CDO Matters Podcast

The podcast discusses the challenges faced by CDOs, the importance of effective communication and delivering business value. It also explores adding fun and excitement to data and analytics, the evolution of roles, the collaborative approach in problem-solving, and misconceptions surrounding data quality.

Episode OverviewIn this 30th episode of the CDO Matter Podcast, Malcolm has an inspired exchange with the ‘Dean of Big Data,’ Bill Schmarzo. Bill is an accomplished author, teacher, and C-level executive with a depth of knowledge in building and running large-scale data operations in service of some of the biggest companies in the world.In the discussion of his most recent book, “AI & Data Literacy,” Bill shares his insights into the six key responsibilities that all consumers of AI must fulfill to optimize their relationship with this transformative technology. In becoming more AI literate, we increase the likelihood that the utility of AI will be maximized for the good of society and not just the corporate bottom line. A critical component of this includes quantifying the concept of ‘ethics,’ which Bill strongly advocates is not only possible, but is absolutely necessary.Beyond the topic of AI, Bill and Malcolm touch on some of the bigger challenges CDOs face daily, including the importance of connecting data investments to business outcomes and how to potentially solve the ‘value’ equation inherent to every data estate. For CDOs seeking inspiration about the future of data management in an AI-enabled future, look no further than this episode of CDO Matters.Episode Links & Resources:• Follow Malcolm Hawker on LinkedIn• Follow Bill Schmarzo on LinkedIn• Read Bill Schmarzo's new book, 'AI & Data Literacy: Empowering Citizens of Data Science'

Episode OverviewIn this 29th episode of the CDO Matter podcast, Malcolm has an engaging conversation with a long-established thought leader and noted author in the data and analytics space, Tom Redman. Malcolm and Tom discuss Tom’s recently published book, ‘People and Data,’ where Tom shares his thoughts on the necessity of engaging data consumers to address some of the bigger data-related challenges all organizations face, particularly data quality. Tom shares his recipe for engaging as many stakeholders “without data in their titles” as possible, and the need for CDOs to assert their leadership through an acknowledgment many legacy approaches to data management simply aren’t working, including data governance. Rather than seeing people as the core problem data leaders need to overcome, Tom makes a strong case that people are, in fact, the solution – especially those outside traditional data roles.Episode Links & Resources:Follow Malcolm Hawker on LinkedInFollow Tom Redman on LinkedInRead Tom Redman's latest book, 'People and Data'

Episode SummaryUnleashing the power of Data Products, and transforming CDOs into Product Visionaries.In this the 28th episode of the CDO Matters podcast, Malcolm explores the topic of Data Products and what CDO’s should know about them. Malcolm taps his expertise as a prior Chief Product Officer to explore the differences between data products as they exist within a Data Mesh architecture, versus the more general concept of data products outside a Mesh. Malcolm takes issue with the idea of a ‘bottoms up’ approach to defining data products, and instead advocates that CDOs take a more practical view of a data product as anything that a customer is willing to pay for. Malcolm urges CDOs who are curious about data products to ensure they first establish a business justification for their efforts – one which focuses more holistically on the benefits that can come from the integration of the discipline of product management into a data and analytics function. Malcolm shares his insights that data products should be treated as an output of a product management focus, and the many benefits that are realized when CDO’s embrace the discipline of product management.Lastly, Malcolm dives into the role of a data product manager, sharing details on the core responsibilities for these experts and how they would differ from the concept of a data ‘owner’. Along the way, Malcolm touches on the misguided idea of data literacy, and how taking more a product-centric approach to building and supporting data solutions could enable CDOs to completely transform their customer relationships.Episode Links & Resources:Follow Malcolm Hawker on LinkedIn

Episode OverviewWouldn’t it be great to have a playbook for how to be an effective data leader?In this episode of CDO Matters, Malcolm has an in-depth conversation with an extremely successful IT and data leader, Renée B. Lahti. With CIO experience spanning multiple industries for some of the best-known brands on the planet — including SC Johnson and Symantec — Renée’s depth of experience shines in this conversation about the current state of the role of CDO, including Renée’s insights on the key factors of CDO success.Failing fast, redefining value, hiring for diversity and being more agile: These are just some of the many fascinating topics discussed in this episode, which could be considered a playbook for any data leader looking to emulate the success of an accomplished data leader.Episode Links & Resources:• Follow Renée B. Lahti on LinkedIn• Learn about the DataEthics4All advisory council

Episode OverviewToday, enterprise data is not only exponentially growing in volume, but in sheer complexity. And how can today’s data leaders extract value if they are unsure where to even start?In the latest episode of the CDO Matters Podcast, host Malcolm Hawker sits down with Ontotext Chief Marketing Officer Doug Kimball to discuss the wide-ranging applications of next-generation graph database technology.Listen to Malcolm’s discussion with Doug to learn more about:• The advantages of graph database technology in particular use cases• How graph databases can reveal previously unknown relationships• The speed advantage of graph technology when querying data• How AI and ML will continue to augment graph technology and reduce human interventionAbout the GuestDoug Kimball is the Chief Marketing Officer of Ontotext, a developer of knowledge graph software helping global enterprises gain knowledge and insights from their diverse data. Doug is an accomplished marketing and communications executive and team leader focused on leading strategic growth within global and domestic organizations.Episode Links & Resources:• Follow Doug Kimball on LinkedIn• Learn more about Ontotext on their website

As companies grow, they often hire dedicated product managers to oversee all the planning, development and launching a product. Business leaders recognize that this is a critical need and expect products to be managed holistically from ideation to development — so why don’t more companies take that same approach when solving enterprise-wide problems with data and analytics?On this episode of the CDO Matters Podcast, Malcolm Hawker sits down with Bethany Lyons to discuss her role as KAWA Analytics’ Chief Product Officer (CPO) before diving into a range of timely data topics including:• “Self-service’ data governance• Product management approaches• Centralized security vs. decentralized data modeling• Enforcing data governance/security policies• The definition and uses of data build tools (DBT)• The longevity of CDO tenures• Key attributes of a good product manager…and so much more!EPISODE LINKS & RESOURCES:Follow Malcolm Hawker on LinkedInFollow Bethany Lyons on LinkedInVisit KAWA Analytics’ website

As we get carried away with modern data trends and new technical developments, it's easy to forget the rich history behind computable data and analytics dating as far back as World War II in the 1940s. Luckily, we have a data historian to remind us!Host Malcolm Hawker sits down with NY Times Chief Data Scientist (CDS) Chris Wiggins to chat about his new book and dive into what led us to this moment in the data space.The two discuss:- The ethics behind using data for social concepts- Quantifying social data- Early stages of AI and the creation of data computation - The history of machine learning- The internet boom of the 90s- AI vs. copyright laws...and so much more!Episode Links & Resources:Follow Malcolm Hawker on LinkedInFollow Chris Wiggins on LinkedInCheck out Chris Wiggins' new book

Who better to discuss an annual Gartner data summit than a former Gartner analyst himself? Host and former Garter analyst, Malcolm Hawker, sits down with Profisee's Director of Digital Marketing, Ben Bourgeois, to talk about his experiences and key takeaways after attending this year's Gartner Data & Analytics Summit. Other topics of discussion include:Demystifying the data fabricMalcolm's journey in releasing 20+ episodes of the CDO Matters podcastDefining data by its business value...and more!EPISODE LINKS & RESOURCES:Follow Malcolm Hawker on LinkedInFollow Ben Bourgeois on LinkedInRegister for an upcoming episode of CDO Matters LIVE

ChatGPT. Composable data and analytics. Connected governance. The data fabric. These are all hot trends and much-hyped tools and frameworks in 2023. But one of the challenges with “bleeding-edge” technologies is a lack of a clear definition — much less a clear guide on how to take advantage of these nascent tools. Good thing we have a former Gartner analyst with thousands of hours of client inquiries under his belt to help finally demystify one of the most-hyped trends in data management today: the data fabric. In this 22nd episode of CDO Matters, Malcolm focuses exclusively on providing an extremely “deep dive” into the data management architecture known as the data fabric. The data fabric has gone from relative obscurity to nearly the top of Gartner’s hype cycle in just a few short years. For this reason, forward-leaning CDOs should understand exactly what the data fabric is and how it can benefit their organization.   Starting with a basic definition of a data fabric, Malcolm proceeds to break this rather complex phenomenon down into its component parts — using language that is precise but highly digestible for any non-technical CDO or data leader. Translating the complexity of a fabric into understandable critical capabilities, he shows how a data fabric can eventually be leveraged as a transformational tool for any organization. For those wondering exactly what capabilities are needed to enable a data fabric, Malcolm reviews the top three capabilities that he believes must be present for any solution to be considered a data fabric. Armed with this information, it becomes clear that the data fabric remains — at least in the short term — an aspiration for most companies given the sophistication and governance maturity needed to enable them.   Malcolm reviews a conceptual architecture of the data fabric and discusses how fabric capabilities will be deeply integrated into — and across — several legacy data management systems, including master data management (MDM), data quality, data governance and data integration platforms. He challenges the notion that any one solution will be used to enable data fabrics — and instead outlines how several software and analytical solutions will need to be deeply integrated to enable fabric capabilities.   Finally, Malcolm ends his demystification of data fabrics by sharing a few key considerations to help CDOs cut through all of the hype related to data fabrics. This includes practical actions data leaders can take now to better position themselves for leveraging data fabrics soon.   Update from Malcolm: This episode was recorded before the production launch of ChatGPT. In the span of just a few weeks, I believe the data fabric has gone from a conceptual framework to something that could easily be envisioned within a modern data estate.   Put another way — if the entire internet before 2012 could be used to train an AI-enabled language model, all your enterprise data could most certainly be used to train AI models that are optimized to support data management use cases. I now believe the data fabric is how AI will be operationalized, at scale, to optimize — and eventually automate — the creation, consumption and management of data within your organization. I’ve struggled for the last two years to visualize exactly how data fabrics could be implemented at scale, but thanks to ChatGPT, I no longer have this struggle. Hopefully after watching — or listening — to this episode, you come to a similar conclusion.  Key Moments [7:51] The Hype Behind the Data Fabric[10:46] Diving Deep into the Fabric[13:01] Data Fabrics Defined [21:31] Key Fabric Capabilities [22:15] Active Metadata[27:01] Incorporating AI Technologies[29:56] Synthesizing Metadata with an Intelligence Layer[35:31] The Data Fabric Architecture [40:06] Key Fabric Considerations [52:31] Closing ThoughtsKey Takeaways The Future of the Data Fabric and AI Dependency (12:38) “We are making a pivot away from people defining the [data governance] rules, the integration patterns, the data quality standards. We are moving away from people deciding that to robots deciding that. That’s a spectrum. We’re largely people-driven today…we’re early in the days of the spectrum from entirely people-driven to entirely robot-driven. We’re early in those days, but where the data fabric goes and the ultimate path here is towards a world highly dependent on the [machines].” — Malcolm HawkerWhat Can Metadata Do for You? (17:40) “What active metadata really means is that, if you had a lot of metadata, and you has some pretty sophisticated analytical tools, and you had some pretty sophisticated new technologies, you could make that data tell you a lot of things about the state of your data enterprise. For example, in theory, you could know when data was accurate or inaccurate.” — Malcolm Hawker The Current State of Data Fabrics (46:12)“Data fabrics don’t exist yet. You can’t go buy one. There is a ton of promise here. But between where we are, and where we need to go and between concept and theory, there are some really major roadblock issues we need to overcome. And frankly, there’s a lot of technology that doesn’t even exist yet.” — Malcolm Hawker EPISODE LINKS & RESOURCES: Follow Malcolm Hawker on LinkedIn