PODCAST

The Data Download

Collibra

Join Jay Militscher, Head of Data & Analytics at Collibra, as he explores some of the hottest topics in the industry from building a data office, to ESG, to the ethical use of data, and beyond. In this five-part series, industry leaders from around the globe share their best practices, learnings, and predictions for the future. But it doesn’t stop there…we also have five bonus episodes in this series that feature our very own Collibrians. These “Inside Collibra” episodes show how Collibra’s very own Data Office is tackling these complex issues. Welcome to The Data Download!

Inside Collibra: How to build a data office
Today
Inside Collibra: How to build a data office
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rapid growth, onboards employees daily, is expanding their market, and/or deals with systems and processes that may be out of date.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
Data mesh, not data mess with Sonali Bhavsar, Accenture
1w ago
Data mesh, not data mess with Sonali Bhavsar, Accenture
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. Resources (Bonus track episode on data mesh) Connect with Sonali on (LinkedIn) Episode 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 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. [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....
Inside Collibra: Treat your data as a product
Jun 22 2022
Inside Collibra: Treat your data as a product
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. Resources (Data Mesh Blog Series) Connect with Luis on (LinkedIn) Connect 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...
Inside Collibra: Comparing your ethics framework to spicy foods
Jun 8 2022
Inside Collibra: Comparing your ethics framework to spicy foods
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 pandemic Connect with Simla over at (LinkedIn) Episode 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...
Don’t just talk the talk with Anna Hannem, Scotiabank
Jun 8 2022
Don’t just talk the talk with Anna Hannem, Scotiabank
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 (LinkedIn) Episode 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 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. [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