Wouldn’t it be great to have a playbook for how to be an effective data leader?

In this episode of CDO Matters, Malcolm has an in-depth conversation with an extremely successful IT and data leader, Renée B. Lahti. With CIO experience spanning multiple industries for some of the best-known brands on the planet — including SC Johnson and Symantec — Renée’s depth of experience shines in this conversation about the current state of the role of CDO, including Renée’s insights on the key factors of CDO success.

Failing fast, redefining value, hiring for diversity and being more agile: These are just some of the many fascinating topics discussed in this episode, which could be considered a playbook for any data leader looking to emulate the success of an accomplished data leader.

• Follow Renée B. Lahti on LinkedIn

• Learn about the DataEthics4All advisory council

Today, enterprise data is not only exponentially growing in volume, but in sheer complexity. And how can today’s data leaders extract value if they are unsure where to even start?

In the latest episode of the CDO Matters Podcast, host Malcolm Hawker sits down with Ontotext Chief Marketing Officer Doug Kimball to discuss the wide-ranging applications of next-generation graph database technology.

Listen to Malcolm’s discussion with Doug to learn more about:

• The advantages of graph database technology in particular use cases

• How graph databases can reveal previously unknown relationships

• The speed advantage of graph technology when querying data

• How AI and ML will continue to augment graph technology and reduce human intervention

Doug Kimball is the Chief Marketing Officer of Ontotext, a developer of knowledge graph software helping global enterprises gain knowledge and insights from their diverse data. Doug is an accomplished marketing and communications executive and team leader focused on leading strategic growth within global and domestic organizations.

• Follow Doug Kimball on LinkedIn

• Learn more about Ontotext on their website

As companies grow, they often hire dedicated product managers to oversee all the planning, development and launching a product. Business leaders recognize that this is a critical need and expect products to be managed holistically from ideation to development — so why don’t more companies take that same approach when solving enterprise-wide problems with data and analytics?

On this episode of the CDO Matters Podcast, Malcolm Hawker sits down with Bethany Lyons to discuss her role as KAWA Analytics’ Chief Product Officer (CPO) before diving into a range of timely data topics including:

• “Self-service’ data governance

• Product management approaches

• Centralized security vs. decentralized data modeling

• Enforcing data governance/security policies

• The definition and uses of data build tools (DBT)

• The longevity of CDO tenures

• Key attributes of a good product manager

…and so much more!

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Visit KAWA Analytics’ website

As we get carried away with modern data trends and new technical developments, it's easy to forget the rich history behind computable data and analytics dating as far back as World War II in the 1940s. Luckily, we have a data historian to remind us!

Host Malcolm Hawker sits down with NY Times Chief Data Scientist (CDS) Chris Wiggins to chat about his new book and dive into what led us to this moment in the data space.

The two discuss:

- The ethics behind using data for social concepts

- Quantifying social data

- Early stages of AI and the creation of data computation

- The history of machine learning

- The internet boom of the 90s

- AI vs. copyright laws

...and so much more!

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Follow Chris Wiggins on LinkedIn

Check 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 fabric
• Malcolm's journey in releasing 20+ episodes of the CDO Matters podcast
• Defining data by its business value

...and more!

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Register 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. 

• [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 Thoughts

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 Hawker

What 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

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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 interests
• Memorable life experiences
• Professional insights
• Outlooks on the data space
• Sources of inspiration
• Leadership qualities
• Current trends and happenings in the world of data and analytics

And so much more!

[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 Challenges

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 Hawker

Malcolm’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 Hawker

Essential 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 Hawker

Samir 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.

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Visit datazuum’s website

Listen 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 appearances

And so much more!

[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 Appearances

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 Hawker

Separating 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 Hawker

Selling 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 Taylor

Scott 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.

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Visit MetaMeta Consulting’s website

There’s a well-known saying: “Teamwork makes the dream work.” Despite there being some truth behind that, the dream can’t become a reality without the right team.

This is especially true when recruiting and staffing skilled professionals for your enterprise’s data organization.

In this episode, Malcolm interviews Kyle Winterbottom, the CEO of Orbition, a niche talent consultancy helping organizations staff data and analytics roles across the globe. Running a company solely focused on helping companies build, grow and optimize their data organizations by recruiting top-tier talent gives Kyle a unique perspective on the market for data-centric roles. As such, this episode of CDO Matters is a great resource for data leaders working to build out a data and analytics function, or perhaps even become a chief data officer (CDO) themselves.  

When discussing the current state of the market specifically for CDO talent, Kyle notes that there’s a massive gap between the number of companies hiring CDO-level roles and the total number of possible applicants — a reverse of the supply and demand in the job market for data engineers.  

So, not only is there a scarcity of CDO roles, but Kyle also notes that many companies actually struggle to define exactly what the CDO role should entail — where many still focus on Python skills and other technical proficiencies typically not required by senior executives. This is leading to a number of suboptimal hires, where the scarcity of roles is often filled by people poorly suited to meet the expectations of a senior business leader.   

Establishing a track record of the delivery of tangible business outcomes is how Kyle recommends data leaders highlight their resumes should they seek these scarce CDO positions since ultimately, it’s those business results that companies expect from their head of data.     

In stark contrast to leadership roles, he notes an abundance of unfilled technical roles in data organizations — particularly data engineers. Kyle notes that if you want to earn the same amount of money that data scientists were pulling in five years ago, now is the time to become a data engineer. This demand for technical skills is also being seen in data governance-related roles — where years of companies focused on “shiny objects” in the data space has led to a degree of talent debt in the areas of data management fundamentals — including governance.   

When discussing what it takes to retain and develop data-related talent, Kyle notes that fundamental shifts have occurred since the global pandemic — where in the past, data practitioners were primarily asking themselves three things:  

• How much will you pay me?

• Will the brand name look good on my resume?

• What kind of tech will I get to play with?  

However, in recent years, these needs have shifted. Increasing amounts of data talent are looking for their work to be valuable, visible and impactful.   

When providing his insights on landing a CDO position, Kyle stresses to “not chase a title,” but instead chase positions with roles and responsibilities that tightly align to the typical responsibilities of a CDO — even if a company may not call it that. Kyle correctly notes, “If you want to sit at the top table, then [the delivery of value] is the job…despite that most organizations still advertise for python skills in a CDO.”     

When discussing the issue of short CDO tenures, Kyles likens the current environment for CDOs to that of professional sports coaches and general managers of highly elite teams — where if you are a new hire, but you aren’t able to show some quick wins in a relatively short period of time, your position as CDO will likely be on the chopping block.    

[1:05] Recruiting and Retaining Qualified Data Talent

[4:32] The Current State of the Data Market

[7:20] Leadership Roles Vs. Technical Roles

[9:35] The Decline of Data Science

[12:00] Increased Demand for Data Engineering

[17:34] Proposing a Data Strategy

[20:41] Data Leadership Trends in 2023

[23:20] Treating Data as a Product

[26:30] The Produce Management Lense

[30:02] Professional Advice for CDOs

[32:50] CDO vs. CDAO

[36:30] CDO Tenure Expectations

[39:00] Incentivizing Data & Analytics Leaders

Recruiting Data Talent vs. Leadership Talent (7:30)

“If there is a senior data engineering role, it might get a handful of applicants, if that, over a period of two to three months. If there is a leadership role [Director, VP, CDO type role], that will get hundreds of applicants in a number of minutes.” — Kyle Winterbottom

Increased Demand for Data Engineering (10:51)

“The marketplace [for data engineering] is in the same place data science was five years ago, where people are getting stupid uplifts in terms of salary, stupid uplifts in terms of title…just because the number of people out there that can do that job is so few and far between.” — Kyle Winterbottom

Changing Interests in the Data Space (14:40)

“I think there has been a dramatic change from even five years ago…most data and analytics practitioners were interested in: How much will you pay me, will the brand name look good on my resume and what kind of tech will I get to play with. These were the top three factors…but their wants, needs and desires have changed.” — Kyle Winterbottom

The Current State of the Data Leadership Market (21:10)

“I think the data leadership market will have its data again — for me, it’s just a matter of when, and if that will be 2023, or maybe just a little bit beyond…and I say that because the instability in that market at the moment is so huge that I think a lot of hiring decisions are being made wrongly.” — Kyle Winterbottom

Kyle is the Founder and CEO of Orbition, a data and analytics talent consultancy located in London and New York responsible for staffing major organizations around the globe. Since 2020, they have served as a dedicated, full-service talent solutions provider for the industry. He is hugely passionate about enabling organizations to drive decisions and obtain value3 from the use of data, analytics and AI. Kyle firmly believes that the two biggest assets that a business has are people and data.

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Visit Orbition’s website

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.

[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 Sharing

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 Khan

The 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 Khan

Adopting 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 Khan

The 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 Hawker

Creating 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 Khan

Saleem 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.

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Visit Discovery Data’s website