Data Citizens Dialogues

The significance of data literacy in our efforts to promote collaboration and decision-making cannot be overstated. In this episode, John Yelle, Executive Director for Enterprise Data Management at DTCC, takes us inside the fascinating world of the financial industry to explore the three V's of data - volume, velocity, and variety - and learn how they play a vital role in driving this digital evolution. 

Join us as we delve into DTCC's data management program, a seven-year journey of dedication and innovation. John shares invaluable insights about the importance of data quality, governance, and the necessary cultural changes for achieving a successful transformation. We also discuss the tangible measures of success, such as risk reduction, faster results, and effective capacity planning, which serve as proof of the merit of a data-first digital transformation approach.

1. Gain insights into the role of data - volume, velocity, and variety - in driving digital transformation in the financial industry.
2. Learn about the importance of data quality, governance, and cultural changes in achieving a successful digital transformation.
3. Understand the measures of success for a data-first digital transformation approach, including risk reduction, faster results, and capacity planning.

• Connect with John on Linkedin

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Discover the fascinating world of data marketplaces and unlock the potential of monetizing your organization's data in our conversation with Jay Bhankharia, a Senior Director at Databricks. Jay shares his expertise on how data marketplaces connect buyers and sellers of data assets and discusses the role of Language Learning Models (LLMs) in enhancing the user experience and aiding data providers in sharing and selling data more effectively.

In this episode, we’ll explore the increasing importance of data marketplaces for businesses in the digital age, highlighting a Gartner study that states that companies who collaborate and share data have three times better economic outcomes than those that don't. Learn how data marketplaces offer an open platform for customers to access data from multiple vendors, transferring it into their own tools and platforms with ease. We also touch on the impact of controls in data marketplaces to ensure data products are user-friendly and easy to purchase, and the exciting advancements in technology that have enabled companies to access and use data more efficiently, such as cloud computing, data science, and governance and access controls. Don't miss our conversation on the future of data marketplaces and how they're becoming a key component of business success.

Resources

• Connect with Jay on Linkedin

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Can data governance programs evolve to provide even greater value from our data? That's the question we tackle with our insightful guest, Raluca Alexandru, an analyst at Forrester. We talk about the key components of a data governance program, such as people, process, and technology, as well as the critical role that data catalogs and business glossaries play in mapping out data assets, structuring them in an easily digestible way, and connecting them to business goals and objectives. 

The episode also delves into the need to create connected intelligence by combining data governance with AI governance programs. We discuss the potential risks of generative AI and how existing data governance frameworks can help manage those risks while creating value. We also take a closer look at the ethical and legal implications of AI programs and why AI governance is essential in reducing bias and adhering to policies and regulations. Moreover, we explore the importance of setting measurable metrics to assess the success of data governance programs, and how an intelligent federated approach can maximize their value. Don't miss this thought-provoking conversation with Raluca Alexandru that covers the future of data governance and intelligence solutions!

Resources

• Connect with Raluca on Linkedin

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Data strategy in 2023 is no longer a mystery. As businesses embrace data monetization, it’s vital to understand how to convert data products into revenue sources. In this episode, we chat with Stijn Christiaens, co-founder and Chief Data Citizen of Collibra, who unveils the secrets of data strategy and bridges the gap between data and business language.

Join us as we explore the evolving role of data engineers and their need for a deeper understanding of the products and applications they work on. We also discuss the significance of data democratization and the emerging data marketplace, which allows organizations to access and measure data value in a democratic way.

Tune in to this insightful episode to stay ahead of the curve in data strategy.

Here are three reasons why you should listen to this episode:

1. Learn the importance of data monetization in your overall data strategy.

2. Discover how the role of data engineers is evolving as they adapt to operationalizing machine learning models.

3. Understand the impact of data democratization and the rise of data marketplaces on the business landscape.

Resources

• Connect with Stijn on Linkedin

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Join us LIVE at Collibra’s DC’22, where we talk about the relevance of data intelligence. We are joined by the 2022 Collibra Excellence Award-Winners: Acceleration All-Star winner Heather Wentworth, chief data officer of Accelerant Holdings; Program of Year winner David Mitchell, senior director of engineering of Cox Automotive; and Collibra Ranger of the Year Peter Vennel, enterprise data strategy and execution executive of Equifax.

How do businesses strike the ideal balance between powerful analytics and ease of use? Tune in to learn how data intelligence can help you solve problems and grow your business.

1. Learn why these individuals and companies stand above the rest in data intelligence.

1. Discover the secret to maximizing Collibra.

1. Unravel the future of data intelligence.

• Learn more about Collibra
• Accelerant 
• Cox Automotive
• Equifax
• Connect with our guests
• Heather: LinkedIn
• David: LinkedIn
• Peter: LinkedIn | Twitter

• According to Heather, the Acceleration All-Star of the Year award represents the company’s efficient planning and implementation from start to production.
• David sees the Data Program of the Year award as a culmination of their data strategy and execution efforts. It also highlights their culture of innovation and collaboration. 
• Winning the Collibra Ranger of the Year award, Peter considers their company’s decision to use Collibra a wise investment. 

• David was a software engineer before moving into the financial data industry, where he discovered his love for problem-solving and helping customers.
• Heather shifted careers because of the culture of valuing sizzle over data.
• Given his professional background, Peter’s transition into the data industry was not by chance. As he continues to work in the field, he hopes to improve its quality.

• Instead of focusing on various aspects, it is best to concentrate on system engagement.

David: “As you look at bringing people processing technology together, you’ve got to win the hearts and minds of people to actually get involved in [system engagement].”

• Learn how to use Collibra’s automation to pull information rather than having people do it.

• Your team is the key to success.
• Start-ups have a significant advantage in moving around the data ecosystem quickly.
• Understand your company culture.

Heather: “​​All of my team have some experience and have seen the [worst of implementations]. I think a lot of that comes to guidance from maybe consulting firms that are not close enough to it or don’t understand the company culture.”

• Peter wanted an in-depth understanding of the Collibra process and got his certification after 8 months.

Peter: “There’s no one size that fits all. Every industry can implement Collibra in a different way.”

• You need to be a domain expert to see the advantage of Collibra quickly.

• In the future, we’ll be able to predict businesses' performance and prevent negative outcomes. 
• We’ll be able to mine data and perform more automated processing.
• Automation will be critical in the quick transmission of data from provider to consumer.
• AIML will be further infused with technical lineage and data quality.

Heather Wentworth is the chief data officer at Accelerant Holdings, where she is in charge of the company’s data strategy and the design and implementation of data intelligence. Her team’s dedication to providing customer-facing solutions that reinvent the insurance experience earned them the Collibra Acceleration All-Star award.

Follow Heather on LinkedIn.

David Mitchell is the senior director of engineering at Cox Automotive. His company's program was named Collibra's Program of the Year. With excellent engineering teams, David builds and operates the cloud-native Enterprise Data Platform, Enterprise Data Marketplace, and ModelOps platform. 

Follow David on LinkedIn.

Peter Vennel is the enterprise data strategy and execution executive of Equifax. He assists organizations in monetizing data as an asset. Peter has worked with multiple C-suite executives and external partners on Strategic and Tactical Data Management and Governance initiatives. He was named Collibra Ranger of the Year.

Follow Peter on LinkedIn and Twitter. 

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This episode will inform you on how organizations can effectively implement data ethics and the importance of upholding your customer’s privacy when using their data.

Have any questions? You can connect with our host, Jay Militscher on LinkedIn. 

Thanks for tuning in! For more updates, please visit our website. You may also listen on Apple Podcasts or Spotify.

Join us LIVE at Collibra’s DC’22, where we talk about the importance of data governance with Joe Wallace, senior manager for Digital Asset Governance, Adobe, and Ryan Galloway, senior vice president for Moody's Corporation. How can companies improve their data quality and decision-making processes? Listen as they explore some of the challenges associated with data governance and share tips for overcoming them. 

Tune in if you want to learn to integrate effective and ethical data governance in your company.

1. Find out how Adobe and Moody’s Corporation integrate data governance.
2. Learn how Adobe and Moody’s Corporation utilize data governance to achieve their company goals.
3. Discover how data governance can help companies succeed in today’s market.

Ryan: “I always say push versus pull. It's a journey. You have to start there.”

• Moody’s Corporation welcomed data governance to their company because they believe it would be helpful for business. As a result, they found an advocate for data governance and thought of using it to solve business problems.
• Any company interested in integrating data governance should consider building the following: standards and policies, business partners, advocacy, and real governance.
• Once these are in place, form a data governance team to help solve real data-related business problems.
• Focus on the mantra, “success breeds success.”

[04:13] Digital Asset Governance and Data Governance

Joe: “Fish with a smaller fish to get a bigger fish.”

• Adobe started small and ended big by building data governance around the company’s goals.
• It grew exponentially as more and more people heard and saw the data-related efforts the company was doing.
• The people could enrich the company because it dedicated its time to guiding them, providing them with playbooks, and facilitating their work.

[07:16] Data, Water, and Cake

• Joe believes that data is like water. You trust the source that the water is clean.
• He adds that data is a component of a cake. The top layer where the frosting sits pertains to the business layer (e.g. process flows, business rules), the one people see.
• Below the frosting are the data products. The final layer is the data lineage.
• The three layers compose every company's data governance. They are assets that draw customers in and protect them.
• Data governance should always be customer-centric.

• Society has moved from wanting more data to trying to solve the problem of excess data. Hence, Joe believes that over the next ten years, people will start labeling and organizing data.

Joe: “The groundwork is there. But I think there's just gonna be more and more and more and more data.”

• Ryan agrees that there is a ton of data in society today. So, the challenge for people now is defining meaningful and not meaningful is critical.
• He adds that automation will improve. Scanning databases and grading data lineage could become automated in the future.

Ryan: "You're never done with data governance. We must realize this isn't going away. This is something we're going to have to continue doing."

Joe Wallace is the senior manager of Digital Asset Governance at Adobe. He has over 15 years of experience in digital asset management and has been instrumental in developing and implementing several successful governance models for major organizations. Joe is a recognized expert in the area of digital asset governance.

Ryan Galloway is the senior vice president of Enterprise Data Management Tools at Moody's Corporation. He is a thought leader in data management and instrumental in helping Moody's Corporation implement some of the most cutting-edge data management technologies worldwide. Follow Ryan on LinkedIn.

If you did, be sure to follow, post a review and share it with others.

This episode will inform you on how organizations can effectively implement data ethics and the importance of upholding your customer’s privacy when using their data.

Have any questions? You can connect with our host, Jay Militscher on LinkedIn. 

Thanks for tuning in! For more updates, please visit our website. You may also tune in on Apple Podcasts or Spotify.

Data can drive game-changing business decisions, but inconsistent data can do the opposite. The world of data has come a long way, but we still see businesses with problematic data reporting, which leads to their eventual downfall. The more we use data, the more we need to see it as both an asset and a responsibility.

In this episode, our host, Jay Militscher, invites Stijn Christiaens, Collibra's very own Chief Data Citizen, to explore the topic of data citizenship and what it means for companies and data professionals. They will dive into what it means to be a data citizen and how to be intentional about data usage. Stijn also shares Collibra's growth over the years and showcases the importance of continuous improvement as data citizens. They also discuss the upcoming Data Citizen Conference and what it means to thrive with data.

By listening to the episode, you will:

• Learn about why correct data handling is critical for businesses to thrive in today's world.
• Discover the meaning and context of data citizenship.
• Understand the value of different kinds of data literacy skills in an organization.
• Find out what questions to ask when using data to create and develop products.
• Pick up insights on how data drives continuous improvement.

Want to know more about how to be a better data citizen? Tune in to discover the what, how, and why of being a data citizen.

AI is no longer for data scientists only. Most businesses have made AI tools part of their workflow. For example, we’ve seen chatbots, automated emails, and the like. As we embrace artificial intelligence, we have to discuss it. What is AI? What is it not? Should we use it for everything?

In this episode, Sarah Hoffman, VP of AI and Machine Learning Research in Fidelity Investments, defines AI and its impact on our lives. She also describes the ethical challenges of data bias and what people are doing to overcome it. Finally, Sarah explains why diversity and prejudice are significant concerns in AI development.

Tune in to this episode to learn how AI pushes for innovation. 

1. Discover artificial intelligence as a new approach to learning and training. 
2. Learn how AI is a reflection of our own beliefs and understanding. 
3. Understand the relevance of diversity in the field of artificial intelligence.

• Connect with Sarah on LinkedIn and Twitter 
• Know more about FCAT
• Be part of Random Hacks of Kindness

• The Fidelity Center for Applied Technology (FCAT) has a long history of innovation and commitment to technology.

Sarah: "We invest deeply in technology. But we've also always recognized that technology is just a tool. It's really how we apply it that matters." 

• FCAT develops platforms and products to empower the next generation.
• Sarah is a part of FCAT's research team. They explore the future of artificial intelligence (AI).

• Sarah uses AI and machine learning (ML) interchangeably.
• ML refers to code learning from data.  It produces answers based on stored information to make predictions.
• AI is math, not magic. 

• FCAT provides services that harness AI’s true potential.
• Financial services use AI tools often. Many people use chatbots, robo-advisers, and automated email responders.
• Several companies have adopted personalization and sentiment analysis.
• Models need to adjust when something changes in the world.

• AI learns biases through data. 
• Ethics boards address ethical issues regarding AI projects and decide whether a problem needs AI.
• Fairness and explainability tools are available to protect against inadvertent biases. 
• Using AI can enhance how we train people and use fairness and explainability tools. 
• AI tools can help with issues regarding biases. 

• Multiple tools are now available to help you automatically write and code. Some tools can write code for you.
• The system is not perfect, but it's a good way for new developers to begin.
• Artificial intelligence can speed up coding for experienced programmers.
• AI tools are limited in terms of fact-checking. It can help with brainstorming, but it’s essential to be critical regarding the data an AI provides.

• The field of no-code/low-code has improved over the years. 
• We must democratize innovation to share ideas with everyone.

• Each individual has their preferred learning method. For instance, some people don't learn well in classroom settings. 
• Artificial intelligence offers another approach to learning.  
• AI can simplify jargon and make knowledge more accessible to a broader range of people.

• AI tools provide feedback on how to use inclusive terms when speaking or writing. 
• AI biases reflect our assumptions and prejudices. 

Sarah: “AI tools are showing us the AI bias. Maybe we can start thinking about it for ourselves and think about 'Do we have this bias?' And I think that's another way that AI could help us be more inclusive."

• Sarah predicted that companies must prepare for a more flexible work environment by 2025. 
• Her prediction happened earlier than expected due to the pandemic. 

• People with similar backgrounds might not see concerns that affect people outside their demographic.
• Diversity in the field of AI helps make its tools more inclusive.   

Jay: “[AI] is democratizing because it brings people into the tech. It's inspiring people–young people. Maybe folks that wouldn't otherwise be taught are encouraged to pursue these things. It's so promising for STEM education opportunities, and it'll be fun to watch as it evolves.”

• AI is math first, not magic. It requires colossal amounts of data to create a model.
• AI is everywhere and impacts humanity in many ways.
• AI democratizes innovation and can create things that previously were only doable by humans.

Sarah Hoffman is the Vice President for AI and Machine Learning Research at Fidelity Investments for over four years. Her research foci include the future of AI, enabling data-driven enterprises, the future of work, and digital ethics. She started as an Information Technology Analyst and gradually built her way into AI and Machine Learning over the past years. Sarah is passionate about enhancing AI to democratize innovation and creativity for everyone. 

If you want to reach out, contact Sarah via LinkedIn or Twitter. 

If you did, subscribe and share it with your friends! 

Post a review and share it! If you enjoyed tuning in, then leave us a review. You can also share this episode with your friends and colleagues. AI and machine learning are a big part of the future, so help your community find out more about it!

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Data analysis, data science, and machine learning. The boundaries between these three may not be apparent, but these fields are related and interconnected. So it’s possible to start a career in one and dabble with another. Data has made it easy to connect and acquire information. However, we must be vigilant in upholding privacy.

In this episode, Gretel De Paepe, senior data scientist at Collibra, shares what she’s learned in her data career. She tackles the importance of data in our lives and its incredible value — in the present and the future. Lastly, she tackles myths on artificial intelligence and machine learning.

Tune in to the episode to learn how to handle data correctly.

1. Find out what inspired Gretel into pursuing data science.
2. Learn how to appreciate data in making our lives better from both the average user’s and company’s perspective.
3. Go beyond data bias and our misconceptions around artificial intelligence and machine learning.

• Connect with Gretel on LinkedIn.

• Collibra offers many services to their customers.
• Data classification helps companies classify fields that contain personally identifiable information (PII) data.
• Asset recommenders give a list of recommendations based on one’s datasets.
• Similarity detection looks for similar assets to prevent potential duplication and keeps the database clean.

• Data analysts looks at the data to provide a data-driven answer for a business question. 
• Data science deals with statistical modelling.
• The leap from data science to machine learning (ML) is small because machine learning is one way to model data.
• ML is simply a tool in the data science toolkit. 

• Gretel’s progression from data analysis to data science was a natural process.
• When solving different challenges, you must explore other techniques and build up your portfolio.
• She invested time and money into learning about machine learning.

• Gretel treats data analysis like a hobby.
• She easily loses herself in a project because she’s interested in data science.

Gretel: “Usually when I start with a project, there's not much information yet. It's sort of, “Oh, we may wanna do something in this area. But we don't really know yet what it is.” And so, the whole exploration phase of trying to identify what it is that we could do, what techniques we could use. And compare them, just try them out and compare them. It's a creative process.”

• We use data in statistics.
• Data is used often in our daily lives and provides many benefits.

• Marketing for artificial intelligence should focus on the fact that it’s only artificial. 
• A machine’s algorithm is limited by what it’s trained to do.

Gretel: “If you have a bias in your data, you will have a bias in your model. So your model is indeed only as good as the data that you train it on.”

• Big tech companies open source their models, architectures, and patent packages. However, their data isn’t.
• Obtaining data that’s vast and also diverse is a challenge.
• Security has to be built-in from the start to ensure the obtained data isn’t biased.

• Auto-ML only works well when the hyper parameters are already known. 
• Computer vision enables the detection of objects in images equal to or sometimes better than human accuracy.
• Another breakthrough was also seen in NLP but language is more complicated than pictures since it’s constantly evolving.

• People will see value in combining ML with privacy protection.

Gretel: “Machine learning is a little greedy beast. It needs a lot of food. It needs lots of data. It's very data hungry. A little hungry,  little thing. And how do you marry that? How do you combine that with also the increasing emphasis on privacy?”

• There’s a new emerging field called privacy preserving machine learning.
• Differential privacy ensures that data can’t be used to reverse engineer other datasets.

Jay: “What's often really interesting about data is finding common things, patterns, clusters of information. Those patterns help to answer questions, make decisions, make predictions, and even recommendations.”

Gretel De Paepe has been working as a Senior Data Scientist at Collibra for three years. She has amassed an experience of over 20 years in data. She started as a data analyst, turned into a data scientist then delved into machine learning for the past six years. Gretel considers herself a data addict who loves anything to do with data. 

If you want to reach out, you can contact Gretel via LinkedIn.

If you did, be sure to subscribe and share it with your friends! 

Post a review and share it! If you enjoyed tuning in, then leave us a review. You can also share this episode with your friends and colleagues. This episode will help them understand the ESG perspective.

Have any questions? You can connect with us on LinkedIn. 

Thank you for tuning in! For more updates, please visit our website. You may also tune in on Apple Podcasts or Spotify.

The topic of sustainability has undoubtedly gained massive traction and momentum across industries over the past years. According to Martin Weirich, ESG is more than a buzzword and a PR trick — businesses have realized the beneficial impacts of sustainable models. From this, the term ESG or Environmental, Social, and Governance has emerged — and it's here to stay in the hopes of a better future for the world.

In this episode, Martin Weirich, Partner Financial Services Management Consulting at PwC, discusses the basics of ESG. Martin delves into the regulatory aspect of ESG and how it can help make the world a better place. He talks about shifting the perspective and the future trajectory of ESG regulations. He also shares the most significant challenges companies face and how to navigate them.

Tune in to the episode to understand how ESG is changing the ways of companies and the world's future.

1. Learn the three aspects of ESG.
2. Discover the impact ESG makes on companies and the world at large.
3. Find out Martin's prediction on what the ESG landscape would look like five years from now.

• PwC
• Connect with Martin on LinkedIn
• Connect with Jay on LinkedIn

• ESG stands for Environmental, Social, and Governance.
• The environmental aspect includes concepts around climate, biodiversity, energy consumption.
• The social aspect concerns equal opportunities, human rights, health and safety, etc.
• Governance is about determining good governance and dealing with topics from an organizational standpoint.

• The European side has already committed politically to specific ties they want to achieve as a region by 2030.
• There’s a new required regulation influencing different sectors to support the political world to reduce greenhouse gas emissions and move toward renewable energy.
• The regulators began with the financial services sector because it deals with the orientation of capital flows. 
• Achieving the requirements also requires acquiring information (financial and environmental advocacy) from investee companies.
• These are not only regulations for bureaucracy’s sake but to make the world a better place.

Martin: "I think the dimension has a prompt. It's not only doing a tick box exercise from a regulatory perspective but also thinking out 'How can my products and my services help contribute to the overarching goals that we have all set?'"

• A lot of companies used ESG as a marketing instrument in the beginning. 
• However, these companies soon realized that there's also public transparency enforced by regulators, clients, investors, and associations.
• Mislabeling, such as green-washing, is a significant risk from a reputational and management perspective.
• The regulations have defined standards and metrics to make ESG initiatives comparable across different industries.
• Many companies have shifted from being very marketing-driven to producing substance first and then being vocal.

• A diversified board is a great step towards achieving the governance aspect of ESG.
• Many corporations have intrinsic motivation to have diverse views on the boards.
• The US is leading on the topic of diversity. The European side doesn't have a detailed discussion of what a diversified board should look like.
• There's a push to have internal discussions and debates within firms about living up to this diversity standard.
• It’s not realistic to change everything at once. It’s more about defining a transition plan towards aligning on the targets and making it public.

Martin: “The diversity element is just one element that’s really helping to spark discussions and to get corporations and leaders thinking what you can really do differently to contribute to the overarching goals.”

• The social aspect is now following the focus on the environmental element.
• The focus of the debate on the European side is on taxonomy regulation. It's about determining whether an economic activity is sustainable or not.
• There are emerging discussions on what defines social activities that deserve support, investment, and improvement.
• People are exploring the topics of human health, safety, and human rights in more detail from a banking and insurance perspective.
• More companies have realized that focusing on the social rather than the environmental aspect of ESG can help them make an impact.

• ESG regulations are now more purpose-led. As such, there's momentum within the workforce to do something different because they see a meaning behind it.
• Companies used to see regulations as a burden and an additional cost factor.

• Almost all sectors are moving toward becoming more sustainable or producing more sustainable products.
• More regulations have triggered financial services at an earlier stage. But there’s also a growing sustainability movement in other high energy-consuming sectors.
• A growing focus on stakeholders instead of financial KPIs is a common theme across sectors.
• There’s also a focus on optimizing supply chains to become more sustainable and transparent.
• The two sides of climate risks are how the climate influences the business model and how to improve how the company is impacting the climate.

• The current greatest challenge is data and implementation.
• Another is the changing political environment outside pure sustainable topics, i.e., other current problems.
• The challenge to data access varies on the sector type. It may involve information on supply chain, loan sustainability, sustainability of investee companies, etc. 

Martin: “The market is still maturing, but we are not there yet where we need to be.”

• Companies will have to disclose some information publicly in a format that makes it easier to digest.
• Presently, we rely on data providers with their own cost and fee structures. 
• In the future, data will be available in regulator-defined public databases and data clouds.
• It will not be enough to rely on public information in some cases. Thus, a hybrid data architecture from vendors and external experts will still be beneficial.

• ESG will still be very high on the priority list of all leaders. 
• There are a lot of new market developments where small niche players will help pivot change.
• Five years from now, we still won’t have achieved what we aimed for from today’s perspective. The journey is more of a marathon than a five-year sprint.
• There will be no sector or corporation that will not focus on sustainability. There will be a lot of public pressure behind it.
• Some smaller players will struggle with the significant overhead of regulations that drive ESG progress.

• Attention on the subject of ESG has recently gained substantial momentum and traction worldwide.
• There's a lot of regulatory focus that is a groundswell of public and societal desire. Implementation of regulations follows a gradual buildup approach.
• ESG is aspirational for the greater good, aligned with doing good in the world using methods and frameworks and accomplishing that in measurable ways.
• New industries, products, and ideas are being created as a part of ESG.

Martin Weirich is a Partner in Financial Services Management Consulting at PwC. His role is to help asset managers and banks transform toward sustainable finance. He is also responsible for helping his team implement the EU Action Plan and adapting business and operating models based on new ESG products and regulatory requirements.

If you want to reach out, can contact Martin Weirich via LinkedIn.

If you did, be sure to subscribe and share it with your friends! 

Post a review and share it! If you enjoyed tuning in, then leave us a review. You can also share this episode with your friends and family. This episode will help them understand the ESG perspective.

Have any questions? You can connect with us on LinkedIn. 

Thank you for tuning in! For more updates, please visit our website. You may also tune in on Apple Podcasts or Spotify.