Producer 1: (00:01) Hello and welcome to Data Radicals. In today's episode, we're taking a look back at Seasons One and Two of the podcast. You'll hear from guests like Stan McChrystal, Tricia Wang, and Paul Leonardi as they discuss traits of a successful data leader, adapting your strategies, and the importance of soft skills.
Producer 2: (00:18) This podcast is brought to you by Alation. The act of finding great data shouldn't be as primitive as hunting and gathering. Alation Data Catalog enables people to find, understand, trust, and use data with confidence. Active data governance puts people first so they have access to the data they need with in-workflow guidance on how to use it. Learn more about Alation at alation.com.
Satyen Sangani: (0:48) In the episodes of Data Radicals, a major theme has emerged. In the last two decades, data experts have been elevated to leadership roles. It's exciting evidence of the value of data and the importance of data culture at any organization. Having a seat at the executive table offers both potential and peril. Take it from NewVantage Partners founder, Randy Bean.
Randy Bean: (01:08) What I see is that many chief data officers in part because they're new in the role or they're new with the organization, they tend to overpromise or set expectations for things that are difficult to deliver, and they don't have those tough arguments upfront. Many of them are new to the C-suite, so they don't realize how tough things can be when things don't go well. And also, many of their responsibilities have been carved out of the responsibilities of other executives, be it the CIO or perhaps the chief digital officer. So, those executives are all onboard and fully willing to support things as long as results were achieved. And when they don't see those results and they don't see them in a timely fashion, they lose patience and they feel that maybe they could have done better. So a level of resentment starts to build. So it's a pattern that I see often.
Satyen Sangani: (2:02) So what do successful CDOs have in common? Randy elaborates.
Randy Bean: (02:06) Those CDOs that are most successful quickly establish trust within business with business sponsors. They work with the business sponsors, each of the individual business sponsors to identify what are the one or two or three most important things to them or one, two, or three most important business questions and see if they can solve those questions even if it's with a very small subset of data to begin to develop that relationship, that trust. When they repeat that trust, it starts to establish further deepened credibility and it starts to establish momentum.
Satyen Sangani: (2:39) How do you build on this momentum? As Snowflake data strategist Jennifer Belissent notes, communication is key.
Jennifer Belissent: (02:45) Most of the successful CDOs that I talk to really see that as a big part of their role. And when you ask people, "What are the characteristics, what are the skills required to be a chief data officer to do this role?," communication comes up as part of it. It's business skills, it's technology skills, but it's also communication skills. One of my favorite CDOs, he called himself the “chief diplomatic officer” because a big part of what he felt like he did was diplomacy. It was being the bridge across different stakeholders. It was negotiating to get access to data or prioritizing specific projects.
Satyen Sangani: (3:23) Flexibility is also important. Jennifer summarizes this as the ability to play offense and defense.
Jennifer Belissent: (03:29) About five years ago or so, there was all of this talk about CDOs shifting from being defensive to offensive. That metaphor came from American football. The better metaphor of what a CDO really should be, it wasn't a shift per se. If you look at European football, the same team is on the field, they play defense and offense. And some of the best offensive players — the strikers out there — are actually defenders. So if you look at Sergio Ramos in Spain, he's a defender but he has one of the highest scoring rates in the league. It's not a question of being defensive or offensive. CDOs have a mandate across the data value chain, across that whole life cycle of data. Data governance extends also across that life cycle. It's not just about security or privacy or ensuring data quality, but it's also ensuring that the right people have access to it and that the right people can use it for driving value, delivering value to the organization.
Jennifer Belissent: (04:24) So at Snowflake, we talk about the three pillars of data governance. It's knowing your data, protecting your data, but unlocking your data and ensuring that people can collaborate securely using that data. The CDO really reflects that holistic end-to-end view of data governance today across that whole spectrum. One of the things that we've seen, particularly in the U.S., is that the CDO is increasingly reporting to the CEO and the chief executive in an organization. And that does reflect more of the strategic view of data and of data as a strategic asset, and the CDO really driving that effort within the organization.
Satyen Sangani: (5:01) How else can data leaders cultivate a data culture? According to Fivetran co-founder Taylor Brown, it demands leading by example and being curious about the data.
Taylor Brown: (05:09) I guess I would just go with lead by example. I think especially the leadership team are the ones that have to dig in and request data and actually follow what the data says, not just what their gut is telling them. And I think that's especially hard for folks who are not used to working with data and maybe not used to having more of an empirical background. And so I'd urge them, those types of leaders — maybe sales leaders or otherwise — to bring other folks in who have a background in data and who will push them and trust them to make sure that they're right. I think that's the No. 1 thing that leaders can do to change their culture, and then try to broadcast it to the rest of the company so everyone knows this is how things are done.
Satyen Sangani: (5:44) Your company's unique DNA will also influence your data culture. Ashish Thusoo, formerly at Facebook and most recently at Amazon, knows this well.
Ashish Thusoo: (05:52) Every single company has their own unique DNA, so to speak. And Amazon's DNA is driven by — our business came out from retail. Then we obviously created AWS. Two things which are common to those businesses. First, retail business is known for being run with high efficiency, so very small margins. So you can see that culture dominates, like frugality is one of our core leadership principles apart from many other leadership principles. That is something that we really, really believe in. And that sort of comes into play as far as Amazon is concerned. And I think those things are a little different from Facebook. Facebook back in 2011 was much more of a product-driven company. Amazon, I feel is a lot more of … it's a machine. It's just an amazing, amazing piece of how you construct an organization to keep delivering innovations after innovations, and markets after markets. It's just amazing how this thing has been constructed. Facebook, back to 2011, was very data-driven. Data was actually the center of their business. It still is in many ways.
Ashish Thusoo: (07:00) Amazon is known for being data-driven. Some of our practices — internal practices — have become like case studies around what it means to be a data-driven culture. So that part is true. Both these organizations focused a lot on innovation. Amazon and the cloud has innovated, has really created the market there. Facebook obviously created the market as far as social networks is concerned both from the product side in terms of what innovations were brought out on the product side, as well as creating a business out of it. Both of these organizations think big. There's no incrementality but a lot of “think big” is encouraged us to where you try to even question the current assumptions and current beliefs and see if that's going to change. And that's how the innovation wheel sort of gets spun in these organizations.
Satyen Sangani: (7:48) When the data tells you that old strategies are no longer working, how do you respond? It takes humility to pivot and try a new tack. Take it from former General Stan McChrystal, who took over U.S. forces in Afghanistan.
Stan McChrystal: (08:01) We'd like to say we changed because a very brilliant leader comes along and visualizes the future. And I'd like to claim that that was the case and I did that, but that's just not true. The reality was, I took over an organization that was the best of its kind in the history of the world. And I know that's a fairly audacious statement, but the counter-terrorist forces that the U.S. created in the wake of the failure of the Iran rescue mission in 1980 — and it took about 22 years to get there — but by the time I took command of it in 2003, we were the best counter-terrorist force ever created. And yet starting in the fall of 2003 what we found was this extraordinarily elite organization, purpose-built to defeat terrorist networks, suddenly ran into a situation where we weren't just failing, we were losing. The skills we'd hone, the processes we perfected, suddenly were not fit for purpose in the new environment.
Stan McChrystal: (08:58) And really in Iraq, against the organization that became known as al-Qaeda in Iraq, we found that this very different kind of entity led with a different operating principle fueled by information technology and operating in a new, more complex environment in that situation was better than we were. It was more effective than we were. And so it wasn't a case of the visionary saying, "Okay, we could be better." It was a case of a commander taking over, expecting to operate in the way we always had — just do it a little bit better — and instead realizing we're losing it, so we've gotta make a fundamental transformation in the middle of the fight.
Stan McChrystal: (09:37) For Stan, that transformation demanded more data transparency. He took steps to act on new information faster, and encourage more sharing of intel at all levels.
Stan McChrystal: (09:46) It's often you have data you don't know you have. When I first took command of the organization — and remember I'd grown up in it, so I'm not clean here — but suddenly I become the commanding general. And in October of 2003, I'd go over to theater but particularly to Iraq to see how we're doing. I go into the main base which was outside Baghdad. I find this pile of garbage bags, plastic garbage bags. And I said, "What is this?” This was a place where we kept detainees and we had our interpreters. And they said, "Well that's intelligence materials." I said "What?" And it was laptops and telephones and printed materials that had been captured on objectives, where we'd gone and captured somebody, a bad guy, and we'd put all the stuff we captured and we'd send it down. And I said, "What's it doing here?"
Stan McChrystal: (10:29) And this pile is like 10 feet high. And they said, "Well, when the interpreter, the translators have free time, we have them open it up and look and see if there's anything of value there." Intelligence is like fruit. It goes bad very, very quick. This stuff sitting there was literally like rotting fruit and very quickly it has no value. And we were putting our people at physical risk to go capture this material. Then we're taking this trove of data and we're just leaving it in a pile to rot because we didn't have the capacity or the focus, etc. So we started changing that. We started creating exploitation cells with translators and intel analysts. We started to be able to push it back to the United States for simultaneous translation and exploitation. We really created over the next couple years a machine that did this that was extraordinarily fast and effective but the effect was even stronger than you thought.
Stan McChrystal: (11:24) Because instead of just getting the value of data that we already had, what we found was when you show that you're able to digest and use the value of data, suddenly everybody understands how valuable data is. And people across the organization start trying to get more data because they know that they see the value from it. So suddenly not only does the value of data rise, the appreciation of it across the organization. They know that all we have to do is understand this. And if we understand this, we can act on this and we can have this effect. If we don't understand, it's blind man's bluff.
Stan McChrystal: (12:00) And so we started realizing what we didn't know where that information might be, and then we would go to do operations to create an effect to produce that information. Sometimes it's just stimulating an enemy reaction so that if you know a part of an organization, you do an operation to stimulate them to react and to communicate. We could see that communication, we could learn from the connectivity to that, we could respond to that, and then we could get more and more effective. And then sometimes you realize our own systems can be our own worst enemies. In 2007, we captured al-Qaeda interacts’ foreign fighter personnel record, and that's kind of amazing.
Stan McChrystal: (12:40) You think their personnel records, what are we talking about? Well, al-Qaeda interact was very methodical. Every time a young man from North Africa or from Saudi Arabia or came in to be a suicide bomber or a fighter, they kept records on it. And we did an operation in the northern part of Iraq and we hit a target and we captured this trove of information. And I remember I was there with my intel guy and I go, "Wow, this is extraordinary. If we could just share this information with the governments of the countries that these young men came from, they could go back and they could start to dry up the sourcing of this and stop what we call ratlines coming here." And I said, "But it's classified, how do we share this?"
Stan McChrystal: (13:20) And my intel officer goes, "Well, it's only as classified as you say it is," and I said, "Wait a minute, I'm part of the big bureaucracy, explain that to me." He says, "You are the capturing command, so you get to set the classification level." And I said, "You're kidding. So I could say, this is all unclassified?" He said, "You could." And I said, "Well, CIA might not like it, other people might not like it." And he said, "They might not, but they would get over it." And so I said, "Great, it's unclassified." He said, "Well, wait a minute, McChrystal's giving away secrets to the enemy. It's their personnel records, they know what's in them and also they know we hit the target so it's not a news flash there." But instead we shared all this information from governments around the region with extraordinary effect. And so sometimes I make that point: We have data and for some reason we don't use it because we're used to not using it or we don't wanna share it with somebody, or we don't wanna take the time to put it in context of what value it may have.
Satyen Sangani: (14:18) By creating transparency, Stan freed up leaders to respond more quickly. He calls it empowered command, where teams at different levels make autonomous decisions.
Stan McChrystal: (14:27) We see the basic concept in many areas like franchising or whatever, where local entities are empowered and encouraged to adapt to local conditions so that they can produce an outcome. But what's coordinated is the overall outcome. We together are trying to put together the jigsaw puzzle or whatever it is that the joint mission is. It's critical that everybody has a common understanding of that. Within that, though, if you can unlock people's ability to get it done whichever way makes sense for them in their particular situation, you suddenly do two things. One, you get them to be much more suited to what's actually happening on the ground there, but second, they get a level of ownership. I found that if I told somebody to do a task, they might try to do that task, but if it didn't work, they'd say, "Hey, the boss made a bad decision." But if I say, "Create this effect, whatever you have to do to do that, figure it out," they owned it because they felt a level of responsibility for what approach that they chose, and it made it much stickier. You got a lot more initiative from them, you got a lot more energy and it's much stickier. And you also emerge identifying the kinds of leaders that become most effective for you.
Satyen Sangani: (15:40) It's clear that humility and curiosity are essential for solid leadership. But what other traits do data leaders need? Digital transformation expert Paul Leonardi reveals three more skills all data leaders should master.
Paul Leonardi: (15:52) We argue in the book [The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI], and I hope that we're pretty convincing about this, that you need to have approaches to three different areas to really be competent and skillful as a practitioner in the digital world. The first is an approach toward collaboration that recognizes that the kinds of collaboration that we have with people is increasingly mediated by digital technologies. There are different strategies and skills you need to be successful at communicating and interacting in that environment. And how we think about collaboration with machines is gonna be part of that approach to collaboration. The second major approach is what we call computation. This is the one that scares most people by its title because they think, "Do I need to be really advanced in non-parametric statistics to understand how my company is operating?" And I think the short answer is no, but what you do need to recognize is that we now operate in a computational infrastructure.
Paul Leonardi: (16:46) The final approach is one that we call change. And the idea there is that, we've often have this metaphor, I think, of change is something that punctuates an otherwise fairly stable existence. And that's just not the way the world works and hasn't for a while, and it certainly isn't going to in the future. That the rapid evolving nature of our tools and the increasing prevalence and availability of different kinds of data means that we're in a constant process of change. So we call this transitioning, right? We're always transitioning from one moment to the next. And how do we manage an organization and how do we manage ourselves in a way that respects the fact that change is a constant? We need to think through how we create the culture of our organizations, we need to think through how we're deploying tools in the manner that you and I already talked about, and how we do things that respect the fact that change is a constant. A digital mindset really encompasses how we approach collaboration, computation, and change. And that's what we talk about throughout the book.
Satyen Sangani: (17:50) Not every data career path is the same. Some paths include multiple stops In the C-suite. Mike Capone explains how he transitioned from a CIO role to becoming a CEO at Qlik.
Mike Capone: (18:00) The way I did it was I built out my network. I found as many people I could who had mentored me outside of my immediate space. I got very friendly with some businesspeople at ADP who I'm still friendly with today, and I asked them first for advice and then later on for opportunities. "Give me a shot at doing something that's a little bit far afield. I’d rather be a data architect, I wanna kinda work for you and your business." That's the best thing. And look, we're all introverted by trade. You don't pick writing code in a cube because you're very extroverted. So you gotta push yourself. And it's uncomfortable; to this day, it's still uncomfortable for me. It doesn't feel that way as I jump on a podcast with you but I still have to kind of psych myself up to be out there. But that's what you gotta do. It doesn't come to you, you gotta go to it.
Satyen Sangani: (18:39) And even if you remain in a data-focused role, remember that soft skills are important too. Consultant Tricia Wang stresses that as “D” makes up one-third of “CDO,” data is only part of that role.
Tricia Wang: (18:51) The biggest thing that I think a leading data science leader should be investing in is not in the common C’s of categorization and cleaning data. It's in the other C’s — which is culture, communication, and customers, and collaboration. These are the C’s that we really work with, and it is actually genuinely hard [chuckle] to do. And your job as a chief data officer or a data leader in the company is, like I said before, data is only part of your job generating the quantification to reflect back to the company. The other half is the leading edges around communication and helping the rest of your business, your business counterparts, to understand the value of this in a way that isn't scary, and where they can see that it actually is gonna improve their business.
Satyen Sangani: (19:41) Legendary BusinessObjects founder Bernard Liautaud knows a thing or two about leading a software company. He revealed his advice for leadership at an even broader level.
Bernard Liautaud: (19:49) To me, a great software company has managed to create alignment, and that alignment is basically communicated by you. If you are able to say, "Okay, this is what we're doing," you're creating that magnetic North. Everybody knows where the company is going. You yourself, you set the culture. So you lead by example, and you create an environment that enables people to achieve extraordinary things, then your job becomes a lot easier because you don't have to tell people what they need to do. You just tell them, "Just look up," and when you look up, you look at the magnetic North and then you know where you're going. Most people are gonna make decisions without your input, but they just need to know where the company's going. And if they know what their job is, where the company's going, they will make the right decision. And with everybody pushing in one direction, companies doing amazing things. Most of the companies that struggle is because people are — not people are bad — it's just that they're going the wrong or diverse directions, right? So if you have created that clarity, you make people autonomous enough under that clarity of vision, and you provide the example, I think you'll continue to achieve extraordinary ambitions.
Satyen Sangani: (21:03) Jepson Taylor, former founder and AI evangelist at Dataiku, appreciates the burden of leading both a company and its people.
Jepson Taylor: (21:10) Founders are inspiring. There's the hero template: They've got a vision, they've got people, they care. Founders care for their employees. They care a lot for them. Employees are family to them. I admire them. Having gone through the valley of suffering myself, I have a massive amount of respect for founders because they carry a weight that most people will never realize. So it's hard for me not to like them.
Satyen Sangani: (21:32) He also appreciates the unique challenges of startups. The stakes are high, the hours are long, and the success rate low. How do you lead in that climate?
Jepson Taylor: (21:40) What she's essentially saying [“To be a founder, you have to get as close to the line of lying and never cross it"] is, if you were truly honest with your employees, your customers, your partners, and people around you on what it feels like on a day you're being sued or a day you have to fire someone or on a day you lose an account, or when you're worried about burn, or you're thinking about what it'd be like to be employed again... So a lot of people talk about this. There's other CEO thought leaders that'll talk about having the worst call of the month, worst call of the quarter. And then next calls, they're high-value prospect, they're smiling. So we talk about the pit in the stomach. So it's not really talking about lying in a way as being shady, like I'm going to like nearly lie to manipulate you. Some people might misinterpret for that. It's about really being guarded on your emotions but also trying to lean in... You're lying to yourself. It's reality distortion. If we really talk numbers, the likelihood of your startup succeeding is near zero. So you're lying. You really think you're just going to succeed? And so I think those are the elements that come out of that quote, which I think are fun. But hopefully, people don't misinterpret it into “sell snake oil” or do things that are so close to lying that you can succeed, 'cause I definitely don't agree with that.
Satyen Sangani: (22:45) Great leaders lead, but they also empower others to lead, too. In this way, leadership is a bit of a paradox. On the one hand, a leader is a singular entity to whom everyone looks for direction, and yet, leadership is empty unless you empower and engage the people you're leading. From an org-chart perspective, these people are under the leader, but this image is misleading. Instead, truly great leaders see a team. They don't see rank or people of lower status. They see a critical foundation. I'll let former Tableau CDO Wendy Turner-Williams have the last word.
Wendy Turner-Williams: (23:17) I really think about data and chief data officers as they're like a supporting actor to the corporate entity as a whole. And you've gotta have those partnerships. You have to have those breakout moments where you're working in unison with these particular business units who are executing on these strategies all up. You need to be able to talk to others. You have to be able to have a learner's mindset. You have to understand what different teams and functions do and how they play into a bigger picture so that you can get into cause and effect. And then when you start to do that, you have a lot more ability to actually have impact.
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Producer 2: (24:00) This podcast is brought to you by Alation. The role of chief data officer (CDO) is more vital and challenging than ever before. Alation offers a vision for building a strong data culture that empowers people to find, use and trust data. Download the The CDO's Toolbox: 7 Tips for Building a Successful and Sustainable Data Culture, a white paper available at alation.com/CDO-tools.
Season 2 Episode 25
Sanjeevan Bala, ITV's Chief Data & AI Officer and DataIQ’s most influential person in data, embraces the ubiquity of data in the enterprise by embedding domain-specific data ‘squads’ within business units to localize decision-making. He discusses how, unlike monolithic data teams, meshy data organizations are the best way to align data initiatives with business value.
Season 1 Episode 25
During the eras of papyrus, parchment, and paper — as well as our current "paperless" age — librarians have been among the gatekeepers of information. In this episode, Alation's senior director of learning and communities, Deb Seys, brings a librarian's perspective to how data (and cataloging it) can tell stories and deliver unbiased metrics.
Season 1 Episode 13
Growth in any industry usually requires innovation. But when you challenge the status quo, you encounter different levels of risk. Bigeye CEO and former Uber data scientist Kyle Kirwan details his experiences on finding the balance between innovation and risk.