Data Citizens Dialogues

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. Here are three reasons why you should listen to this episode:Understand how data governance is defined, and how it can be used to improve business processes. Learn why Adobe’s data governance is rooted in making trusted data discoverable, and how the company defines trust, data, and discoverable. Discover the water analogy of data governance and how data governance will continue to grow. Episode Highlights[00:57] How Data Governance Started at AdobeAdobe 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. [07:06] How To Define Data Governance 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.[10:35] How Adobe Defines Discoverable Data 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. [14:30]...

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,...

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 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

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...

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,...

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.