CDO to CDO Podcast: Wendy Batchelder

March 16, 2021 Chris Knerr

The CDO to CDO podcast is hosted by Chris Knerr, Chief Digital Officer of Syniti.




Subscribe Here:  Listen to all the episodes in the Syniti Podcast Series




Chris Knerr (00:22):

I'm Chris Knerr, chief digital officer Syniti, a world leader on enterprise data software, and we're partnering with CDO magazine, MIT CDOIQ, and the International Society of Chief Data Officers, to bring you this ongoing series of interviews with thought leaders in data and analytics. Today, I have the terrific pleasure of talking with Wendy Batchelder, Chief Data Officer of VMware. Welcome Wendy. Great to meet you.


Wendy Batchelder (00:48):

Hey, Chris, thanks for having me.


Chris Knerr (00:52):

Absolutely. So, let's jump right into it if we can. You were, after an interesting and distinguished career in various data fields, appointed Chief Data Officer in Q4 of last year. That's awesome. Congratulations. Tell us a bit, what, what are you most excited about in this role and what's your vision for chief data officer and for your organization?


Wendy Batchelder (01:13):

A great question, Chris. It's tremendous to be the first Chief Data Officer at VMware. I'm really excited that the company has recognized the significance of Data and Analytics. And is acknowledging that by appointing a Chief Data Officer, I think that in and of itself is something to be excited about. My vision for the company is to really become more data-driven and being able to put forward consistent data metrics, reporting, and data products that will enable and empower the company to future success. So, really moving away from being more of an order taking organization into more of an offensive organization is a transformation that I am just really excited to be at the forefront of and to be able to just help drive as a part of my role.


Chris Knerr (02:05):

Yeah. That's terrific. And I didn't actually realize that you're the first, so that's even better. And I love it when I see that kind of acknowledgement on the part of boards and executive management, that this is a really important area to focus on. Maybe in terms of your vision, I could just drill into that a little bit. When you talk about kind of moving away from the IT order taking function, into driving the business more, is your focus more on top line growth for VMware, helping your customers with top line growth? Or is it more oriented around internal efficiency, resource allocation, working capital internal P&L. And I think this is kind of an interesting area of focus, I'll call it versus cost efficiency, and internal versus external focus.


Wendy Batchelder (03:03):

Yeah, Chris, it's a combination of both. So, I have within my organization, I've responsibility for data governance, quality, metadata, analytics and insights, data science, and also RPA. And so, because of that, I think, unique blend, we have the benefit of really looking at things from a services perspective, how do we provide automation and just advanced capabilities for the organization to drive efficiencies and cost savings and just helping the company to be more efficient and effective. But on the other side, just serving up those next opportunity by propensity modeling, just there's so many opportunities for us to really help our customers, to have the best product in their hands with their needs. So, we're really looking at both sides of that coin and how do we drive both top line growth, but also operational efficiency within the organization across all business units. So, we're in, I think, a unique position to be able to see that end to end, which is, I think some of the power of having a enterprise data function.


Chris Knerr (04:10):

Yeah, absolutely. So, that's great to hear and I completely agree, and I go to that question right away because I feel that all too often, there's been an emerging awareness of the importance of data, enterprise data management, but then it devolves into how can I scrape a few more pennies. And I'm not demeaning that, the cost efficiency is very important. But I think that, especially in my view as we're emerging into, hopefully this year, a post pandemic world, refocusing on growth is going to be critically important. So, it's great to hear that that's part of your vision and part of your brief. So, Wendy, along those lines, there's been a lot of emphasis in 2020, and I think there will be going forward on business outcomes and value delivery. And the creation of a vision, measuring that with outcomes and then measuring that with metrics. So, maybe just double clicking on this growth story a little bit, are there one or two business or value outcome programs and/or metrics that you could share that are part of your emerging growth story as a Chief Data Officer?


Wendy Batchelder (05:26):

Yeah, absolutely. So, part of my stepping into this role is really looking at things from like an OKR perspective. What are the objectives that we're really aiming to drive as an organization? How do we help influence and drive those business outcomes, like you mentioned, and then how do we measure that? And I think for a lot of data professionals, especially those who came from more of an IT perspective, that's harder to do. It's I think a unique skillset for a lot of deep data professionals, to be able to translate that into true business impact. And that's where I see a lot of people almost struggle, to be able to translate their value in terms of that executive staff member, or help them understand the real practicality of delivering on these data solutions that are near and dear to all of our hearts.


And so, we have really spent a lot of time in the last really, three months in translating what are the company's objectives? How do those cascade into our objectives? How do we support those? And how do we measure that? So, one area is around, on customer renewals, how do we speed that process up? How do we make things more efficient? And a lot of that comes down to stitching master data together with purchase history information, and being able to make that more automated. So, just looking at those opportunities, one of the metrics we're looking at is that timeline. How do we make that speed of transaction faster? How do we improve that in terms of days? And then from there, how do you shave off hours or even minutes in that transaction life cycle, to make the customer experience that much better? So, we're able to see and look at customer experience and how that translate into things that we need to do to help empower the organization to move faster, better, with higher quality data and just better operational efficiency.


Chris Knerr (07:12):

I love that example. And I share your experience with having, in a different way, because we're an enterprise data company as well. There's a lot of change management and a lot of education to help data professionals. And I think as leaders in the field, that's very important to focus on, not just that we're moving the bits and bytes or moving the master data, but that it actually translates into something tangible. And I think from a growth perspective, I love that that example of just making sure one, that your customers have the right thing for their needs. And then also, once you figure that out, not getting lost in the mechanics of getting that renewal done, which, obviously in large companies, that there tends to be additions to cycle time, non-value added stuff, bureaucracy, and then just getting visibility to that.


And what's nice is it sounds like there's a great alignment with your commercial organization, that that's something to focus on, and that thread of alignment through all of that. So, I think that's a terrific example. Maybe to shift gears a little bit, there's a point, career-wise that I think you might have a unique perspective on, which is that you came from the financial services industry, and now you're in the tech industry. And one of the things I feel like has been ... this is another maybe change management question, I think confusing for folks in what say, traditional industries like financial services or manufacturing, is you look at these tech companies and it seems like they sort of launch stuff into orbit really quickly, and that old-school industries, old-school companies are, and I don't mean that in a pejorative way, just that they've been around and it's a different business model or struggling to catch up.


Are there a couple of leadership observations that, now having done both, you could share with your former colleagues in financial services or other traditional line industries, what you've learned from being an executive leader in the tech sector, so that when everyone's sort of admiring that, is what they're seeing real? Is there a different way of thinking about it? What are some of the nuances or learnings there?


Wendy Batchelder (09:30):

Yeah, and I think that's a wonderful question because there are some, very, I think stark differences between industries that are, I would say more highly regulated, in that there is a lot of boxes to check, compliance exercises to complete. In the tech sector, it's not as rigorous. Now, there still are definitely requirements for compliance and privacy and whatnot, but there's not as much formality in some of the mandates. You're not dealing with the OCC or the FRB or some of these other regulators that are so important to the financial services industry. So, one piece of advice I would have for our financial services friends, if you will, is really thinking about the power of why? Why are we really working on these data priorities? Is it to check a box or is there a business value proposition, I think that at least in my experience, sometimes gets pushed to the wayside.


And so, just really thinking about why are we doing these different programs, how is that benefiting the user, the customer, and just always having that customer driven lens. That sometimes gets lost in the regulatory mandates or the drivers. We certainly saw that in the data space with BCBS-239 and some of the other regulations that came up out of the financial crisis, as being the driver to data maturation, and really thinking about it in terms of what does the user need and really thinking about why we're doing the things that we are, and not getting lost in the bureaucracy or that extra governance that comes with just being a very highly regulated industry. I think in the tech sector, you see more of that user experience, customer experience being the main driver. And so, just having that that shift in paradigm of really thinking about why we're doing these things, what is the real impact, not necessarily being the check a regulatory box, but that it makes life better.


And I think that's really important to keep in mind. And I that's the distinction I saw coming into the tech sector, is you really get to think about the value and not as much of the compliance aspect. And I think that's really, really exciting, as part of the driver for me to move into tech.


Chris Knerr (11:45):

I think that's a great insight and I love that way of thinking about it. And before I was in technology, I was in life sciences for a long time. So, I had a similar experience with the heavy and appropriately so, regulatory framework, but it just, too often I agree, kind of becomes an end in itself, and you end up with the tail wagging the dog. And I like that lens of what's the customer experience or what's the business experience? The other thing that a journey that we went through about 10 years ago, was thinking about compliance risk tiering, at what things are really important and absolutely need to be bulletproof as opposed to what things got added on. And I think there was an interesting thought process where if you were to take your suggestion, dimensionalize that with what's the compliance requirement, what's the risk profile, and then how does that attach to some customer value or customer experience?


That's an interesting way of thinking about it. Let me go in a slightly different direction if I can. And I'll share with you first, a mental model that I've had a long time on the concept of architecture. And I think VMware has got a very interesting place in what I would consider enterprise architecture. So, when I think of architecture, I think of there's a physical architecture. So, you have buildings. There's a social and people architecture, you have relationships. There's a legal entity, a tax regulatory architecture. There's obviously a business process architecture. There's an application architecture, which maybe is what most IT folks spend most of their time thinking about when they hear the word architecture. Underlying that, there's an infrastructural or what used to be called the hosting architecture. And then there's a data architecture.


In thinking about this for probably 10, 15 years, I feel like the data architecture and data interoperability often get short shrift and not thought of, in looking at that overall architectural situation. So, where I think you may have an interesting dog in the fight here is that part of your core value proposition is around app rationalization, and the digitization of underlying technical infrastructure. Within that new set of capabilities that you bring to your customers, what, what's your perspective on how companies get data right, or how companies get data wrong?


Wendy Batchelder (14:25):

Yeah, I think a lot of times, we have this mindset that we have to physically move data together to get the value out of it. And I see that a lot in the data practice, if you will, in that we think we need to take copies of data or actually physically move it to a centralized area in order to be able to get what we need from it. And I think as we're seeing more things done on the edge, more IOT type technologies emerging, a lot more data than we've ever seen before, that becomes a bit impractical. And I think it drives the importance of how do we gleam that value and really understand our data in a way without having to move it? And so, what I think we're going to see a lot more in the industry, both with our customers and internally, certainly with our own business is, how do you govern and gleam that value without moving the data?


And I think that's going to increase the value proposition of metadata, and really understanding what data you have where, and really driving that, almost that indexing capability of good, clear data definitions, understanding where all your customer information is and resides, how we protect it on the edge. So, I think security will go through some sort of a continued maturation as far as how we protect data where it's captured, even more so than in the past. And so, I do think we'll see this more disparate data architecture emerging, and an increase in focus in how do we really get our arms around all of the data that exists across our organizations, and being able to manage it there where it lives instead of having to bring it together to build whatever analytics or insights we need to run our companies.


Chris Knerr (16:07):

So, I think that's a really interesting insight. And I've thought about this and let me just replay some thoughts. And I think it's aligned with what you're suggesting. So, for a long time in, let's go way back to the traditional EDW world, right?


Wendy Batchelder (16:07):



Chris Knerr (16:26):

So, there was kind of this idea of one-stop shopping. And then the metaphor is like, well, we're going to build a department store and it's going to have everything, right? You go in the door, you can buy everything from housewares to clothing, to food, metaphorically. Where we're really ending up with is almost more of like a shopping mall, and then set of satellite stores around it. So, I agree with you that there's been this story for a long time that app rationalization and cloud and virtualization is going to make everything simpler.


And I think to some extent, the opposite's been the case, right? That if you're a CIO or a CDO, actually it's getting more and more complicated. So, sometimes what we would call simplistically, more data in more places, but the need to adjudicate and govern and distill the value out of that, the expectations on that are actually rising. So, you almost have a pull from opposite directions. So, maybe in short hand, your point of view would be that the capabilities around metadata, around lineage and in a more technical level, around a core edge and agent-based architecture are going to be very significant, are significant now and will increase insignificant over the the medium and probably the long term, is that a fair summary?


Wendy Batchelder (17:52):

It is. I mean, really what I'm, in the terms of your analogy, it's the importance of that information kiosk, right?


Chris Knerr (17:59):

Yeah, definitely.


Wendy Batchelder (17:59):

If you could go to the mall and you need the information center to tell you where the stores are, that's essentially what our metadata capabilities and the importance of that are. You're not just going to naturally know where all those stores are, right? You have to go to some sort of place to get the map of where it is, and find out what types of things are available. And that's really what a really solid data catalog can help you with, that really strong data lineage information. And then being able to measure quality of that data on the edge, like being able to bring all that together, in a way that allows a user to find what they need without having to move all the data, I think that is going to be a tremendous value and also potentially some cost saving, right? There is a cost in bringing everything together into a central store, to running that department store. That's expensive.


Chris Knerr (17:59):



Wendy Batchelder (18:46):

So, if you can leave data where it is, and simply build a really good mapping system to understand it, I think that's going to also reduce some of the heavy burden of moving data around.


Chris Knerr (18:58):

Yeah, I'm I'm with you, I mean and so there's ... And of course, to some extent you can bulletproof this technically, but in reality, every time you move something, you're introducing a new failure point, right?


Wendy Batchelder (19:10):

Yeah, [crosstalk 00:19:10].


Chris Knerr (19:10):

So, integrations, replication, they're always a pain. I mean, I think the other thing back to where we started around the business value, just again simplistically, but it's an important point as I'm trying to do machine learning, AI, I want more raw materials for that engine. So, the more data sources that I can bring in, but you can't just bring anything in and have no governance and no adjudication or the outputs of your models turn out to be a mess and turn out to be useless. So, that underlying concept of more distributed, agent-based, but then with that framework of data quality and governance around it, I think is a really, really key trend to look at. I love that concept.


So, maybe I think there's one more area that I'd love to get your insight on, which is around talent and leadership. And in talking with all kinds of executives, but CIOs and CDOs in particular, we hear a lot about the talent crisis. So, do you feel ... and I'll just sort of throw a few dimensions I normally look at, and then I'd love to hear your perspective, do we have a talent pipeline crisis? Are we on a collision course where there's all this demand and then not enough people to meet it? And then secondly, and perhaps in a related way, what's your view on how we're doing from an overall diversity and a gender diversity standpoint? Do you feel like we've gotten better as an industry in the last five years, or are we treading water? What's your perspective on all of that?


Wendy Batchelder (20:52):

I'll answer your last question first and that I think we are in many ways, treading water. I feel like we made a really good leap in the data industry, and in the technology industry, from 10 years ago. I think we did a good job really pushing there. And I see a little bit of a tapering and it's just my perspective. Others may have a different opinion, but I wouldn't call it a crisis, but I feel like there's one maybe on the horizon. I do think it's our job as data leaders, as technology leaders to be qualifying the workforce. So, that's an area that I'm very passionate about, in making sure that we have really given everyone, every student the opportunity to be exposed to computer science, to STEM, to different types of careers, kind of serve on the Iowa Governor's STEM Advisory Council.


And co-chair the career exploration working group, which is aimed at solving that gap here in the State of Iowa. And so, there is, I think a significant burden for us to bear, to make sure that we're not just looking for these people, but we're actually helping them to become educated and qualified to really power the future. There's not going to be less data to manage in the future. We know that that is only-


Chris Knerr (22:09):

If we go back ... yeah.


Wendy Batchelder (22:09):

Exponentially increasing, right?


Chris Knerr (22:09):

Yeah, yeah.


Wendy Batchelder (22:11):

So, we need to make sure that we're investing now in qualifying individuals, especially those, if we feel like we're not getting the diverse talent that we need, to have the proper innovation we know, from I think, many studies that have best innovation, you need diverse experience. You need diverse backgrounds at the table. And if we're not finding that in our talent pools, we need to do something about it. We can't just wait for that to magically happen.


Chris Knerr (22:36):

Thank you for sharing that. And for the record, I agree. I almost always ask this question and I've yet to find any leader who's like, "Oh, we're doing awesome. Don't worry about it." So, I would tend to agree that we've got, maybe it's not an immediately looming crisis, but then we've still got structural issues that we need to deal with. Just, I had a thought, kind of connecting this back to the change management that we talked about, where you have a lot of data professionals, who, in a sense, I believe suffer from over specialization. And really have trouble connecting dots on the business side. I wonder, I've had some success over my career in importing talent from non-traditional sources. So, I mean, people who work even in like PR, in the technical field or people who are, in ERP speak, what would be called functional people, and turning them or we're coaching them into technical people over time and training people on some of those management consulting capabilities.


I think that's potentially, an interesting area to focus on. The other related question I was going to ask you, and this sort of goes to talent sourcing. If you look at this problem overall, do you have a perspective on buy versus build? In the context of technology outsourcing, right? So, I think people don't talk about it that much, but in reality, every tech company in the world has got subcontractors and sub-sub contractors and specialists working in different things. I wonder, and I'm just thinking out loud on this, does that create an opportunity for some fertile ground for recruiting or any other thoughts you have on that in the talent space, like buy versus make versus outsource?


Wendy Batchelder (24:48):

Well, I think we have to be creative in how we get things done in this space, because there's not a pipeline of talent that is rich and full and to the extent that we would like it to be. So in my opinion, we have to be looking broadly. What are you really looking for in a data professional? Are you looking for someone that's coming out with a PhD in computer science? Is that really necessary or is there something that you can do to train them and give them on-the-job experience and/or do some sort of shadowing or rotation type programs, whether it's from the business or even within our own data teams. Think about the disparate skill sets that you have between like a data strategist or a data quality expert or a computer scientist, right?


And just thinking about, you know, how do you career path people through those different disciplines and give them that broader perspective? I do think we have to be thinking outside of the box, and that do you, do you really need someone with a college degree or is on the job training or some sort of trade school sufficient? I think that when you look outside of maybe the norm, you can get some really different mindsets, different talents, different skillsets, maybe someone who just has a high aptitude to learn is a great person to get into the data space. So, I just think we have to be a little bit more open-minded as far as where we're sourcing things from, as far as talent, tools, technologies, and just applying them in new and interesting ways.


So, back to your question about build versus buy, I mean, it depends. I wish I could give you a more specific answer, but I think we have to really look at what are we trying to accomplish? What are the criteria to get there? And is our old way of thinking really appropriate given the way that companies and needs are evolving.


Chris Knerr (26:30):

I think those are great points and I agree. It's interesting. I just have this observation, I have a, it's perhaps a generalization, but I think it's at least a worthwhile thought experiment. A lot of the folks I've worked with in my career, who have been superstars at one thing or another, are people who are cross-functional. So, I think this idea of, as leaders, working on that change management, but also promoting some non-traditional movements is really important. And then your other insight I love, we sponsor an organization in Philadelphia called tech impact, that essentially takes at risk college-age, young adults and gives them technology training, to leapfrog them. And they may or may not end up with an associate degree. They don't have a bachelor's degree, but it's a great starting point.


And there's some ... the organization has been around for quite a while. And a friend of mine is on the board of it. There's some phenomenal success stories for a young man who is literally working outside the Philadelphia museum of art, part-time at a hot dog cart, and ended up running a technology support organization after six or seven years. So, I think that non-traditional talent sourcing and thinking about ... I love it when we can bring people in from our partners. And I don't know it was a little bit when we send them out to our partners, but if it's the right thing for them personally, I always support it, from what we want to do from an industry development standpoint. Well, Wendy, I normally ask people for a prediction, but I think you already gave me such a good one around data centralization and being more enlightened about that and thinking about that agent-based and distributed architecture, that I'm going to go out on a limb and say, that's a prediction, and we should really all pay attention to that?


Just to quickly summarize. I love the conversation about business outcomes and about growth. And I really love the example of tying that back to something that's commercially tangible from a growth standpoint, like your customer renewal, not just efficiency. I love the conversation about change management and talent and let's pay attention to the pipeline crisis and continue to ... the impending, I'll say, and continue to focus on diversity and inclusion. So, that's sort of my quick summary of a terrific conversation. Any closing thoughts or anything you'd like to add for the audience?


Wendy Batchelder (29:02):

Oh, just one thing, for our fellow data professionals, I would say don't be afraid to roll up your sleeves and try to understand your business. I think that's an area where we sometimes shortchange ourselves and thinking like how the business has that, and we can stay over here in our little data world and not get real clear on what the business outcomes, that are important to our company, are. I think we have to break that mold and really be understanding and being experts in our businesses, and really strengthening that business acumen. So, if there's one thing for our fellow data professionals to gleam away from, just what we should be working on as far as building our own skillsets, I think really becoming experts in our various businesses, is very important and often overlooked.


Chris Knerr (29:48):

I could not agree more and I think that's a perfect landing spot. Thank you so much. I really, really enjoyed our conversation. Terrific to meet you. And thanks so much again for joining me today.


Wendy Batchelder (29:59):

Of course, it's been a pleasure. Thanks, Chris.

About the Author

Chris Knerr

Chris Knerr is Syniti's Chief Digital Officer. As a former Fortune 50 Client Executive Sponsor for large-scale data migrations at Johnson & Johnson, as well as a Syniti alliance partner, Chris serves as a powerhouse whose proven success, background and experience helps accelerate Syniti’s data strategy, analytics organization and offerings.

Follow on Linkedin More Content by Chris Knerr
Previous Article
CEO to CEO Podcast: Andy Zimmerman
CEO to CEO Podcast: Andy Zimmerman

This week's guest on the CEO to CEO Podcast, Andy Zimmerman, is president of frog design, one of the world'...

Next Article
Platform Focus Series: Syniti Knowledge Platform
Platform Focus Series: Syniti Knowledge Platform

Join Kevin Gulley our Head of Product Marketing for our Platform Focus video series as he Interviews Syniti...