Data Citizens Dialogues

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rapid growth, onboards employees daily, is expanding their market, and/or deals with systems and processes that may be out of date.In this episode, Stijn Christiaens, Founder and Chief Data Citizen at Collibra, joins us to discuss the importance of handling data and starting a data office.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. Here are three reasons why you should listen to this episode: Understand the importance of data in a growing company. Identify if your company needs to build its own data office.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.”Episode Highlights[07:47] More People Means More DataCollibra 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. [08:19] An Expanding MarketFor 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?” [09:58] Leading the WayStijn 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.”About StijnStijn 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.Enjoyed this Episode?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 and building a data office for your company’s future.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.

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.Here are three reasons why you should listen to this episode:Learn about the four pillars of data mesh.Understand why a federated approach is favored over a centralized system.Discover how Accenture walks the talk in data mesh and data-led transformation.ResourcesBonus track episode on data meshConnect with Sonali on LinkedInEpisode Highlights[00:33] The Four Pillars of Data MeshData 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.[03:47] Federated vs. Centralized ApproachA 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 InfrastructureA 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.[08:13] Data Mesh TrendsData 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.[11:59] Businesses’ Data Mesh Readiness 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.[14:34] Readiness to Adopting a Self-service InfrastructureSelf-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.”[22:09] Product Thinking MindsetA 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.[27:09] Data-led Transformation in AccentureAccenture 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.[29:36] Jay’s Key TakeawaysThe 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.About SonaliSonali 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.Enjoyed this Episode?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 you about the four pillars of data mesh and their importance when bringing data into the marketplace.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 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.Here are three reasons why you should listen to this episode:Understand the significance and the four pillars of data mesh.Learn how Collibra effectively implements data mesh.Discover how to get started in bringing in data mesh within organizations.ResourcesData Mesh Blog SeriesConnect with Luis on LinkedInConnect with Jay on LinkedIn Episode Highlights[01:50] How Data Mesh Can Help Business DomainsIT 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.[05:50] Domain OwnershipMost 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.[06:48] Data as a ProductData 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.[08:25] Self-service Data InfrastructureLuis 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.[11:07] Federated GovernanceLarge 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.[14:14] Data Mesh at CollibraCollibra 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.[16:16] Initial Response Inside CollibraThe 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.”[20:07] How Organizations Can Get Started with Data MeshThe 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.[21:20] Future of Data Mesh at CollibraToday, 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.[25:51] Jay’s Key TakeawaysData 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.About the SpeakersLuis 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.Enjoyed this Episode?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.Here are three reasons why you should listen to this episode:Find out why Scotiabank puts a premium on data ethics.Learn how Scotiabank effectively implements data ethics within its organization.Discover how the field of ethical data management is growing and where it will be in a few years.ResourcesConnect with Anna over at LinkedInEpisode Highlights[02:14] The Significance of Data EthicsScotiabank'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 EthicsScotiabank 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 EthicsEven 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.[22:06] The New But Growing Space of Data EthicsThere'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.About AnnaAnna 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.Enjoyed this Episode?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.Here are three reasons why you should listen to this episode:Gain an understanding of what data ethics is all about.Discover the significance of unconscious bias in handling data.Find out how Collibra strategically instills data ethics within the company.ResourcesAn article on Lancaster University’s study on why weather forecasts were less reliable after the COVID-19 pandemicConnect with Simla over at LinkedInEpisode Highlights[01:20] Connecting Data and EthicsSimla 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.[04:45] Unconscious BiasData 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.”[10:18] How Collibra Strategically Instills Data Ethics 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.About SimlaSimla 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. Enjoyed this Episode?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.