finding a lot of cross collaboration between this new aspect of a bu this new part of the business and technology teams it sounds like the technical process here was first identifying this Challenge and then the application of you know what I might call traditional data integration techniques to kind of get all the information about their supply chain in the right place and then imagining like traditional ml models as opposed to you know anything fancy yeah sounds like kind of a bread and butter type of problem that that you know I think of it as like unsexy AI sometimes like it almost feels table Stakes yeah yeah right it's just a new yeah business objective new outcome it's Ai and and these models have been so publicly available now you know it's such an interesting time to have access to all of this and really easily deploy this and so thinking about well how do I build one from scratch you know and do I even need to build one from scratch right and how do I get started like what are these platforms that we can use to get started here and what are these data sets that I can leverage there's so much available out there you know I look forward to where this becomes as a mature Ai and ml use case as it is for other aspects of a business like from Financial operations right to market trends to other things which arguably is sexier it's it's really nice to start seeing this part play a role here want to make sure we cover really interesting use case that is focused on conservation particular in the Amazon can you tell us a little bit about that one yeah yeah that one I'm I'm I'm pretty passionate about it's it's a pretty cool use case you know kind of to set the stage when you think of the Amazon rainforest it's the largest rainforest in the world it spans nine countries and you know not many people know this but you know it's absolutely crucial to keeping the balance of the ecosystem it is one of the largest carbon removal entities in the world it it as well it's kind of like what you were referring to earlier just the interconnectedness of yeah all of these systems and that is a a huge piece that plays a significant role globally yeah it I mean you know on the bleeding edge side a bit off topic but a lot a lot of tech companies like ours are investing in other companies who are looking at Carbon removable technology because it's needed but we've got like free Carbon REM removable technology right now in the form of biology you know and and it's at risk and on top of it being one of the largest carbon dioxide removers in the world it also dictates weather patterns and so as as it depreciates or as it's infected impacted you know that dictates the weather patterns around the world and this is not a new problem right deforestation but it has been increasing and so what we did was collaborate with our research teams and a number of different universities and agencies uh down in South America to put together a project called project guacamaya and and bakaya means MAA like the bird MAA in Spanish and so the idea was okay well how can we start addressing the detection of deforestation trends that are illegal and so the idea was can we start using satellite imagery analysis camera trap Imaging analysis as well as bioacoustic analysis to essentially monitor the rainforce S Health and biodiversity and so again from a challenge perspective it's a very interesting one because it's such a vast geographical area right and we're having to start implementing realtime monitoring and then on top of that the complexity of analyzing multimodal data right so you think of imagery from a photo perspective imagery from a satellite perspective now we're talking bioacoustics right how do I put that all together and so on the satellite front right we all started leveraging the planet Labs high resolution satellite imagery and what we did was we worked through those photos on a daily basis to identify signs of deforestation and illegal Mining and the AI model that we built helped track the changes over time and then detect specifically unauthorized roads and the reason to detect unauthorized roads is because that typically often preceded deforestation activities on the camera trap analysis side right we had C there were camera traps across the rainforest that were able to take photos on detection of movement and so thankfully in that scenario there was a model that could help here called the mega detector and what it did is it helped filter and classify images of what they called bioindicators which is essentially a a way of saying you know species whose presence or absence might indicate an ecological change right and then lastly on the bioacoustic monitoring side which I thought was pretty fascinating the idea of capturing sound specifically animal sounds like birds and differentiate between the different species to identify either migration of species away from a specific um area or the introduc the introduction of an invasive species into an area all this being put together into an open source model now so now not only can they leverage it for the initiatives in the Amazon rainforest but now the hope is that it's going to be um adopted by other parts of the world with ecosystems like that that could be affected that could leverage such a model the model created on the bioacoustic side is an interesting one we'll drop a link to the paper into the show notes the end result is this contrastive language Audio pre-training model that is a multimod model that aligns the audio with textual information to help enhance the process of identifying particular sounds uh really interesting uh on the research front as well as the practical application here you know we we we should and we have kind of taken it for granted that this is something addressing sustainability is something organizations should do but are there particular drivers that are prompting organizations like Microsoft and the customers that you work with to pay attention to these things now I know government initiatives for example are a big part of that you know how do you think about the the drivers yeah I mean the first one I was going to mention was the first one you mentioned right I think that's one of the biggest drivers uh unfortunately that's that's sometimes what it takes is kind of strict government regulations to have people move towards progress so yes we are seeing stricter government regulations as well as industry related agencies that have regulations we are starting to see Rumblings of that here in the US with a regulation out in California and one coming from the SEC these are all looking to essentially collect or require collection of environmental data uh specifically around scoped emissions from a terminology perspective we have different types of emissions right you can think of carbon emissions you can think of the use of water to waste to biodiversity loss emission data for scope one is really the direct emission of an organization direct operations scope two is emissions related to their utilities so things like consuming electricity gas heat Steam and so forth scope three is the most complicated one there are two aspects to scope 3 scope three from a downstream perspective is everyone in your supply chain all the emission that they emit on your behalf is your scope three emissions then there's Upstream if you create a product and the use of that product as an example Xbox right every time someone uses an Xbox that's scope 3 Upstream emission and so how do I start considering collecting that data and reporting that that is by far the most difficult one but yes so we're definitely seeing stricter regulation from governments but as well as consumer demand shifting so we are seeing more and more customers uh looking to do business with those who are actually being responsible as it relates to sustainable business practices and that is why a lot of people are starting to publicize their commitments around sustainability from an operational perspective right this also is about cost savings right so cost Savings in terms of reducing waste and energy consumption it's also a smart risk strategy right you know addressing issues like climate change and resource scarcity the other is really around like Corporate social responsibility doing good right making sure that you're contributing to the social good and environmental stewardship right Microsoft specifically like thankfully we are in a position to do more to help with climate change um and we believe we should I mean it's built into our mission statement our mission statement is to empower every person and organization on the planet to achieve more very different from our original putting a a PC on every desk on any on every home right but also like the reality is you know we do better as a business when economies around the world are growing and doing well so we don't do well if our customers are not doing well um and I'd also be remiss to to kind of not also bring up our commitments in this space uh as well right we have been in this space for quite a while now we became carbon neutral in 2018 and a few years ago we set the most ambitious commitments by 2030 we are looking to be carbon negative zero waste and water positive by 2050 we are looking to remove all historical emissions since the beginning of Microsoft in 1975 and so these are Big commitments are part have enough positive impact that undoes essentially the historical emissions yeah through a combination of reducing our environmental footprint and through the invest into carbon removal agreements yeah and so lots of drivers nowadays far less are those doing for the sake of doing good more starting to do more because they're being forced to do more from either the government perspective or consumer demand perspective along those lines I recently spoke with one of your colleagues lauron benois in a conversation around Ai and power and energy and one of the key topics that came up was the growing need for power to drive data center growth and a big part of that was driven by AI workloads how does you know Microsoft being kind of central to this growing use of power around AI get reconciled with a drive towards sustainability and being responsible in using resources this this is a massive challenge that that's in front of us right the idea of having to decarbonize the delivery of AI to the global economy and we are seeing the reality of those of that through our numbers uh we've just recently released our sustainability report for 20123 and this has been one of our challenges and we are committed to a full stack end to end approach to tackling this problem right this is this is really a once- in a-lifetime opportunity for us and it's arguably the biggest bet the company has made we have outlined a thorough approach but just to give you some insight on you know some of the strategies that we have in mind right I think firstly it's about improving the Energy Efficiency of AI itself right so the code and chips that complement the centers right so we can do more with less right designing AI models right to run more efficiently exploring small language models like arar models as well as hopefully building a momentum for green software engineering principles that can be applied to First and thirdparty AI applications another strategy that we have implemented is how can we continue to optimize our data centers right to essentially minimize the use of energy and water and part of this is and is is you know comes from the the idea of like well what are those upfront design choices that we have to make to figure this out and so ideally it's about building as few data centers as possible to meet the demand and then operate them as sustainably as possible our data center team like really can come in from an Innovative perspective because they asked the big questions like can we cool data centers without using water yes right so we are able to use EV evaporative cooling uh technology in temperate climate data centers right this it's called ad arotic uh Cooling and using air cooled chillers in warmer regions right this hopefully eliminating the use of water from cooling this is the exact same strategy we are using to move in building all of our new AI data centers moving forward another example of kind of what we do with our what we're doing with our data centers is kind of reimagining not only as consumers of resources but kind of integral into the ecosystem so an example could be what we've done in Sweden and Finland with our First sustainab Data Centers where we're actually recycling the waste heat to provide the community of that area heating during the winter what we can't reduce we have to remove as I mentioned for carbon we need to remove carbon in terms of water we need to replenish water so as much as possible we are coming up with tactics and strategies and putting together an Investments on on uh initiatives and companies that are focused on on carbon negative Technologies and water positive Technologies so in talking about the kind of the scope of the challenge for an organization and the degree to which it's highly interconnected maybe we can jump into the groupo bimbo use case as an example of how an organization tackles the reporting challenges associated iated with ESG compliance yeah and and also to set the stage right you know ESG a big acronym being thrown around this idea of environmental social and governance I get asked a lot like what the difference is between ESG and sustainability and so as I mentioned earlier sustainability is this broader practice right of meeting the needs of the present without impacting the future ESG right is really about how do I evaluate an organization based on specific metrics around environmental data social data and governance data a lot of quantitative and qualitative data this is mainly dictated by regulation so help them asking for disclosure of this sort of data to evaluate the profile of the organization to investors right understanding from their point of view what their their commitments and impact and progress towards them are and so we're seeing very much organizations start to think about okay what do I need to do to one Define commitments that I can publicize and then start tracking my progress towards those commitments and at an atomic level it's really about okay well I need to collect environmental data I need to connect collect Social and governance data which is typically in HR systems Finance systems Erp systems right and so how do I bring that together how do I put that ideally in a model of sorts something that can understand an ESG relationship and then how do I build Downstream uh reports and dashboards that can help not only my internal stakeholders but external St stakeholders allowing me to then help in my disclosure in my auditing of specific regulations so groupo bimbo is a great example because they set some pretty ambitious sustainability goals and for those that aren't familiar with group they're like one of the largest Bakery manufacturers in the world they're probably most well-known to us here at least in the US verar Lee and timans you see them at the end of the aisle in groceries you know you try to bypass them but sometimes you can't help it and so uh it started off with again not a new use case they're they have a supply chain I'm looking to reduce costs optimize my production cycle and so lots of iot and OT opportunity here to collect that data in parallel the foresight was this is great that this initiative is moving forward to do this to collect this sort of data now let's also leverage it to start reporting on the environmental data so they were able to use our Azure Cloud to host the iot services necessary the data Lakes necessary and the data Factory Pipelines necess necessary to essentially allow for the ingestion of the data and dumping it into a Lakehouse that allowed them to carry out an elt process and extract load and transform process using these pipelines and then leveraging our Microsoft cloud for sustainability solution called Microsoft sustainability manager to take the environmental data and take it through the necessary emission calculations and then reporting it so helping them build not only customer report reps but helping them build reports for disclosure we are seeing a lot of companies with the same Challenge and desire is how do I collect all of this together bring it into a single model that can understand the relationships between these different data points do the necessary calculation and then be able to address my specific needs around reporting and dashboarding that's one of the biggest Investments we've made in our Cloud for sustainability it's not only the Sol solutions that accompany it from a data n perspective but the underlying ESG data model that we have that can help a customer out of the box not having to build their own ESG data model we've actually had customers come up to us with pre-built ESD data models and understand the Monumental effort that they went through to create them when we already had it and I may be overloading this but I'm thinking like a knowledge graph or some kind of graph of the relationships between servers that roll up into racks that roll up into Data Centers that roll up into you know overall it you've got products that have suppliers that have suppliers and you know just kind of all of the relationships between the various things that go into an overall sustainability profile is that the idea spot on I mean the relationships like when you think of tying together environmental data with like social data you know uh uh being able to track an emission for a specific facility and what business unit is it tied to and who are their employees that are in that facility do they drive to work to that facility there's so many different relationships you know between all of these different categories that people are looking to get insight on and be able to report from a voluntary perspective but also from a reg regulatory perspective MH it does seem like they're would need to be some standardization like it's great to have the flexibility to report on the various ways that you might see the problem but it's I'm imagining something analogous to Gap reporting and like Financial Services like if everybody chose their own way of figuring out earnings then comparing what one company's doing against another isn't all that helpful there's a similar situation for sustainability I'm imagining that's what the government regulations are about not just setting metrics and benchmarks but also standardizing at least a few of the key numbers that everyone should be thinking about yeah I mean in perfect world we'd have fewer because the way I think about it is every time a regulation comes out or some voluntary framework is being looked at to report to you're needing to build a specific schema that is mapped to the different Frameworks and so great we helped with the ESG data model right that's great we need help you're saying the problem is that there are too many such standards it's been a problem for different Industries security is also a an example of one of those there's just like hundreds and hundreds of different types of Frameworks and regulations that you have to map to right and so not a new problem a new problem to this industry again why we're trying to help with these outof the-box models the ESG data model is one we have another model that you can help then transform it to for the csrd that'll then help you report towards that specific framework this is with the help of our new data solution within that cloud for sustainability and that's called sustainability Data Solutions on fabric this is hopefully addressing the need uh for customers on putting together an ESG data state for them that can give them the way the Insight they need from an analytical perspective Ive on this data but as well as helping them through out of the boox disclosure reports be able to report to those different agencies as well as build their own customer reports and so what we've talked about thus far is again back to the the basic table Stakes that you mentioned earlier kind of traditional data pipelines and data analytics is there also a role for llms generative Ai and some of the newer technologies that are coming online large language models definitely being a a space here that is is a need and a place where we are investing in you know especially with the fact that in in the industry of sustainability we're having to not only read data uh in unstructured forms but also structured and so it's great that now we can kind of build these co-pilots to Target these structured data repositories and be able to ask questions on it and then generate information from it so very much seeing more and more in that space and then as well as what we've seen traditionally you know with the ideas of AI so things like anomaly detection outliers Trends analysis we have a capability in sustainability manager where we call it intelligent insights that we can take your existing data and you can give it some parameters in terms of helping you understand future States and future models and able to map that out for you so it's pretty exciting for this space to finally see something every demo we give where we're showing customers on how to create these reports from scratch using co-pilot to you know asking questions on existing data sets has been eye openening any parting thoughts in terms of where you see this all going or what you're most excited about I think what I'm most excited about is the increase in conversation I'm having with customers around this space whether it's because they have to or they want to or combination of both it makes me feel optimistic that customers are wanting to have this conversation with us or in general right with any other solution right that they're looking to identify how they can set commitments measure to measure progress towards them do good I think that this conflicting nature of AI and environmental consumption will be a very hot topic and I'm glad it is because we we are tackling it head first it is a top priority for us and you know if if history has told us anything with with Microsoft putting big bets on things like we'll do we'll do all right we're unwavering to our commitments as an example by 2025 the end of 20202 all of our data centers will be running on renewable energy energy and that's a big deal that's a big deal for us that's a big deal for the community and it sets the stage this is table Stakes for any Cloud company today here are the commitments here's what you should strive for and hopefully customers understand that so I see a lot of progress here but not without its challenges so I feel like there'll be bumps along the road but I'm optimistic that we're working together towards the same outcome awesome awesome well Andres thanks so much for taking the time to share a bit about what you're working on Sam thanks for having me really appreciate it thank you\n"