CDO Matters Podcast

As an expert in your field, you need to approach your work from every perspective. If a medical doctor can deliver a particular diagnosis for a patient, then they should also be able to do the same for themself. The same principle applies to data organizations and how they approach their own enterprise data.As a CDO, if your internal customers must pay to access the data or insights you provide, would they feel confident in your ability to deliver value from your data products? Is your business singularly focused on understanding and meeting customer needs — both now and on the road ahead? If not, then what’s standing in your way? For many CDOs, the answer lies in making that crucial transition from being data-driven to data product-driven. In this episode, we discuss making the shift to becoming data product-driven. Malcolm is joined by Saleem Khan, Discovery Data’s Chief Data & Analytics Officer (CDAO). During the discussion, Saleem shares valuable insights on thriving within the CDO’s function for an organization whose core product is data. Saleem’s shared insights include a framework for managing a data product pipeline, leading a team of data product managers, and implementing processes to anticipate future market demand. Saleem’s recommendations even include insights on a sales enablement methodology that can be used to ensure data consumers will derive benefit from data products and the critical role that a marketing function can play in ensuring data customers have a clear understanding of how a data product helps deliver a specific business outcome. Given Saleem’s role is primarily as a Chief Product Officer (CPO), it should be no surprise that he won’t discuss data quality, data governance, data pipelines or anything else deeply focused on data management. Instead, he focuses on several best practices and actionable insights for how to approach the role of a CDO should you wish to ensure stakeholder value remains at the core of everything you do. Key Moments[1:57] Saleem’s Career Journey to Discovery Data[3:45] How Data Can Be Monetized/Validated[6:30] RAD Defined[9:12] The Role of a CDAO[13:10] Implementing the FRAME Framework[19:30] Aggregating Customer Data[21:40] Data Governance as a Business Model[23:35] Anticipating Consumer Interests[25:20] Taking a Product Management Approach[27:25] The Problem with ‘Data Literacy’[31:24] The Current State of Blockchain[34:00] Data as a Consumerized Product[38:51] The Fragmentation of Data SharingKey Takeaways Predicting Debt and Data Monetization (3:45)“One of the first forays I truly had into data science and the data world was building out a prediction model…where we would try to determine which companies were most likely to issue debt. So, on one end of the spectrum, you’ve got Microsoft and Apple which are incredibly cash-rich and they usually don’t borrow…then on the other end of the spectrum, you’ve got companies that require a lot of cash because they’re very capitol-driven and capitol-intensive and have to take out a lot of debt. And whenever debt is taken out, SMP has to take a rating on it.” — Saleem KhanThe Role of a CDAO (9:14)“As Chief Data and Analytics Officer, 50% of my job is sales and marketing, the other 50% is product development…My job becomes making sure I communicate with customers, especially our largest customers, as often as I can…Data is our product, so [for customers] who better to hear from than the Chief Data Officer about what trends there are, and where you are taking your data set?” — Saleem KhanAdopting the FRAME Framework (14:12)“We have a framework as part of our data operations. We have another framework called, FRAME. FRAME is an acronym that stands for ‘Fuel, Refine, Analyze, Magnify and Execute’. Each of these components is a different part of the data operations lifecycle…There are multiple ways to distribute your data and your content to customers.” — Saleem KhanThe Problem with Data Literacy (27:25)“I happen to be vehemently opposed to that phrase [data literacy] because it turns the problem to a user problem where I think it should be on the creation side. If you’re creating a data product, if you’re creating something for consumption…and they don’t know how to use it and don’t know how to derive value from it, I would argue that that is a product failure, not a user failure.” — Malcolm HawkerCreating a Product Narrative (29:25)“When you have a product that has a miss, there is one of two reasons: One could be that it was just a terrible product, and two could be ‘wrong place, wrong time’…but the products that do work out, they tend to work out because the CDO is working directly in tandem with sales and marketing to create that narrative, to tell that story of value to the customer… to make sure that customer has a simple and crystal clear understanding of how this data product will deliver a specific business outcome.” — Saleem KhanAbout Saleem KhanSaleem is the Chief Data & Analytics Officer (CDAO) at Discovery Data. With over 15 years of experience in the data space as a patented product, data and technology executive, he remains proficient at using data-driven and analytical techniques to deliver new digital products and implement digitally-enhanced process transformations.EPISODE LINKS & RESOURCES:Follow Malcolm Hawker on LinkedInFollow Saleem Khan on LinkedInVisit Discovery Data’s website

When it’s time to make a big decision, it’s always great to get a second opinion! This is especially true when a major business decision comes your way. When it comes to something as foundational for your business as your enterprise data, it’s best to consult the experts. Growing your business starts with an effective data strategy. But what if you could bring your burning data questions to a seasoned professional without the financial burden? There’s nothing better than hearing advice and insights from a leader in the field. Host Malcolm Hawker does just that kicking off his inaugural monthly session of CDO Matters LIVE. In this special live episode, he answers top-of-mind inquiries about all things master data management (MDM), data governance, data fabrics, business value and more. Joined by Profisee’s Director of Digital Marketing, Ben Bourgeois, Malcolm opens the episode by posing the question, “Why do we separate analytical [reporting] uses of data from operational [data management] uses of data?” Data and analytics are often used in the same sentence but treated as two separate items. He immediately points out the relation between the two with analytics often leading to operationalized insights and decision-making which ultimately leads to action within a business. While separating them provides freedom on the analytical side, it often leads to analysts then defining their own business rules, data definitions and how they want the data to be shown in future reports. This further isolates the two areas from being used cross-functionally across multiple departments for individual purposes. Ben then poses the question of whether this separation stems from data ownership within an organization. Malcolm clarifies the ideal definition of data ownership by explaining how it refers to data laws, rules and standards within a business that are applied cross-functionally. The other aspect of ownership then relates to enforcing those established policies. Ultimately, he concludes that assigning individual owners only hurts rather than helps a data-driven enterprise. From there, Ben provides Malcolm with some of the more notable and relevant topics and inquiries hitting the data space as of late. The topics discussed throughout the remainder of the episode include: Transitioning from top-down or enterprise-wide initiatives to more federated approaches focused on business units The value of treating data as a product Effective marketing and the difference it makes for a master data management (MDM) or IT initiative Using Salesforce and other CRMs as a substitute for MDM within an organization …and various questions submitted during the live Q&A! If you want to ask your burning data questions live with Ben and Malcolm, be sure to register for the next of our live monthly sessions of CDO Matters LIVE. EPISODE LINKS & RESOURCES: Follow Malcolm Hawker on LinkedIn Follow Ben Bourgeois on LinkedIn Register for an upcoming session of CDO Matters LIVE

The truth isn’t always black and white. Sometimes, it requires more context and background when attributed to different scenarios and situations.The same can be said about your data and whether a “single version of the truth” can be properly applied to multiple use cases for your business.In this episode, Malcolm interviews Jeff Jonas, the Founder, and CEO of Senzing — a software company on the leading edge of developing “entity resolution” solutions — which solve the growing challenge of uniquely identifying people or objects across multiple systems of duplicate, low-quality data. Malcolm and Jeff discuss how advances in technology are fueling more modern forms of entity resolution, where companies are now able to implement more context-centric approaches to complex matching, particularly within their master data management (MDM) programs. As technical as “entity resolution” may sound, the two uncover the global effect this technology has on people each day — including the job of the Chief Data Officer (CDO). Also known as “disambiguation” or “fuzzy matching,” effective entity resolution allows software systems or data stewards to decipher whether records for Richard Smith and Dick Smith may represent the same person, even when it is not overtly suggested by the data.Jeff describes how entity resolution sits as a foundational component of data within MDM, customer relationship management (CRM), know your customer (KYC), supply chain and every other major business process that relies on accurate, trustworthy data. Jeff correctly notes that aside from horrible customer experiences that may arise from a lack of effective entity resolution, “it creates a lot of waste for companies to think you are two or three people instead of one.” Citing a person’s example of having their name represented three distinct times in a hotel loyalty club database, he emphasizes the toxicity that comes with a lack of focus on entity resolution for companies who are trying to be both customer and data-centric. While many companies — particularly those already investing in AI/ML — may be attracted to implementing DIY solutions for entity resolution, Jeff notes that it’s “super expensive to build”, especially given the complexity and diversity of data, and even language itself. The ability to understand meaning across objects, cultures, languages and even alphabets is at the core of reliable entity resolution and building bespoke solutions for tackling these complex problems — at scale — is beyond the capabilities or budgets of an overwhelming number of companies. When considering a “single version of the truth”, Malcolm unpacks the 30-year history of large, monolithic enterprise resource planning (ERP) suites that created the mindset of master data only living in a singular place within the organization. Thanks to the democratization of IT, the “single version” mindset is shifting both as a practicality and as a business need. Today, master data can be sourced from a single location while supporting multiple versions of truth based on the use case of that data. In talking about the evolution of large-scale entity resolution, its use in MDM to enable multiple versions of the truth and the legacy requirement to have data stewards manually review records, Jeff notes, “There are definitely times…when you want a human to take a look and make an adjudication. But, I will tell you, in large-scale systems, you don’t have enough humans.”Rather than adding more people into data stewardship roles to support higher confidence matching, Jeff advocates the approach of widening the pool of data used by entity resolution processes — beyond just name and address — to make match decisions, including the possible use of third-party data sources. The last few minutes of the conversation go deep into AI/ML, and how these new technologies are used to augment human data stewardship processes. Jeff makes a great case to suggest that most stewardship tasks could be mostly automated, but that many companies are unable to duplicate pure human judgment. Throughout the conversation, Jeff and Malcolm take extremely complex technical issues and make them digestible and relatable to CDOs — consistently refocusing on how using entity resolution is critical to establish truth in an organization, especially when using MDM systems to manage said truth. CDOs who want to have more informed conversations with their technical staff about the role of entity resolution going beyond just “fuzzy matching” will find this episode of CDO Matters highly insightful. Key Moments[3:42] Entity Resolution Defined[7:50] The Impact of Poor Data Quality[11:23] The Death of the ‘Single Version of the Truth’[15:45] Entity Resolution Failures[24:03] Understanding Unique Entities[26:13] The Cost of Being Wrong[29:10] Human Intervention vs. Trust in the Algorithm[32:05] Gaining New Insights with MDM[35:09] Valuing Human Judgment [40:03] The Future of Entity Resolution in Digital TransformationKey Takeaways What Is Entity Resolution? (1:58)“Entity resolution is recognizing when two things are the same…it’s [also] called ‘fuzzy record matching’, ‘link detection’, ‘disambiguation’, ‘match/merge’ and lots of names. It’s been congealing into this term ‘entity resolution’ and is being more used. And really, the definition that I would have for it is recognizing when two identities are the same despite being described as different.” — Jeff JonasThe Cost and Complexity of Efficient Entity Resolution (5:15)“This problem [with poor entity resolution] is ubiquitous, and it turns out, is super expensive to build. People think you can hire a little team and do some AI/ML and think you are going to match well. And I am telling you, you cannot create something competitive in five years for twenty million.” — Jeff JonasIs the ‘Single Version of the Truth’ Dead in 2023? (13:07)“There are still many people saying you need a single version of the truth…At an operational level [within a company], there are multiple versions of the truth. The way a marketer would define a customer is different than the way somebody in finance may define a customer, particularly B2B, where one would be a ‘sell to’ and the other is a ‘bill to,’ and they are both correct. A lot of people still think that is what MDM is, and it can be that if you want it to be that, but it doesn’t have to be.” — Malcolm HawkerWorking with Multiple Versions of the Truth (14:12)“I think the ‘single version of the truth’…those are the dark ages, the dark days. The truth is you really want systems that can present truth to the eye of the beholder. It’s about who the recipient is. But there are two forms of truth: One is about separating ‘who is who?’ from which attribute is the best attribute…and how many entities does the organization have? Do you really want marketing to have a different [data] account than finance?” — Jeff JonasHuman vs. Software: The Role of Human Judgment (29:38)“There are definitely times/cases in data when you want a human to take a look and make an adjudication. But I will tell you, in large-scale systems, you don’t have enough humans. Second, I will tell you, ‘How does the human do it?’ The human is using additional data. It’s either data stuck in their head or they’re searching it up somewhere…but a lot of times, you have to actually do research…so making these decisions on records with some human intervention is about adding data. And one of the things that we propose is that there are kinds of data that is the initial data needed.” — Jeff JonasAbout Jeff JonasJeff Jonas is not only the CEO and Founder of Senzing but also the Chief Scientist. Since 2016, the organization has provided fast and easy API for accurate data matching. For more than 30 years in the field, he has been at the forefront of solving big data problems for both companies and governments. National Geographic recognized Jeff for his talents in the data space, referring to him as the “Wizard of Big Data.”EPISODE LINKS & RESOURCES:Follow Malcolm Hawker on LinkedInFollow Jeff Jonas on LinkedInVisit Senzing’s websiteLearn more about ‘entity resolution’View a PDF of Jeff’s publication, Privacy by Design in the Age of Big Data

For CDOs to be successful today, they need to think more like a product manager. After all, product managers are responsible for every facet of their product. They determine which customer needs their products fulfill and define what success looks like for their product. And they’re ultimately responsible for reporting that performance to executive leadership. Hopefully those responsibilities sound familiar to listeners of the CDO Matters Podcast — except that for them, their core product is data. In our latest episode, Malcolm is interviewed by Rishabh Dhingra on the Inspired Podcast, where Malcolm shares his perspectives on the growing trend toward treating data as a product. CDOs who are considering the addition of the product management mindset into their business will find his perspectives refreshing — given the great value that he believes product managers, and treating data as a product, can bring to most data-driven organizations. Malcolm shares details on his expertise in the field of product management, having been a Product Manager, a Product Director and, ultimately, a Chief Product Officer (CPO). Having managed teams of product managers in several software companies through the heyday of the internet boom, Malcolm has first-hand experience working in highly agile and fast-paced environments — where quickly adapting to changing needs was a daily struggle. As Malcolm describes it, the core DNA of a good product manager is all about problem-solving — where professional product managers are trained specifically to determine the optimal combination of product attributes to address customer needs given known constraints on time, money or resources. Product managers also know how to build business cases to support investments in their products; otherwise, businesses wouldn’t invest in them. One of the biggest benefits of implementing more product management into data management is that they will provide the skills necessary to build business cases for data and analytics products — being a standard operating procedure in the world of product development. When it comes to data as a product, Malcolm believes many data leaders are often missing the mark by incorrectly focusing efforts on defining products rather than customer needs. He explains that it ultimately doesn’t matter if a data product is a field, an attribute or an entire table — but what matters is if a customer need is solved. The need for data people to take a “bottoms-up” approach to data products — where the product is a function of the available “raw materials” — is a major flaw in data organizations that product managers could help a CDO avoid since product managers are inherently focused on solving customer needs. Why should companies consider managing data as a product? According to Malcolm, companies that deeply integrate product management practices into the field of data management — and who deeply embrace all aspects of data as a product — will drive competitive differentiation. The benefits of integrating product management practices into data management are many, but his highlights include better business cases, resource prioritization, cost management and many others as just a small subset of the universe of benefits with more focus on data as a product. By the end of this episode, current or aspiring CDOs who have not already considered the integration of product management practices into their data organizations — both for products and the supporting organization — should have a roadmap for implementing these PM practices into their data organization. Key Moments [1:15] Transitioning from Product Management into Data and Analytics [7:06] Resolving Customer Problems with Customer Data [12:30] Malcolm’s Role at Profisee [15:40] Confusing Data Migration and Warehousing with Data Management [19:20] MDM Implementation: Successes and Fails [27:30] Why Product Managers Make Great Business Leaders [29:40] Defining Data as a Product (DaaP) [37:20] Applying Data as a Product Within Your Organization [47:30] The Future of Data and Analytics Key Takeaways Malcolm’s Role as a Thought Leader and MDM Evangelist (12:40) “Primarily, I’m focused on evangelism…it is my job to raise the awareness in the market of the importance of data and analytics, the importance of master data management (MDM). How MDM can drive value for organizations and how it can be used as a foundational element for digital transformation.” — Malcolm Hawker Data Warehousing vs. Data Management/Governance (15:40) “So many companies that if you just put all of the data in one place that you have solved for data quality. That you’ve solved for having a single source of truth. That you have solved for having consistent data governance. That couldn’t be farther from the truth. All you’ve done is put your data into one bucket. You may have limited the number of queries that you have to make or the number of sources that you have to go into. You may have made it a little easier to centralize permissions and access to that data…but putting it into one place doesn’t solve for that issue…I am all for using data warehouses and I am all for using cloud-based solutions for housing data, but if you don’t address some data quality issues, you’re going to have a lot of problems.” — Malcolm Hawker Limiting Your Scope (23:45) “Most data and analytics leaders are not building business cases. That means they struggle with scope. But product managers know you’ve only got time, people and money. If one of those has to go, then you have to limit your scope…If you don’t have a business case, then it’s really hard for you to limit your scope. It’s really hard for you to prioritize. It’s really hard for you to understand where the biggest benefits are going to be…you can’t differentiate whether A or B or C is going to drive value for the business. It inevitably leads to scope creep. It inevitably leads to situations where data and analytics leaders can’t justify the things that they’re doing.” — Malcolm Hawker Bringing Product Management to the Data Space (33:42) “If we could apply more product management into data management, data management would be a much better place. I would argue it would be far more customer-centric, it would be far more effective, it would be far more productive, we would be able to quantify the business benefits that we were driving, we would be able to prioritize our efforts, we would be able to spend money more efficiently, but what we do instead is we get into these arguments about, ‘What is a data product?’ Is it a field? Is it an attribute? And it’s not helping, because it’s backward. Start from the need.” — Malcolm Hawker Where are Data and Analytics Headed? (47:35) “In terms of the future, there are some things that we know are here and will continue to be here and continue to expand [into] what I would have called when I was at Gartner, augmented data management. What that means are the application of AI and [machine learning] and cool new technologies…to provide added layers of automation in the world of data management. I would put the creation of management of data fabrics in the bucket as well…with limited numbers of people, we need more and more automation in the data space.” — Malcolm Hawker About Rishabh Dhingra Rishabh Dhingra is the host of the Inspired podcast and is currently a Solutions Consultant in Business Analytics at Google. Having graduated from the Thapar Institute of Engineering & Technology in 2011, he serves as a veteran in the field with more than 11 years of experience architecting, designing and developing enterprise-scale business intelligence and analytics solutions for insurance, legal, banking and other industries. EPISODE LINKS & RESOURCES: Follow Malcolm Hawker on LinkedIn Check out the Inspired podcast Follow Rishabh Dhingra on LinkedIn

First impressions are everything. As a brand-new CDO, it is important to hit the ground running with a data strategy that pinpoints key use cases and quickly delivers value within an organization.In our 14th episode, Malcolm shares his perspectives on the top deliverable for most CDOs: the definition and execution of a corporate data strategy. He outlines his model for a ‘Data Strategy Minimum Viable Product’ (MVP), which embraces a highly iterative and pragmatic approach to defining and executing a strategy. This approach starkly contrasts more traditional approaches which often take years to deliver any business value. Rather than separating a strategy’s definition from its execution, the Data Strategy MVP deeply interconnects the execution with the ongoing evolution of the data strategy — where CDOs can earn the right to change corporate cultures or operating models by delivering business value rather than management edict. Malcolm argues that forcing a business to wait years — or even several months before it will realize any benefits of a data strategy — is a key reason for shortened CDO tenures. While a typical approach to a data strategy would involve 6-12 months of business analyses and requirements gathered across an entire enterprise before actually executing a strategy, the Data Strategy MVP hinges on a razor-sharp focus on quickly identifying a few key business outcomes.This is quickly followed by the definition and execution of a data strategy specifically to address those limited sets of outcomes. By repeatedly focusing on a small set of outcomes, organizations can successfully execute a holistic data strategy. Malcolm also dives into how taking a more tactical and results-driven approach to implementing a data strategy requires a strong data leader who can balance longer-term needs — such as defining an adaptable technology architecture or the right governance model — against short-term needs. Finding this balance will not be easy but is necessary to ensure short-term decisions do not compromise the ability to fully align the data to the business strategy in the long haul. Another key to the Data Strategy MVP — as outlined in the shared model — is the acknowledgment of the several business characteristics that CDOs cannot change in the short term. These include the data culture, corporate operating models and overall data and analytics maturity level.While more traditional strategy approaches would place changes to these things as strategic dependencies, Malcolm instead argues they should be considered more as constraints. This helps guide decisions on the best business outcomes the evolving strategy should be focused on in the immediate future. CDOs who have been tasked to execute a data strategy should find this episode of CDO Matters highly useful — especially those concerned that too much of a focus on data strategy is hampering their ability to deliver value.Newer CDOs unfamiliar with more agile approaches to program management will also benefit from this episode — as will those aspiring CDOs who are looking to make an impact for their business partners by finding ways to bring value in more iterative ways. Key Moments[2:10] Why Are CDO Tenures So Short-lived?[4:12] The Responsibilities of the Modern CDO when Establishing a Data Strategy[7:02] Creating a Centralized Data Culture[13:05] MVP Data Strategy Attributes[18:45] Defining Your Outcomes[20:52] Filling Gaps in Governance Maturity [24:30] Leveraging Analytical Insights Rather Than Operational[28:15] Delivering Realistic Business Outcomes[30:40] Succeeding as a CDO[33:05] Establishing an Analysis Roadmap and Governance Framework[36:46] Placing Technology at the End of Your Strategy[42:10] Summarizing How to Provide Significant ValueKey Takeaways Why are CDO Tenures so Short-lived? (10:32)“I would argue that one of the reasons for very, very, short CDO tenures is that [CDOs] rightfully identify that some fundamental ways the business operates need to change… however when you put those organizational changes or those cultural changes or those operating model changes as dependencies to be able to deliver value — and if you make those changes your top priority — you are going to have a hard time delivering value in the short term.” — Malcolm HawkerStart Small and Grow from There (18:32)“A key to this whole thing — being MVP-driven — is to focus on a limited number of outcomes... not all of them — one or two. If you want to take an MVP approach to a strategy — and I would argue it’s the best thing you can do is to show some quick wins and show some value and not get stuck in an 18-month long discovery and consulting engagement.” — Malcolm HawkerDelivering Plausible Value (32:25)“If you want to succeed at a data strategy…you do need to incorporate some idea of executing against the strategy as a part of your strategic model, where you plan to deliver iteratively as a part of executing on that strategy. Where that strategy evolves based on your ability to deliver.” — Malcolm HawkerLimit Your Scope and Don’t Boil the Ocean (36:14)“For now, you just need to figure out the governance needed to enable [your top outcome]. It’s simple, stay focused, keep a limited scope, be agile, deliver on one outcome. Figure out the governance dependencies for that one outcome instead of figuring out the governance dependencies for everything. Because if you try to figure out the governance dependencies for everything, you’ll be at it every day for the next three years — two to three years easily. That will consume every resource you’ve got, you won’t have delivered value, and it’s not going to be good. It’s not.” — Malcolm HawkerEPISODE LINKS & RESOURCES:Follow Malcolm Hawker on LinkedInChief Data Officers don’t stay in their roles long. Here’s why.Why Do Chief Data Officers Have Such Short Tenures?

Curiosity can lead you to unlikely places. For Malcolm Hawker, a simple curiosity about enterprise data led to a lasting career as an expert in the field and a thought leadership figure in the data and analytics space.Malcolm is interviewed by Anthony Algmin on the Data Leadership Lessons podcast to discuss his personal journey to becoming a thought leader in the data field. In this lively conversation, he shares insights on his career progression starting as a phone-based Technical Support agent for AOL in the mid ‘90s making $7 an hour to where he is today. Along the way, Malcolm shares several valuable insights on the current state of data leadership with practical advice on how CDOs can leverage critical data to become a more data-driven organization. Malcolm dives into why he chose to leave his position as a Gartner Analyst and use podcasts, LinkedIn and other channels to share best practices with CDOs as a part of his mission to “raise the bar” on data leadership. A common thread across Malcolm’s interview is the idea that many of the best practices consistently shared with CDOs and other data leaders are not being implemented today. He attributes much of this as a failure to align business incentives with IT incentives, as well as a failure by CDOs with more of an IT background being unwilling to defer to other experts to help solve difficult problems — at least those who aren’t high-priced consultants. Through this 45-minute conversation, Malcolm’s passion for helping CDOs consistently sits at the core with the support of his deep experience as a consultant, a software vendor, a CIO and a Gartner analyst. He explores the importance of CDOs having the right combination of business, technical and sales skills — and the importance of using the right messaging and mediums to ensure data organizations place business first, not technology. CDOs looking for new — and perhaps a bit irreverent — views on the state of data leadership today should find this episode of CDO Matters a breath of fresh air. Key Moments[1:00] Malcolm’s Career Overview in the Data Space[6:30] The Early Internet Days with AOL[9:20] Pursuing Data Management[14:40] Adding Value as a Data Thought Leader[15:43] The Appeal of Data Evangelism[25:20] Practicing Humility as a Modern CDO[31:50] Making Data Literacy Actionable[36:15] The Buzz Behind Data Fabric and Data Mesh[39:10] Learning from Data MistakesKey TakeawaysWearing Multiple Hats in the Data Space (5:02)“In many ways, I learned the wrong way in some instances. Or the less effective way, I should say, of doing MDM and data management and implementing a governance program. But those lessons really carried forward and through the rest of my career…I’m in a unique position of having seen what works both as a practitioner and an analyst and a consultant and a software vendor. So, I’ve worn every hat. — Malcolm HawkerMalcolm’s Endgame for Data Thought Leadership (10:42)“After 30 years of having been in this space, I feel like I know what works, and I know what doesn’t work. I’ve seen it work, and I have seen it fail. I’ve been on the end of the failures, so I really want to help people avoid that stuff. I want to help CDOs succeed. I want to help VPs of Data and Analytics succeed. I want to extend the tenures of CDOs. I’m in the right place. I feel like it is the right time.” — Malcolm HawkerThe Makings of an Effective CDO (28:25)“It’s a bit of a unicorn skillset. The right CDO is certainly a bit of a unicorn. It’s half sales, half business and half technology — and yes, that’s three halves. So, it’s tough, but there are people out there that do it.” — Malcolm HawkerThe Truth about Data Literacy (32:08)“I was at a presentation…and they were talking about data literacy and I asked him a question that I thought just was a mic drop. I said, ‘If data leaders were more business literate, would we be asking business leaders to be more data literate?’…I tend to have a little bit of a problem with the phrase ‘data literacy’ because I think it’s condescending because the flipside of that is illiteracy.” — Malcolm HawkerAbout Anthony AlgminAnthony is the host and founder of Algmin Data Leadership and the Data Leadership Lessons podcast. He also serves as the Convergence Platform Program Lead at AbbVie, a pharmaceutical manufacturing firm based in Chicago. Anthony is also the author of the book, Data Leadership: Stop Talking About Data and Start Making an Impact!, published with DATAVERSITY. He has made over a hundred speaking appearances to deliver data leadership insights to the masses. EPISODE LINKS & RESOURCES:Follow Anthony Algmin on LinkedInVisit AbbVie’s websitePurchase Anthony’s book, Data Leadership: Stop Talking About Data and Start Making an Impact! Visit the Data Leadership Lessons podcast

Do you have what it takes to be a Chief Data Officer (CDO) in today’s business landscape?On the latest episode of the CDO Matters Podcast, Profisee Head of Data Strategy and former Gartner Analyst Malcolm interviews Justin Magruder, the CDO of SAIC Corporation. The conversation begins with a focus on his role as CDO as well as some of the major changes Justin has implemented within his organization to help SAIC become more data-driven.This includes creating a data office center of excellence (COE) outside the IT organization that embraces a producer/consumer operating model — where it’s the job of the data office to provide data products that meet the needs of its consumers.The data products — and core capabilities — are managed by a team of product managers within SAIC’s data office. Data serves as a primary focus where economies that efficiently share data are a top priority. The discussion then pivots into Justin’s journey to becoming the CDO of one of the biggest technology services companies in the world. According to Justin, “For those who want to become a CDO, who aren’t already in technology, there is hope.” In fact, Justin did not come from a background in technology, but rather as a Catering Service Manager at a large hotel in New York. Through a long series of choices always leading him back to data, Justin outlines a career focused on enabling companies to become more data-. Data and analytics leaders who want to become a CDO — especially those without technology backgrounds — should be inspired by listening to this episode as Justin shares some of his keys to success. Justin stresses the importance of “communicating and collaborating” to anyone looking to transition into a CDO role. Key Moments[1:25] The Role of the CDO in Modern Organizations[5:37] Developing a Data Strategy[9:42] Data Commonalities Across Industries[12:03] Finding Purpose in Your Data[14:46] Evangelizing as a CDO[18:58] Treating Data Management as Product Management[22:00] The Significance of Data Sharing[25:02] Starting a CDO Career[30:38] Important Characteristics of a Modern CDO[35:20] The Importance of Communication for CDOsKey Takeaways SAIC’s Approach to Managing Data (4:15)“We help businesspeople get the data they need to do their job — it’s as simple as that. What makes us successful at SAIC is that we’ve been able to govern the intake of data requirements from across the business using very common ‘voice of business’ type approaches, very agile methodologies.” — Justin MagruderImplementing a Successful Data Strategy (13:11)“What [a data catalog] is really about, we have learned, is automation — and how we build a framework where we can have this integrated environment and we can really find out the authoritative source for any given data point at any point in time…so it’s really been rewarding to see how the introduction of data strategy to a technology firm is beginning to have a positive impact on how we do business with our customers and the value we provide to them.” — Justin MagruderThe Importance of Data Sharing (24:30)“We realized we can really supercharge some of the work we are doing for our customers if we can do this kind of integration and sharing of information. It turns out we don’t need three copies of the same thing. If we can learn how to share one, it would save us all time and money.” — Justin MagruderBecoming a CDO (25:50)“For those people who aren’t already in technology, there is hope [for becoming a CDO]. I started my career after college with a big Fortune 500 company in a management training program, the company was Marriott. I went through Marriott’s management training program and became a Catering Service Manager. How did I get here?...I wound up in New York City for a big hotel...and I began to question how we put together profit/loss statements for my department. I worked with the hotel’s Food and Beverage Director to look at the way we were calculating gross margin and things like that...he said I should go to business school and go into banking. I came out of business school and fell into a technical role.” — Justin MagruderAbout Justin MagruderJustin is the CDO of SAIC Corporation, a $7.4B Fortune 500 technology integrator at the forefront of enabling the digital transformation of some of the largest government entities in the U.S. He is a pioneer and a thought leader in the field of data governance, master and reference data and data operations, with more than 25 years supporting data operations, leaders and decision-makers to improve business performance through better data management. EPISODE LINKS & RESOURCES:Follow Justin Magruder on LinkedInVisit SAIC’s website

Our understanding of data and its role within a business isn’t the same as it was 20 years ago. As data leaders embark on a modern data strategy, it is important continually adapt to change while operating with agility. Malcolm interviews Samir Sharma, the CEO and Founder of datazuum, a data strategy and analytics consulting firm. Samir and Malcolm discuss many of the more significant challenges facing Chief Data Officers (CDOs) as they pursue their strategic priorities, with Samir providing a trove of useful and highly pragmatic guidance for any CDO looking to meet both short-term and longer-term goals. Early in the discussion, they dive into the unhealthy fascination data leaders have on implementing technology without first understanding the business problems it will solve. Malcolm and Samir also share CDOs need to clearly understand their organizations’ existing operating model and their level of data and analytics maturity, as both are critical in defining a data strategy roadmap. Samir shares his framework for working with CDOs whose stated strategic goals require greater maturity but are often starting at ground zero — which involves taking an agile and pragmatic approach to testing the organization’s ability to provide short-term business benefits around certain data use cases within specific engagement models. The conversation then shifts to the importance of the culture of an organization and how that impacts the ability of a CDO to deliver a data strategy. However, unlike more conventional approaches to delivering a data strategy that puts culture change as a dependency for CDO success, Samir makes a compelling argument to work best within the existing culture and find ways to deliver value and speak in a common language without requiring drastic cultural shifts. He outlines how developing a common language and a clear understanding of expected outcomes is key for allowing CDOs to work within the cultural constraints of a given business. Data literacy programs come under the crosshairs of both Samir and Malcolm, where it’s posited that data literacy programs may often be a symptom of larger organizational dysfunctions. Rather than seeing data literacy as the solution, Samir makes a case for a broader focus on having CDOs and their teams more focused on developing knowledge of business processes and where those processes are architected — all with a deep understanding of the importance of data from the ground-up. CDOs focused on defining or executing their data strategies will find this episode of CDO Matters particularly useful, especially those frustrated with the speed or effectiveness of their efforts. This episode makes a compelling case for a more agile, iterative and pragmatic approach to a data strategy that removes major dependencies — such as culture change or a focus on literacy — to one focused on adapting to any existing culture and operating model. Key Moments [2:06] Top Takeaways from Big Data London Conference & Exhibition [7:28] Focusing on Business Outcomes and Building Data Technologies [11:35] Creating a Data Strategy [14:32] Data Ownership and Domain Retention [19:10] Business Maturity and Understanding Data Products [21:10] Assessing Your Data Strategy [28:44] Remaining Pragmatic and Operating with Agility [31:31] Maintaining a Business Culture and Preserving Data Values [36:11] Knowing Your Business Process [38:06] Deconstructing Data Literacy [41:10] Breaking Down Your Business Data Model [42:52] Making Changes to Drive Value Key Takeaways Building Data Technologies (10:07) “I think when I look at it, technology is easier than having to put together the notion of scratching your heads and wondering what you are going to do…I think this is just another iteration of the whole marketing movement for tooling…we had all of the various different terms that we were attempting to implement…all of those areas that which we’re trying to improve how we work, but why have we got all this stuff? Because the systems can’t integrate…we need to start with a data-focused view.” — Samir Sharma Is Data Ownership Relevant? (14:31) “I think it’s okay to talk about ownership from the perspective of an individual application. But when you start talking about domains that are used everywhere, it’s a horrible label…To me, the notion of ownership…not all data is created equally. I think the notion of ownership is misguided.” — Malcolm Hawker Launching Your Data Strategy (21:48) “There’s got to be a certain amount of standardization…a certain amount of proof of value that you can start to show stakeholders who are going to invest in this thing long-term. I think that’s one thing that many people forget. Before you go out and start thinking about centralization versus decentralization or a factory model or whatever you might want to have, you got to think about use cases…we want to prove value and we want to show how we can do it and want to show that early benefit to stakeholders.” — Samir Sharma Promoting an Adaptable Data Culture (35:06) “My view around culture is that there is one. We don’t need to disrupt it. What we need to do is get better at engaging with each other. We need to set a foundation of ways of working that will use business language and be able to talk to somebody about the outcomes that they are looking for.” — Samir Sharma About Samir Sharma Samir is the CEO and Founder of datazuum, a data strategy, and analytics consulting firm. Advising businesses on how to prioritize data activities, identifying growth possibilities and using data to boost revenue and profit. His clients span the UK, Europe and North America while ranging from medium-sized firms to major multi-national corporations. Prior to datazuum, Samir worked at Computer Sciences Corporation, Accenture, Christie’s and Vertex Business Services where he led the development of their data and analytics business. He writes on all things related to data strategy, roadmap development and how to execute the data strategy where he shares his experiences and lessons learned. Samir is a frequent keynote speaker and hosts the Data Strategy Show podcast, which was named one of the Top 10 Podcasts of 2022, as well as leading Ask Me Anything events with top data executives. EPISODE LINKS & RESOURCES: Follow Samir on LinkedIn Listen to the Data Strategy Show podcast Visit datazuum’s website

The world changed in March 2020. Public events and social gatherings took a backseat. As we recover from the pandemic over two years later, are we ready to attend large in-person conferences?In this special 10th episode of CDO Matters, Malcolm becomes his own guest and takes a look into the future of in-person conferences through the lens of his experiences at four industry events this summer including an in-depth review of a celebrated annual conference, Microsoft Ignite. Malcolm details the pros and cons of the conference. The highly anticipated event hosted 3,500 in-person attendees in Seattle, but over 200,000 online attendees around the globe. Most notably, the in-person attendees – many of whom came from overseas – paid thousands of dollars to attend while online attendees received free admission. Malcolm discusses the differences between the two experiences such as peer networking, vendor interactions, and expert advice. Was the in-person experience worth it? He also discusses the incredible diversity of conference content – ranging from the desktop to the cloud and everything in between – being an additional challenge for CDOs seeking in-person networking and peer interactions. Unexpectedly, any data-related content that was available focused on data infrastructure, but not on people or processes supporting data management. The irony of a conference with little CDO-centric content or networking opportunities, but with a primary theme/track of “becoming data-driven” is highlighted in this episode. Here, Malcolm is forced to question the future of these events and whether in-person conferences remain relevant in 2022. Despite a suboptimal experience in Seattle, Malcolm shares his firm belief that in-person conferences are still alive, and that creating a highly effective ‘hybrid’ event, based on experiences at other conferences he attended this summer – most notably the CDOIQ conference – is doable. There is a pent-up demand for conferences coming out of COVID-related lockdowns driving attendance, but more importantly at the right conference, there are still significant benefits CDOs can gain from in-person exchanges of insights and experiences that online events cannot provide.Key Moments [2:07] The Role of the CDO and Why We Started the Podcast[6:07] Audience Attendance [8:52] Networking Challenges[10:17] Presentation Style and Sensory Output[12:37] Are In-Person Conferences Dead?[14:02] The Future of Hybrid Events[16:07] On-Site vs. Online Event Experience[18:22] Discussions and Interaction[20:07] Presentation Hubs and Breakout Sessions[25:07] Conference Layout[28:22] The Good and the Bad (What Worked and What Didn’t)[34:27] Closing StatementsKey Takeaways Why the Podcast Started (1:33)“We started this podcast because…what I saw in the market were really things that just weren’t working for CDOs…a lot of the same messages that we had been hearing for years. What I see less of is moving the needle: results. We know that CDO tenures are very short. Anywhere from two to two and a half years…when I look to the community of people, like myself, that are providing insights, providing best practices, providing the tips and tricks on how to be a better CDO, I saw very little derivation…yet businesses changed.” — Malcolm HawkerAre In-Person Conferences Dead? (13:07)“Are on-site, in-person events dead? Most certainly not. I went to four this year. I went to Ignite, the DGIQ conference in San Diego, the CDOIQ conference in Boston and I went to the Gartner Data and Analytics conference in September in Orlando and they were all full…there’s most certainly a demand for on-site events. Now whether this is a post-COVID phenomenon? I really don’t think so.” — Malcolm HawkerIn-Person vs. Online (16:07)“Some people do actually enjoy interacting with vendors and being able to talk to others about their solutions. But if you don’t get the networking, if you don’t get to interact with vendors, if you don’t get the peer, one-to-one opportunities, then why would you attend in person? It would be really tough to justify spending thousands of dollars if you can do the exact same thing online”. — Malcolm HawkerEPISODE LINKS & RESOURCES:Follow Malcolm on LinkedInVisit the Microsoft Ignite website

The data space isn’t what it was 20 years ago. As enterprises change the way they conduct business, we should also change our traditional approaches to data governance.In his latest discussion, Profisee Head of Data Strategy Malcolm Hawker talks with Moxy Analytics CEO Laura Madsen to dive deeply into the topic of data governance and discuss Laura’s love/hate relationship with a field that can often defies logic in today’s modern data estates. Laura challenges many of the more traditional approaches to governance that are clearly not working for many companies — most of which have not changed fundamentally in decades. Laura makes a compelling case to “blow it all up” and start completely from scratch with approaches to data governance that are more scalable and adaptable to modern business needs.Throughout the discussion, Laura takes aim at other data management gold standards, including what she sees as the absurdity of aspiring to a single definition for anything today. Laura advances the idea that data quality is not absolute and that striving for data quality standards that aren’t defined or measured is a fool’s errand. The discussion of data quality not existing without data governance — and vice versa — is an insightful exploration into the critical need to measure and define data quality metrics and standards. Laura highlights the paradox that having one without the other makes it impossible to know if you succeed at either quality or governance. Laura highlights other data-related technologies as critical components to enable scale while also acknowledging they cannot “magically solve all of our problems.” The discussion concludes with a focus on the radical democratization of data, a concept that Laura believes is critical to breaking through old, unproductive patterns of data management. In this transition toward data democratization, Chief Data Officers (CDOs) will know they are on the right path when an environment exists for users to question the data, where those questions form the foundation for ongoing data improvements. This episode of CDO Matters should appeal to any CDO who feels their data governance program is ill-suited to support their ever-evolving business needs. It should inspire CDOs to revisit their assumptions about data governance — and potentially motivate many to consider some radical changes to governance “business as usual.” Forget what you think you know about single sources of truth, data quality metrics or top-down approaches to governance — Laura Madsen challenges all of these concepts (and more) in this provocative episode of CDO Matters. Key Moments 3:18 The Current State of Data Governance5:23 “I Hate Data Governance”7:16 Data Governance is Broken8:45 Illogical Approaches to Data Governance10:13 Data Stewardship for A Different Era11:38 Do We Need Centralized “Command & Control”14:28 Don’t Rely on a Single Definition of Your Data17:59 Data Quality is Not Absolute20:23 It’s Difficult to Prove Governance is Working without Data Quality and a Metric22:33 Leveraging Data Governance Technologies25:50 The Radical Democratization of Data28:11 Data Governance Starts with People29:38 Data Governance is Change Management30:28 The Gender Gap in TechnologyKey Takeaways The Way We Think about Data Governance Doesn’t Make Sense (4:40)“When I see something that is wrong or lacking logic — and I do think that a lot of the ways we think about data governance now lack logic in a modern data environment — I just tend to want to blow those things up. There’s a fair part of me that still struggles with data governance as a result…we’re still doing a lot of the [traditional approaches].” — Laura MadsenIllogical Approaches to Data Governance (7:54)“I never intended to write a book about data governance. That was never on my radar at all…I was building a modern data platform and I didn’t really care about the landscape. Fast forward, I leave that job…the thing that kept coming back to me was that data governance was the Achilles heel of most programs and our ability to deliver results.” — Laura MadsenData Governance for a Different Era (10:23)“In a space where in the late 90s, most of our data warehouses were maybe a handful of tables…nothing in terms of the construct of data governance changed from the late ‘90s to when I Googled that definition in 2019. Two decades…why are we still doing the same things with data governance in the data space?” — Laura MadsenThe Importance of Context in Data Governance (15:40)“In what reality do we want everyone to be operating on one definition of something?...I want you to look critically at what you are executing and what is not working…Let [data leaders] have different definitions…When it matters is when you want to have a better sense of management around [definitions].” — Laura MadsenFocus More on Process, People and Culture than Technology (22:53)“We [initially] had no tools in this space at all…Data catalogs are changing the game. They help you focus on usage to define your use case…more eyeballs on the data means better data…tools can help us with that, but they cannot solve all of our problems. These are all problems that can be solved with some thinking about process and people and the culture of an organization and way less focus on the technology.” — Laura MadsenClosing the Gender Gap in Technology (32:53)“If we’re not willing to face these things and have discussions about them, then we are never going to improve them…We have made improvements and I sometimes think we need to acknowledge at least that…but, we still have a long way to go…it’s intentionality. It’s making yourself uncomfortable, realizing you have some culpability there, and moving forward.” — Laura MadsenAbout Laura MadsenLaura Madsen is the CEO of Moxy Analytics and the author of three books on the topics of BI/Analytics, Data Strategy and Data Governance. With over 20 years in the field, she is a leader in the data and analytics industry and has supported the definition and implementation of data strategies and analytics/governance programs at multiple organizations across the country. She's a selfless champion for diversity, inclusion and gender equity matters through organizations like Sistech. Laura is also a Halestorm fan, myth-buster, BS caller and has perfected the art of cynicism.EPISODE LINKS & RESOURCES:• Follow Laura Madsen on LinkedIn• Purchase Laura’s book, Disrupting Data Governance: A Call to Action• Learn more about Sistech, the Sisterhood of Technology Professionals• Visit Moxy Analytics’ website