CDO Matters Podcast

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

Acquiring expertise in any field is a tool that is sharpened over time, not overnight. This is especially true for the complex world of data and analytics. It takes drive, passion and experience to become a credible data leader.For this episode, Malcolm joins datazuum CEO and Founder, Samir Sharma, on the Data Strategy Show podcast to answer personal and professional questions that provide insight into who Malcolm is as a data thought leader both in and out of the office.Throughout their discussion, Samir asks a series of rapid-fire questions covering several topics including:Personal interestsMemorable life experiencesProfessional insightsOutlooks on the data spaceSources of inspirationLeadership qualitiesCurrent trends and happenings in the world of data and analyticsAnd so much more!Key Moments[1:30] Rapid-Fire Questions with Malcolm[8:30] Three Things Malcolm Can’t Live Without[17:50] Current Inspirations in the Data Space[24:45] Malcolm’s Professional Epiphany [45:14] Advice for Data Leaders[53:20] The Problems with Data Literacy[57:55] Malcolm’s Highlight of the Year[1:01:25] Malcolm’s Personal Leading Style[1:03:05] Common Data ChallengesKey Takeaways Current Data Inspiration with Blockchain (17:50)“I know we’ve been talking about blockchain for years. Gartner put it on the data and analytics time cycle in 2019 as this rocket ship that is going up and then two years later, it fizzles and it’s gone. But I am bullish on blockchain in the service of data management and how business is run. It is coming. It’s slow and business adoption is lagging, but there’s something there. Is it going to solve all problems? Of course not…but there are some problems that are purpose-built for blockchain in the data management realm that, I think, are going to be very interesting, particularly when you fold in the notion of widespread data sharing. Blockchain is very good at creating ecosystems of shared data.” — Malcolm HawkerMalcolm’s Professional Epiphany (24:45)“I can vividly remember…when I was moving up the corporate ladder. I’d been getting slow and gradual raises. Interestingly, I was bound to one company during my process of getting a green card in the U.S.…what I figured out was that I had an epiphany that was professional and personal, which was that I’m in charge. I am in charge. Hard stop. Period. There is nothing being done to me. I cannot blame this person or this person…or this situation for things that are happening to me. Everything that happens to me is a direct result of conscious or unconscious actions.” — Malcolm HawkerEssential Leadership Qualities (1:01:25)“Inspire. I want to inspire people. That’s definitely one. I will say I am a hands-off leader. I hate being micro-managed which means that I am constitutionally required to be hands-off in my leadership style. And let’s say supportive. I think that a lot of that has to do with setting clear expectations. That is the kind of support that I need. To me, as a very hands-off person, as a very self-driven person, as a very self-motivated person, just tell me what my boundaries are. Tell me my budget. Tell me my timeline. Tell me my constraints. Tell me your expectations of me. To me, that is the ultimate form of support…that is how I try to manage as a leader.” — Malcolm HawkerAbout Samir SharmaSamir Sharma 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. He 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 Malcolm Hawker on LinkedInFollow Samir Sharma on LinkedInVisit datazuum’s websiteListen to the Data Strategy Show podcast

What happens when you get two modern data experts in a room to talk shop over a beer? Let’s find out!In celebration of the 20th episode of the podcast, Malcolm sits down again with the guest who kicked things off on Episode 1 of the CDO Matters podcast, Scott Taylor (aka. The Data Whisperer). Malcolm hosted Scott for a casual chat in his Florida home to catch up and talk all things data over drinks. Throughout the episode, the two discuss current trends, issues and observations about the data space.Topics of conversation include:• A retrospective of the CDO Matters podcast• Data storytelling and finding a unique voice within the industry• Lessons and tips for current and aspiring CDOs• The importance of data messaging• Upcoming events and appearancesAnd so much more!Key Moments[3:00] Looking Back at Episode 1 on Data Storytelling[10:00] Bringing a Unique Voice to the Data Conversation[15:30] The Failure in Data Management Consulting Today[23:12] Lessons for CDOs from Scott and Malcolm’s Travel Consulting[28:05] Reformatting Data Message Delivery[31:15] Misconceptions about Organizational ‘Culture Change’[35:50] Separating Data from Analytics: Analytical vs. Operational[41:06] Scott’s Upcoming Data Plans and AppearancesKey Takeaways Delivering a Data Narrative in a Unique Voice (10:00)“I hope I am bringing a unique voice to the [data] space. That was the whole goal [with the podcast]. One of the reasons why I wanted Scott to be guest number one and why I’m thrilled that he’s guest number 20…I wanted this to be a different voice. I’ve been in the data space for a long time. A lot of what I see…is the same old messaging over and over and over…the conclusion that I came to is that the way we’re delivering the message is wrong…what I was seeing from Scott was him delivering the message in a very different way through storytelling.” — Malcolm HawkerSeparating Data and Analytics: Operational vs. Analytical Use Cases (38:20)“Going back to analytical versus operational, I don’t know how you throw those into siloes. It doesn’t make any sense to me. What I see happen is when you give the keys to domains or groups or functions or departments to come up with their own analytics…they’ll create their own rules and their own data definitions and their own data quality rules and dashboards…and maybe that freedom is good, but then you have to operate cross-functionally to move a contract out of sales and into finance or move the product from manufacturing into marketing…and then, what happens?” — Malcolm HawkerSelling Leadership on a Data Solution (31:20)“When you’re going for funding [on a data project] and you’re back at saying that some version of this latest, greatest thing is going to fix all of the problems that I told you we were going to fix…the same as the last time. And so you’ve got, I believe on the business side, a certain amount of cynicism and weariness…And I don’t think it helps that, as data people, we come barging in there talking with selective amnesia…pretending that we never said that our previous approach would solve the problem.” — Scott TaylorAbout Scott TaylorScott Taylor, also known as The Data Whisperer, has helped countless companies by enlightening business executives to the strategic value of master data and proper data management. He focuses on business alignment and the “strategic WHY” rather than system implementation and the “technical HOW.” At MetaMeta Consulting he works with Enterprise Data Leadership teams and Innovative Tech Brands to tell their data story.EPISODE LINKS & RESOURCES:Follow Malcolm Hawker on LinkedInFollow Scott Taylor on LinkedInVisit MetaMeta Consulting’s website

Kyle Winterbottom, CEO of Orbition, discusses the scarcity of CDO roles and the struggle to define the role. The podcast also explores LED sign troubles, the disparity in job applicants for technical and leadership roles, the shift from data science to data engineering, building a data analytics culture, integrating product management into data management, and the short tenure of CDOs.

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