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. 

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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
• 
  
• 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.