SDO 019 - Managing Enterprise Scale Data Governance Challenges
Interview: Tiankai Feng, Data Governance Leader & Musician
What are your thoughts on data governance?
Getting the business to care about data infrastructure can be an uphill battle, given how abstract data is. Combined with the underpinnings of a data product or dashboard being hidden in plain sight, it’s hard for non-data people to feel the need for data infrastructure. Yet one of the strongest levers to get the business to pay attention is through data governance.
Gartner defines data governance as “…the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.” In other words, reduce the risks associated with data and thus the risk of losing revenue. This is especially apparent in regulated industries, like finance or healthcare, where data leaks can cost companies millions; such as Anthem being fined $16M for a data breach. Even non-regulated industries face risk without proper data governance, including Bird who “overstated its revenue for more than two years by recognizing unpaid customer rides.”
These risks only become amplified as your company scales to the enterprise level. This is exactly why I’m so excited for you to hear from Tiankai Feng about his experience in data governance within a 60K+ employee organization.
— Mark
Hear from Tiankai Feng, Data Governance Leader & Musician:
Tiankai Feng is an extremely talented data professional, has worked at one of the top brands in the world, and worked on some extremely interesting data projects at the enterprise scale… but that isn’t what caught my attention. What caught my attention was Tiankai’s creative music combining data, singing, and the piano! Prior to my passion for data was my love of dancing, where I used to street perform and was part of a dance crew. Thus, I always gravitate to creatives such as Tiankai, as that creativity allows you to view the world in a unique way. The moment I heard one of his songs, I knew I had to interview him on this newsletter to learn how he thinks about data.
Your most recent position was heavily focused on data governance within product. What makes this role so difficult, and how can data teams best navigate the complexities of data governance?
Tiankai: “Very good question. I think data governance generally depends heavily on the data domain that you're in, because with different data types and different data domains, you have different challenges. Being in the product data domain means, especially in companies like Adidas or any, let's say footwear or apparel company, that it's really the core of what a company does. Basically, how revenue is generated. That means also that in these organizations, many people are working with real data. So it's a very complicated stakeholder landscape.
Also, I would say that as compared to more regulated industries like the financial industry or the pharmaceutical industry, the footwear and apparel industry is-- besides some sustainability regulations-- not as much regulated, and that makes pushing data governance and data quality measures a lot harder because it's all based on good intent and convincing people, and it's not externally pushed basically to do so.
And lastly, I would say that there's really a lot of different knowledge and motivation levels across different stakeholders. So balancing the resistance versus the advocacy of it all and making it all work together is really hard as well.
So in total, I would say in summary, that means for me in the triangle of let's say, people, process, and technologies, the people part is definitely the hardest, and it also requires the most focus when it comes to data governance. So I'm usually paying a lot of attention to communication and relationship building as the key pillars for successful data governance.”
What are the unique data challenges experienced by enterprises, as large as Adidas, and how do you navigate them as a leader?
Tiankai: “I would generally say in data management, you have the data creator side and the data consumer side, right? And especially when you have a big company, that means you have a high amount of data creators and a high amount of data consumers. And the bigger the organization is, the less they talk to each other rather simply put.
And that means there's just a bigger symmetry of information and knowledge and all of the misunderstandings too. That means that there's a lot of mismatch regarding the intended purpose of data and the actual use case of data, and then that often leads then to dissatisfaction and sometimes even conflicting requirements towards data quality.
If you put on top then the ever-changing IT landscape of different data systems being replaced or changed or upgraded or whatever, that makes it even an operational challenge because as a data governance team, you would have to balance projects where you have basically helping with like new systems being integrated, versus the actual end-to-end governance on an existing landscape.
This, having said, is what I think are the biggest challenges in a big enterprise. And the only, I think, direction to make it better is really open transparency and being honest with each other on what is going on. And if you have a central place, like a data catalog where you can already centralize a lot of the transparency there, that would be nice. But you'll still need that communication element to let everybody proactively tell each other that things are happening and there are certain data being created and certain data being used to really avoid these misunderstandings. I think a big part of it also is assessing with stakeholders together about the impact of data, because you also want to make sure you prioritize the right things. So it all goes back again to just transparency and communication, I would say.”
You are one of the most musically inclined data leaders I know. What made you decide to mix music with data, and how do you integrate music into your leadership style?
Tiankai: “Yeah, it's a great question. I don't think I get that question often enough, to be honest. I'm very glad to answer that. So maybe just for context, I started playing piano when I was five; admittedly forced by my father because it was his dream and he put it on me because he couldn't learn it when he was little. But I was only liking playing piano when I was 10. And that journey from being forced to and actually seeing the value itself in playing the piano already gave me a lot of starting points on what happened and how I would bring it to others to do things they might not enjoy in the beginning. But either way, I started writing songs when I was 15. I played at a bar pianist during my study years as well, which was a really good gig that I had.
So music has always been a big part of my life. I just hadn't shown that side actually too much on the professional channels yet before, that only happened now a few years ago. And I think for me, actually, music and data have more common than people think because the analogy would be that music consists of certain notes, right? And notes are predefined in the frequency physically and everything. It's just like data points that are predefined with certain values, right? But how you put it all together, how you visualize it, how you tell a story around it, or how you basically phrase it. This is when artists create it. So basically you combine it all, use different variations of it, and that makes it art from a very technical point of view, from very scientific point of view, all of a sudden becomes art.
And this is how I would see working with data as well. It doesn't have to be all super scientific if you can make art out of it. And there's so much flexibility now in the new world to actually make art out of this. So in this way, my music mindset actually I think makes me a better leader because I try to inject creativity in everything I do and also would work with that with my team members on it.
And I think for those people that don't work in data, that actually they have the opposite perception. They think it's like this numbers crunching, Excel opening job, that everybody just typing numbers all the time, but it really isn't anymore. And at this point, data people have gotten so good as well in communicating that it's just a new world of working on data.
Lastly, I would say making music together is a whole thing on its own right? Being in a band or being like a jazz big band makes you individually perform as part of a bigger thing. But you have to listen very actively to everybody else too, to actually fit in. And that is for me, like the ne plus ultra definition of teamwork, right? Where it's not only about you performing, but knowing what everybody else wants and where they're at, and basically being part of the bigger whole.
And this is something that I think also gave me a lot about just meeting culture or collaboration aspects to be not only the talker but also the listener. And then basically driving it together as teamwork. So yeah, I think that is how I would basically see how my music self is influencing my data leader self. And I'm very happy I can combine the two now, actually a lot more.”
Person Profile:
Tiankai Feng is a data governance leader and musician. Feel free to connect with him on LinkedIn to learn more about his work, as well as catch his latest data songs. His exact words were "I’m always looking for exchanging knowledge and best practices [with people].”
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