Data Citizens Dialogues

The issues our society face today are increasingly complex and urgent. The rise of technology and the impact of the pandemic has put the spotlight on our growing social and environmental crises. But these crises are an opportunity for companies to rise up and show their worth. Now more than ever, companies are called to shift their solutions and initiatives in a way that will positively impact society at large by engaging in collaborative and holistic solutions. This way, they foster a more responsible corporate environment and create a sustainable future for all.

In this episode, Melissa Mavlanova-August, the Global Head of Equity and Impact at Collibra, discusses the link between ESG and DEI. Melissa shares the initiatives and programs Collibra organizes to promote social and environmental awareness across the company. She also discusses the work involved in ESG and DEI career paths and the importance of storytelling in sustainable change management.

Tune in to the episode to understand the impact incorporating ESG and DEI can have for creating sustainable and ethical companies.

1. Learn the meanings of ESG and DEI and the direct link between them.
2. Discover how companies can shift to a sustainable social and environmental focus.
3. Find out how to get into the ESG and DEI career paths.

• Collibra
• BlueDot
• BlueDot Public Benefit Reports
• Teach First
• Accenture
• Setting the Table by Danny Meyer
• Connect with Melissa on LinkedIn
• Connect with Jay on LinkedIn

• ESG is about ensuring a sustainable business that’s well-protected for the future in terms of its practices and human resources.
• Before formally starting their ESG efforts, Collibra's DEI-related work had shifted its focus on corporate social responsibility. 
• They realized that there is a direct link between ESG and DEI.
• Melissa’s team looks after everything — the planet, people, and economy.

• Many of Colliibra's appetite for environmental, sustainability, and DEI work has come from their employees.
• Collibra had the environmental community, Planet Collibra Community, before formally establishing an ESG team.
• They rebranded ESG to "communities" to focus more on the human touch. 
• Collibra has nine DEI-related communities that foster a sense of inclusion and accountability.

• The pandemic has brought the environmental crisis to the forefront of media and industries.
• There is a direct link between system inequality, systemic change, and environmental inequality. Environmental crises impact marginalized communities most.
• Collibra's growing sense of environmental responsibility led them to formally establish ESG and DEI into their framework.

• For Earth Month, Collibra organized an employee-led program aiming to promote environmental consciousness and awareness across the company.
• At the Reunited Company Employee Conference, employees built a water filtration system to be shipped to a country in need. 
• Planet Collibra focuses on practical, measurable value.

• Collibra integrates ESG principles across the business into company plans and processes, such as the Swag Store
• The Swag Store is a product of Collibra's partnership with BlueDot. BlueDot only partners with providers who drive environmental change or are part of a marginalized community.
• The swag includes everything from t-shirts and socks to gadgets.
• BlueDot provides regular impact reporting to see how much change results from the swag partnership.

• A business needs to be aligned to the correct stakeholders to utilize ESG and DEI career paths.
• Intentionally maneuver throughout your current organization and speak to the right people. Then, contribute to key initiatives and ask strategic questions.

• Melissa has always advocated for change, people's rights, and the environment. 
• She studied French and English in college but wanted to get into a field where money is and which she loves.
• Her first job was as a teacher at Teach First, a nonprofit teaching and leadership academy. It sparked her desire for equality and inclusion.
• Then, she worked as a management consultant for Accenture, where she got to work out her passions. 
• From there, she moved on to other opportunities that honed her DEI muscle until she found herself at Collibra.

• The skills you need for DEI, CSR, and ESG are very similar. Passion is only a small part of it.
• These are all technical disciplines that also require influencing skills.
• Above relentlessness, it’s the methodology that makes you successful in the  DEI, CSR, and ESG initiatives.

Melissa: “A lot of people see these disciplines as the ‘soft side of business’ or they're nice to have, and it's really not like that. It's a very rigorous discipline. It requires a lot of perseverance and a smile to make sure that things move forward.”

• Part of it is the regulatory components of ESG. There is an increase in regulations from a DEI and environmental perspective.
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• Increased regulation is driving competition. Businesses are getting urged to focus on ESG because of stakeholders’ demands.

Melissa: “I think the whole world has kind of got to a stage where it realizes that in order to do sustainable business, you have to make sure you are doing business sustainably. And how can you make sure you're doing business sustainably? Well, you need to make sure that you are thinking about your environmental footprint, your impact on people and the communities you serve, and that you are also making sure you've got a critical business model in place, a resilient business model, which can ensure that you can stand the test of time.”

• The daily requirements of the job are also technical, i.e., staying on top of regulatory and reporting requirements and customer demands.

• Change is incremental and needs to get driven at a sustainable pace.
• The storytelling and messaging are crucial. Being specific with what you're achieving can make people realize the change you effect.

Melissa: “If people expect us to change the world tomorrow or to get rid of the environmental crisis tomorrow, then that's gonna be very disappointing when we don't do that. But if they see that we are going to take these 10 steps in the next six months, and it's gonna get us to X, Y, Z, then they're motivated. And so that's why storytelling and messaging is so important for me.”

• Change management is about telling the right story and doing the right things at the right time with the right people.
• It’s best to be collaborative and facilitative instead of making decisions for people.
• Being strategic, telling the story, and managing change keep people's passion alive and spearhead success. 

• ESG professionals spend a lot of time manually reporting on what they’re doing.
• Melissa wants somebody to create a reporting mechanism or tool for ESG teams to report in a consistent and standardized way.
• Her ideal state 50 to 100 years ahead is for ESG and DEI not to exist anymore because it’s just the way to do business.

• Organizations with corporate social responsibility, ESG, and DDEI initiatives are increasingly becoming attractive places to work.
• ESG programs help organizations do good by addressing environmental concerns. Social and governance initiatives help organizations through transparent practices.
• Weaving ESG and DEI programs together amplify the impact both of them make. 
• Companies implementing these programs can perform better, attract the best talent, and contribute meaningfully to society.

Melissa Mavlanova-August is the Global Head of Equity and Impact at Collibra. She oversees the company's environmental social governance (ESG) and diversity equity inclusion (DEI). In line with this, she leads Collibra's global team in developing the company's internal ESG roadmap. Melissa's vision is to create sustainable and equitable products and help customers use data to change the world.

If you want to reach out, you can contact Melissa Mavlanova-August via LinkedIn.

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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 appreciate and take part in ESG and DEI initiatives that can help us all work for a sustainable future.

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Over the years, we’ve seen technology grow and how companies use the power of data to drive decision-making. However, we may be gathering more data than we can handle. Some companies may observe inconsistencies among data reports, leading to arguments and a lot of time wasted. This is where data governance comes in.  

In this episode, Joseph Wallace, Senior Manager of Data Governance at Adobe, joins us to talk about the importance of data governance, like how it can deter arguments around inconsistent data and improves business processes. He shares how data governance works, and how Adobe has a vision to make trusted data discoverable. He also shares what he believes is the future of data governance and how we must look at data like how we consume water. 

Tune in to the episode to dive deeper into data governance. 

1. Understand how data governance is defined, and how it can be used to improve business processes. 
2. Learn why Adobe’s data governance is rooted in making trusted data discoverable, and how the company defines trust, data, and discoverable. 
3. Discover the water analogy of data governance and how data governance will continue to grow. 

• Adobe implemented data governance around five years ago. This program started because the company started noticing inconsistencies among data reports. 
• The data governance program began by defining how metrics were created and calculated, and what they mean. 
• The initial point of their data governance program was to avoid arguments over conflicting data by creating an official source of truth.
• The more the program has grown, it has decreased storage costs and computing costs. It can also drive new insights and new revenue opportunities.  
• Joe began looking at how many people are looking for data and how long they are looking.  

Joe: We've spent 20, 25 years saying ‘data, data, data, gimme the data. I want to analyze the data. We're gonna drive with data. We're gonna do things with data.’ And now we're here with mountains of data and we have no clue what to do with it. We have no real knowledge of where it is and how to use it. What's the most efficient and effective way of using it? That's the real value proposition of data governance. It's protecting it. It's making it searchable. It's helping people use it in the right ways at the right times for the right purpose. 

• For Joe, data governance is about making trusted data discoverable. 
• For Adobe, trust means customers are using their products securely and know that if there’s an issue, it could be recovered. 
• Trust also involves operational data used for operational decisions that drive the company. 
• Joe explains that their data governance program is the hub of the wheel. Its spokes include privacy, corporate governance, and legal components.

• Data covers actual tables and columns, assets like IP, and both customer and Adobe content.  
• Joe prefers the term “digital asset governance,” since data can mean different things to different people. 
• In order to make data discoverable, Adobe uses a trust score and a prioritization model. The trust score looks at how people are using products, and how often. 
• Right now, the prioritization model is undergoing changes to prioritize customer data. 

• Initially, Joe needed to convince people why data governance is important. Now, people are coming into the team on their own. 
• Over time, Joe has created different templates and playbooks built from previous cases. 
• He helps empower teams and people throughout the business to do their own data governance. 

Joe: “You can't govern it if you don't know it's there. As we build out that catalog and as people add more things, then we can actually truly begin to put the govern in governance.”

• In the full episode, Joe shares how data is like water. You don’t think about how or where it comes from, you just drink it. 
• Similarly, people need to be able to access data the same way.
• Data governance – the identification and classification of the – is first. Protection is second. Updating and auditing is third. 
• Data governance never stops since data, and its use, will always change.  
• Joe shares that governance is about validating that the data is compliant. 

Joe: “Data should be the same. It should be clean. You shouldn't have to worry about it. You shouldn't have to think about 25 different angles of questions or concerns that you have with it. You just consume it. And that is a really important component of what data governance can provide. It's clean water, clean data.” 

• Next, Joe will work on tying all the dots together, including the service registry and the business process.  
• Not only can this ensure you can troubleshoot through components when there’s a technology problem, but this can also improve change management. 

• Joe shares that people typically don’t disagree with governance until you ask them to do something for it. 
• His main challenge with the data governance program was convincing people that it was important to do, especially to solve business problems.  

• Joe shares that Adobe is a pioneer in data governance. He sees that its capability will expand further. 
• We also need a world data organization similar to how we have one for global health. 
• The problem now is that every country and state has different ways of defining things. 

Joseph, or Joe, Wallace is the senior manager of data governance at Adobe and has worked in the company for over 12 years. His specialties include financial analysis, corporate finance, auditing, leadership, and much more!  

Joe graduated from Indiana University Robert H. Mckinney School of Law, then worked for an NGO and became an intern at the Federal Bureau of Investigation. He believes all these experiences shape how he sees data governance.  

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As companies grow and their market expands, their systems and processes become more complex. Managing data assets can be overwhelming without proper knowledge, organization, and mastery. This is where the concept of a data office comes in handy. In a time when data is more valuable than ever, it is imperative that a company understands how to make it work for them.

It might be time for you to consider forming a data office within your company, particularly if your company: is in a position of rapid growth, onboards employees daily, is expanding their market, and/or deals with systems and processes that may be out of date.

In this episode, Stijn Christiaens, Founder and Chief Data Citizen at Collibra, joins us to discuss the importance of handling data and starting a data office. He explains what inspired him to begin creating the data office within Collibra, and how this concept may pave the way for future companies. 

Tune in to the episode to further understand how to build a data office for your business. 

1. Understand the importance of data in a growing company. 
2. Identify if your company needs to build its own data office.
3. Learn from Collibra’s journey of building a data office.

Jay Militscher: “It meant to me that data and facts inform whatever point you’re making at the moment. Are you making a recommendation to buy something? Where’s the chart? Meaning data, to back up that decision. Are you delivering a critique on something? Again, where’s the data to back up an otherwise subjective opinion.”

• Collibra experienced rapid growth in its company, onboarding more people than the system could handle.
• As more people filter into the company, more data is added to the system.
• This increase in staff also implies an increase in customer interactions. 

• For Collibra, rapid internal growth meant growth in the market. 
• They needed to streamline their transition from data governance to data intelligence.
• Stijn believes that the growth in people and in the market means growth in data, which needs to be mastered.

Stijn Christiaens: “And, in that sense, we also said, okay, if we set up a data office now because we need it, right? Because systems and processes will also have the added benefits if we do this right to continue to lead our customers. And then you start to experience, really, also what some of your customers experience, right?” 

• Stijn took on the challenge of accepting the new role of becoming the “data boss” to lead the way not only for Collibra but for future organizations. 
• “Data Office 2025” is realized by Stijn and his team for future organizations that will face similar challenges as Collibra is experiencing. 
• This includes dealing with new data technology and new tools for data stakeholders across the business.

Stijn Christiaens: “All organizations, over time, we need to get better at mastering data assets. So all organizations, just like they have a chief financial officer. They will have a data boss or somebody responsible for data and maybe a data office just like their finance and HR, let’s say. So, we saw a trend, and then we said, okay, we can actually do this.”

Stijn Christiaens is the co-founder and current chief data officer at Collibra. He’s been involved with the company for 15 years and spearheaded the creation of the data office for Collibra.

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Data is a land mine for growth, and more businesses are realizing the great potential it holds. The value and insights from data have seen a rise in demand within organizations, and with this rise comes the expectation of a rapid turnaround time. However, this poses a significant challenge to central data teams, who handle data management and analytics. This led to the concept of data mesh, a decentralized approach that reduces friction and gives business domains the ability to quickly access and query the data they need.

In this episode, Sonali Bhavsar, Managing Director for Global Data Governance at Accenture, joins us to talk about the four pillars of data mesh. She discusses how companies can start applying data mesh to their workflows. Sonali also shares how Accenture helps its clients achieve data-led transformations.

Tune in to the episode to know more about data mesh, its significance and some tips to apply it to any business.

1. Learn about the four pillars of data mesh.
2. Understand why a federated approach is favored over a centralized system.
3. Discover how Accenture walks the talk in data mesh and data-led transformation.

• Bonus track episode on data mesh
• Connect with Sonali on LinkedIn

• Data mesh is an approach to reduce friction in data workflows in order to maximize the value of data.
• Its four pillars are data as a product, domain ownership, self-service data infrastructure, and federated computational governance.

• A federated approach allows your line of business to make decisions on their operation and what they value to meet regulatory obligations within their jurisdictions.
• A centralized approach has not been a sustainable model for the longest time. However, it’s most likely to work for an isolated, less hierarchy-driven organization.
• Traditionally, data governance decisions made outside of the business lineup and restructuring can lead to delays or unwanted results.
• For more complex organizations, each line of business might need to implement data governance in a certain way that may change or evolve within those lines.

[07:14] Self-service Data Infrastructure

• A big wave of data literacy is happening and the end goal is self-service.
• Self-service means that the consumption of citizens’ data, whether internally or externally, is built on trust.

• Data mesh is not a new concept, but it’s becoming a hot topic. There is now a stronger awareness of data as a product, which was more theoretical before.
• The decentralized form of data ownership came about because businesses now see more value in data and want to maximize it.

Sonali: “You really want to support that end data citizen to be flexible to use the data that they want to use it as, versus going through permissioning and asking for that data.”

• Some components will remain centralized. Federated simply means the catalog of data products leans toward business ownership rather than a centralized data ownership. 
• There is a big pivot on determining data quality and its lineage, which affects whether different industries can use data and what data product can or cannot be made from it.

• Industries such as financial services, insurance, and pharmacy already apply data mesh.
• High-tech companies are following suit.
• The line of businesses and the industries they’re in are altogether getting disrupted.

• Self-service is about firms investing in data catalog and data quality tools. Formerly, this was only done to observe regulatory protocols.
• Financial services have been always ahead of that curve because of regulations. Younger firms are often more flexible to pivot into something new.
• When you formalize the trust factor when dealing with data, the core pillars of data governance become ingrained as part of your ecosystem.
• There is now a clear delineation among different departments on how they want to handle data from different perspectives.
• The ideal situation is that businesses will have a clear responsibility, but also a fluid or gray area.

Sonali: “You want data mesh to be sustainable and operational, and keep data mesh as data mesh and not as a data mess down the road.”

• A product mindset means looking at different domains from an agile point of view and going through them iteratively.
• Data quality control is standardized to keep track of the data’s lineage.
• Consumers must be on the same page as businesses that the data product is validated and trustworthy for consumption.
• Ensure access and security are already validated to avoid bringing in random products that would endanger data consumption.

• Accenture is going through its own data mesh and transformation regularly.
• Accenture has built their own data marketplace as an asset that complements what Collibra brings from a catalog perspective.
• True data marketplace is an important aspect of data-led transformation. Accenture has an entire offering on data-led transformation for their clients across all industries.

Sonali: “If the data was not good, AI is never going to be your driver, and writing good machine learning algorithms and AI is amalgamation of your machine learning algorithms.”

• Accenture believes going through a data-led transformation is their major asset.

• The four pillars of data mesh are crucial for treating data as a product.
• Central data teams can and should still exist. The key is to act as an enabling force for the business.
• Change is hard; don’t try it all at once.
• Instead, make sure that the leadership is committed in the long haul. It will take restructuring and investment in skills and scalable technology.

Sonali Bhavsar is the Managing Director for Global Data Governance for Data and AI at Accenture. She helps enterprises reinvent businesses to be data driven and fully utilize their data with the latest thinking and solutions available for advanced analytics, data management, and data governance.

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

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Post a review and share it! If you enjoyed tuning in, then leave us a review. You can also share this with your friends and family. This episode will inform you about the four pillars of data mesh and their importance when bringing data into the marketplace.

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Data mesh is a relatively new concept that aims to reduce friction in maximizing the value of data. It distributes data control to different business domains that have experts in the data relevant to them. A catalog of data products contributes to the data owners' efficiency in curating and analyzing their data for business insights.

In this episode, Luis Romero, the Product Marketing Director at Collibra, talks in-depth about the four pillars of data mesh and how it can empower businesses. Jay Militscher, the Head of Data & Analytics at Collibra, also shares Collibra’s humble beginnings in executing data mesh and how they hope to improve their already robust system.

Tune in to the episode to know about data mesh, its significance, and how to utilize it within your organization.

1. Understand the significance and the four pillars of data mesh.
2. Learn how Collibra effectively implements data mesh.
3. Discover how to get started in bringing in data mesh within organizations.

• Data Mesh Blog Series
• Connect with Luis on LinkedIn
• Connect with Jay on LinkedIn 

• IT and data teams are not the experts on the data coming from the other departments.
• It’s best to have data in the hands of experts who will manage, curate, and cleanse data. Eventually, they turn the data into a product for its consumers.
• Analysts and business users waste a lot of time finding the data they need, and sometimes they even find difficulty in trusting the data.
• Data should be pre-packaged and available in a catalog for anyone who needs it, making it easier to verify and extract the right insights from it.
• The four pillars of data mesh are data ownership, data as a product, self-service data infrastructure, and federated governance.

• Most organizations have multiple business domains such as finance, engineering, marketing, etc.

Luis: “We should instead put that data into the hands of the true data stewards right within these domains.”

• The different business domains are best positioned to manage, curate, and make the data fully and readily available to be consumed by the business.

• Data owners with full knowledge and expertise about the data should treat data like a software product.
• A software product has a vision, strategy, and life cycle. We should treat data in the exact same way. 
• Treating data as a product means providing all the necessary facts and documentation. So that when it's in a catalog, it's ready to go.

• Luis observed that 99% of their customers complained about their complex data landscape because they have their data across different sources.
• Having various data sources can overwhelm companies when they retrieve and process data — more so when turning it into a usable product.

Luis: “We got to figure out a way to remove the friction from both the data producers and the consumers, and make it easy for them to go and find that data, bring that data together, understand the quality of the data, and again put it out there in a data marketplace, a data catalog, but again, make it very, very self-service.”

• Make data as self-service as possible by leveraging all kinds of cloud technology.
• Enterprise data catalogs can enable a one-stop shop for retrieving your data across all data sources.
• Set up a  data marketplace where all the users can go to find certified data sets.

• Large enterprises have acquired many independent business entities across multiple acquisitions over several years. 
• A healthy balance between reducing risk and supporting compliance is needed, or the different entities will feel constrained as they achieve their individual goals. 
• Some policies work for everyone within the organization, but some policies will need domain-specific context and control when dealing with their data.
• Sharing between the different entities under privacy regulations can happen, but it's about fostering the right balance of governance while still enabling their freedom.

• Collibra began its approach to data and analytics with business domain ownership first before there was a central data office through its business intelligence (BI)  functions.
• Collibra's data and analytics professionals received appropriate infrastructure and tools to enable BI functions in different departments.
• The data office's job is to grow a team with data engineering, infrastructure, machine learning, and data science skills to enable these business domains.
• Collibra had the infrastructure for a data mesh, so they didn't have to reorganize and are hiring even more data engineers and data scientists.

• The initial response from other business domains was to get better tools.
• The data office worked with other departments to help them modernize their technology stack, such as cloud systems.
• Their data office built the data and analytics technology stack, but the business users had total control over the data pipeline.
• In the beginning, Collibra faced difficulties due to not having a self-service infrastructure at scale in the cloud.

Jay: “We get to use our own product here at Collibra so that when each of those data product owners produce a data product, they're actually publishing it through the Collibra catalog so that each of those analytics folks shop for data products in the Collibra platform from each other.”

• The organization’s management must be committed to this approach because it isn't a one-time project but a way to move the whole organization forward.
• The management must be ready to invest in the skills, development, and cloud technology necessary to support this broadly and scalably.

• Today, Collibra's data office is lending advanced analytics with machine learning to other domains. Later on, each domain will do its analytics directly.
• Data mesh began centrally in the data team because they are building the infrastructure and process necessary to regularly operationalize models to retrain the other business domains.
• The data office wants to implement more automation and integrations across all the analytics needs and services of the different domains to reduce friction.
• In adopting data as a product mindset, Collibra will include all the documented data and development processes in the data catalog available for all data product owners.
• To implement federated computational governance, Collibra needs to start automating its governance workflows.

• Data mesh is about decentralization and distribution. It can start in a central data office that provides the data infrastructure to other domain-based data professionals.
• A data catalog can act as a marketplace where data product consumers can access data and use the data to publish their products in the same catalog.
• Federated governance provides global organizational oversight and some guardrails and policies while also maximizing local context.
• Successfully implementing data mesh principles requires strong data fluency, executive-level commitment, and funding for infrastructure modernization.
• Any company can start by picking a valuable domain ready to build a data product. Build up wins and learn to improve the implementation as you onboard more business domains.

Luis Romero is the Product Marketing Director at Collibra. He helps customers get a pulse of the up-and-coming trends in the market and identify their challenges. He also ensures that Collibra is positioning its products and solutions directly in line with its customers' initiatives and business outcomes.

If you want to reach out, you can contact Luis Romero 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 with your friends and family. This episode will help them implement data mesh within their organizations through the lessons learned by Collibra.

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

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

Data ethics may be a relatively new field, but its underlying principles are nothing new. Currently, regulations on data ethics are lacking, but organizations are still making data ethics a priority. Ethical data management is a must in today's data-driven world. 

In this episode, Anna Hannem, the director of Data Ethics & Use at Scotiabank, joins us to discuss the importance of data ethics, the best practices to ensure the ethical use of data across your organization, and her insights on the growing field of data ethics.

Tune in to the episode if you want to know how you could integrate data ethics as part of your company’s culture.

1. Find out why Scotiabank puts a premium on data ethics.
2. Learn how Scotiabank effectively implements data ethics within its organization.
3. Discover how the field of ethical data management is growing and where it will be in a few years.

• Connect with Anna over at LinkedIn

• Scotiabank's focus on data ethics started only a couple of years ago. The concept of ethical data management isn't new, but the field or profession is.
• Our world has become virtual and digital, making it data-driven. We can now feel the vast implications and impact when organizations use our data.
• Many big companies made mistakes that weren't necessarily illegal or had malicious intent but still led to breaching customer trust.
• Scotiabank is committed to upholding customer and public trust through data ethics.

[04:48] How Scotiabank Practices Data Ethics

• Scotiabank instills data ethics principles into its culture, processes, and procedures to educate within the organization and the industry as a whole.

Anna: “But in fact, data isn't black and white, right? It's how we collect it, where we collect it from, and how we're intending to use it.”

• Scotiabank implements an ethics assistant, an AI-powered tool that guides its model developers by giving insights on the proper use of data.
• In the US, some financial organizations negatively impact minority populations. The algorithm may be the problem despite bias, diversity, and discrimination training.
• The analytics team should be able to work with the business team, who then makes sure the customers are on the same page on what went into the algorithm for the unwanted outcome to happen.
• Scotiabank is guided by its main ethical principles of being fair, transparent, and striving to safeguard customer data. They treat accountability seriously.

[16:56] Developments in Scotiabank’s Data Management and Ethics

• Even without regulations on data ethics in North America, people are receptive to the processes and tools to instill data ethics.
• Anna observes that people are open to doing extra work to do what's ethical when it comes to customer data. 
• Make processes for data ethics easier so that people are inclined to do it repeatedly. 
• Data ethics started in Scotiabank’s Chief Data and Analytics Office before being implemented in other parts of the organization.
• Anna wished they already knew other areas that could have benefitted from their processes and implemented them there sooner for faster scalability.

• There's no degree yet for purely data ethics, but some universities offer it as part of their data analytics course.
• Scotiabank is partnering with universities to help them build programs on data ethics.

Anna: “There are not that many thought leaders yet in this space, and so as regulations are coming, we want to be influencing that, and we want to already be ahead of some of these curves and instilling best practices and learning from them ahead of time so [we know what worked well and what didn’t].”

Jay: “What's the safest way, the best way, the most appropriate way to drive value? And then it becomes an enabling thing as opposed to an obstacle or a barrier to progress.”

• Anna envisions more automation in data ethics and improving their ethics assistant tool to assist their model developers more easily.

Anna Hannem is the director of Data Ethics & Use at Scotiabank. She has been in the field for over ten years with experience in data management, governance, and analytics. She sees data ethics as the intersection of her many passions. She also has a degree in behavioral psychology, which she treats as an asset and influences her decisions in data ethics.

If you want to reach out, feel free to contact Anna 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 with your friends and family. This episode will inform them 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 me on LinkedIn. 

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

As technology grows, we've come to recognize the power of big data: how it influences company policies, consumer choices, and even government decisions. Data should not be just for profit — it should have an ethical and moral basis, which is where the importance of data ethics comes in. If you'd like to know more about data security and its ethical considerations, you're in for a treat this week. 

In this episode, Simla Sivanandan, Senior Manager of Data Intelligence at Collibra, joins us to talk about the importance of data ethics and how Collibra upholds data ethics within their organization. She also shares how the real problem is unconscious bias when dealing with machine learning (ML) and artificial intelligence (AI). 

Tune in to the episode to dive deeper into data ethics and unconscious bias.

1. Gain an understanding of what data ethics is all about.
2. Discover the significance of unconscious bias in handling data.
3. Find out how Collibra strategically instills data ethics within the company.

• An article on Lancaster University’s study on why weather forecasts were less reliable after the COVID-19 pandemic
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• Connect with Simla over at LinkedIn

• Simla initially found the concept of data ethics unnatural. Data is precise, while ethics are very subjective. 
• Ethics may seem simple, like doing the right thing, but what’s right can differ for different people.

Simla: “You see the power of data, where people are using that to make decisions that affect your life, your life quality, and all of that. So, we, as data professionals, always see the power of data. I think, as data citizens, it's our responsibility to use it ethically [and] wisely.”

• During the vaccine shortage at the start of the pandemic, the government used data to determine who was the priority, which has ethical implications.

• Data ethics is much bigger than machine learning (ML) and artificial intelligence (AI), which businesses use to personalize the customer's online experience.
• Companies must be aware of the purposes and risks involved in asking customers for their personal data.

Simla: “To me, really, the gold standard is: If I'm working in a bank, am I comfortable banking with them? If I'm working in an insurance company, am I okay to purchase that? That kind of tells me: Am I okay with the way they are treating my data, right? That's where I am that it's not just ML or AI.”

• Simla believes that the conversation around ML and AI involves unconscious bias.
• There are cases wherein we have no control over the data, even if we understand why it’s happening.
• Unconscious bias is a vital conversation to have in data ethics.

Simla: “Exclusion creates bias, and that might be unconsciously happening because we are not thinking through or we’re not picking a big enough sample set. That's where I'm coming from. So, it's always important as a data professional to be aware of this, right? As I limit my sample set, it can have unintended consequences, and we should address that.”

• Collibra is guided by its core values: being open, direct, and kind. The company strives to communicate directly, thoughtfully, and kindly.
• Collibra always thinks about how their work matters and its impact on many people and industries, which guides their ethical value system.
• Data ethics is everyone's responsibility, not just companies and governments.
• Social media should recognize its power and strengthen the moral framework within its algorithm to protect consumers instead of prioritizing more clicks and users.

Simla Sivanandan is the Senior Data Intelligence Manager at Collibra. She's a data management professional with over fifteen years of experience in the field and has worked on data governance, regulatory reporting, business analysis, and technology solutions support.

If you want to reach out, you can contact Simla 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 with your friends and family. This episode will inform them of the importance of data ethics and becoming aware of unconscious bias when dealing with data.

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

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

Join Collibra as we unite listeners around the importance of data and unpack its impact on the world. We sit down with customers, partners and thought leaders to discuss some of the hottest topics in the industry — from AI governance to the importance of data sharing to how to ensure data reliability and beyond. Welcome to The Data Citizens Dialogues.