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What’s up everyone, today we have the pleasure of sitting down with Tiankai Feng, Data & AI Strategy Director at Thoughtworks and Author of Humanizing Data Strategy.

Summary: Data governance feels like the Jedi Council, steady with its rules, while marketing ops moves like the Rebel Alliance, quick to adapt when perfect data never arrives. Tiankai believes progress comes from blending discipline with curiosity, bringing data in early as a partner, not a critic. He’s seen teams thrive when they pick trade-offs upfront, document how everyone fits together, and take ownership of clean, reliable inputs instead of trusting AI to fix sloppy work later. Even the best tools still need humans to design the logic behind the scenes. When teams care about context and build real relationships, data becomes the backbone that keeps marketing strong under pressure.

In this Episode…

• How Data and Marketing Create a Symbiotic Relationship
• If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel Alliance
• How to Organize Data Teams and Improve Marketing Collaboration
• How to Handle Data Disagreements Without Crushing Creativity
• How to Use Shadowing to Fix Broken Marketing Alignment
• AI’s Impact on Data Quality, Analytics Careers and Composability
• How to Use Authentic Communication to Build Influence in Marketing Ops

About Tiankai

Tiankai Feng is Director of Data & AI Strategy at Thoughtworks, where he leads global service offerings spanning data governance, AI strategy, and modernization initiatives. He is the author of Humanizing Data Strategy – Leading Data with the Head and the Heart, and serves on the Education Advisory Board at DataQG. 

Previously, Tiankai spent over six years at Adidas as Senior Director of Product Data Governance, shaping data practices across global teams. He is also Head of Marketing at DAMA Germany, helping grow the country’s leading data management community. Earlier in his career, Tiankai worked as a senior consultant with TD Reply, advising major brands on digital strategy and performance. Recognized as a top data product thought leader, he is passionate about bridging the gap between technical excellence and human-centered data cultures.

Building Marketing Alignment Through Data and Collaboration

How Data and Marketing Create a Symbiotic Relationship

Most data professionals do not start their careers by obsessing over why advertising can make people feel something. Tiankai shared that he studied campaigns as a kid and felt driven to decode the hidden mechanics behind each message. He called it the science behind the feeling. He wanted to understand why a phrase could trigger a decision and what evidence proved it actually worked.

When he chose his degree, he blended marketing with database systems because he believed data could ground creative work in reality. He wanted a way to measure the effectiveness of ideas instead of relying on gut reactions. That decision led him into marketing analytics, where he learned to balance instinct with structured evidence. He described this period as the moment he first saw every click, conversion, and impression as a trail of signals pointing to what people valued most.

Tiankai shared that many companies separate marketing from data in ways that weaken both. He believes that every creative idea grows stronger when it gets tested by proof. He said, “You have a lot of thoughts and gut feelings, but what if you could actually rely on proof to make better decisions?” He still asks this question whenever he evaluates a strategy or decides how to communicate the value of a data project.

He also applies marketing principles inside his own teams. He treats internal projects like product launches and focuses on storytelling as much as reporting. He learned that evidence alone rarely convinces stakeholders. People respond when data feels relevant and easy to act on. He credits this mindset to his early work in brand campaigns, which taught him that information becomes meaningful when it connects to someone’s goals and emotions.

“By heart, I’m still a marketer,” he said. “Even now, I’m applying what I learned in marketing to convince stakeholders to work with me.”

This blend of skills helps teams create strategies that people believe in and understand. When marketing and data share the same goals, campaigns feel both credible and inspiring.

Key takeaway: Blending marketing analytics with creative thinking lets you challenge assumptions and build strategies that people trust. When you share data work, present it like a product launch. Frame the message in relatable stories, make the numbers clear, and show how the information supports better decisions. That way you can help teams act with confidence and prove the impact of their ideas.

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If Data Governance Is the Jedi Council, Marketing Ops Is the Rebel Alliance

It’s true. I keep borrowing Star Wars metaphors to make sense of the work. Sue me. haha

Tiankai described clean, governed data as the Jedi Council, the calm authority that brings order and discipline. He shared that marketing operations always felt more like the Rebel Alliance, a team of underdogs improvising bold plans and building strategies out of whatever they could find in the hangar.

In those early years, nobody had a clear guidebook. Teams cobbled together workflows, tested ideas with half-finished data, and celebrated any dashboard that did not explode during a quarterly review. Tiankai remembered feeling like every small win was a victory against the Empire of bad processes. This scrappy environment fueled creativity, but it also came with plenty of late nights and occasional panic.

Today, marketing ops feels more settled.

“There’s more experience and more best practices to be shared,” he said. Teams now have detailed frameworks, polished documentation, and tools that mostly work the way they promise. That way you can spend less time guessing and more time refining campaigns that drive results. You can treat the Jedi Council as a helpful ally rather than an unreachable ideal.

Tiankai still believes good operators keep a bit of rebel spirit. Even the best-governed data will sometimes contradict reality on the ground. When those moments happen, it helps to trust your instincts and build something that makes sense for your business, not just the standard playbook. The Jedi Council can provide discipline, but someone still has to step into the hangar and fly the mission.

Marketing operations has grown up, but it never lost the urge to experiment. The work feels rewarding when you blend clear frameworks with your own curiosity and a willingness to bend the rules when the stakes demand it.

Key takeaway: Data governance acts like a steady Jedi Council, giving your marketing operations clarity, trust, and a strong backbone. To get the most from it, combine those proven systems with the resourcefulness of a rebel team. Stay ready to challenge assumptions, tweak the plan, and follow your judgment when data alone does not tell the full story. That way you can build workflows that are disciplined enough to scale and flexible enough to handle reality without falling apart.

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How to Organize Data Teams and Improve Marketing Collaboration

It is interesting to consider how data ownership used to feel like an afterthought in early SaaS companies. Tiankai remembered scraping together metrics by hand, jumping between marketing dashboards and brittle product logs. Engineers built tracking because nobody else would, and marketers learned enough SQL to be dangerous. Now, even small companies usually have at least one data professional, but confusion around ownership and process has only multiplied.

Tiankai explained that teams face a constant tug-of-war over where data roles should sit. Centralized BI teams create consistency. Definitions stay clean, and duplicate reports are less likely to sprout overnight. However, centralization makes it harder for marketers to get timely help. A simple question about funnel drop-offs can become a two-week ticket that arrives just in time for priorities to change. Embedded analysts can work faster and build stronger context, but they often drift into silos and develop conflicting definitions for the same metrics. Tiankai warned that no structure solves every problem. He encouraged leaders to choose deliberately rather than pretend trade-offs do not exist.

He described the tension in clear terms. When data teams sit close to decision-making, they can act quickly, but consistency becomes harder to protect. When they sit farther away, they can maintain standards, but business teams feel disconnected. Tiankai recommended defining how data professionals engage with marketing work so each side knows what to expect. Leaders should outline how requests get prioritized, who maintains documentation, and how disagreements about definitions get resolved. That way you can avoid the passive-aggressive cycle where nobody feels accountable.

“As long as you have shared goals,” Tiankai said, “it really shouldn’t matter what function you’re sitting in.”

He also shared advice for marketers who want to improve collaboration with data teams. Skilled data professionals will ask for business context before jumping into technical requirements. They will look for the story behind the metric rather than simply extracting numbers. If they skip that step, marketers should step up and explain why they care about the request and what decisions rely on the answer. Tiankai encouraged marketers to see this as an act of empathy instead of an extra chore. A few minutes spent explaining goals can save hours of confusion later. He said that basic understanding is more important than any tool or process.

Key takeaway: Choose your data team structure based on the trade-offs you are willing to live with, and document how roles interact so expectations stay clear. Embed shared goals in your operating model so data pros and marketers feel responsible for the same outcomes. Offer context early when making requests, and look for signs that your data partners care about understanding your objectives. A mix of clear accountability and mutual respect creates smoother collaboration and better decisions.

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How to Handle Data Disagreements Without Crushing Creativity

Data has a way of draining the energy out of a room, especially when it contradicts a marketing campaign everyone has already fallen in love with. Tiankai has watched entire project teams freeze when performance numbers show that the audience did not care about the story they were trying to tell. He has also seen how quickly that discomfort can turn into finger-pointing. Marketers often feel the need to defend their creative instincts, and engineers feel obligated to guard the integrity of their technical solutions. Each side believes their perspective carries more weight, and that assumption poisons collaboration before it even starts.

Tiankai believes the problem usually begins long before the campaign launches. Many teams wait until results are flatlining to bring data into the discussion. By then, it is too late to make changes without triggering panic or blame. He has a clear stance that if you want a healthier relationship between data and creativity, you need to embed analysis during the early planning stage. When you do this, you reduce the emotional charge later because everyone has already agreed to learn from the evidence together.

“Disagreement is a way to find common ground, and it only works when people stop interpreting every question as a judgment of their skills.”

He has a practical method for making these conversations easier. Instead of dumping a spreadsheet on the table and expecting instant alignment, he recommends telling a structured story about what the data means. He encourages teams to share specifics and propose actionable tweaks instead of vague conclusions. For example, you can offer choices:

• Shift budget toward the channels where your audience spends the most time.
• Adjust messaging to highlight the benefits customers actually mention.
• Rework visuals to match the formats that performed strongest in testing.

That way you can create a collaborative plan that feels like a shared solution instead of a verdict on who got it wrong.

Tiankai often calls out a behavior he sees across marketing teams: the tendency to ignore signals in the data when those signals threaten the creative narrative. He has no patience for the idea that data is an optional add-on you check after launch. In his experience, when teams treat metrics as an afterthought, they end up spending more time cleaning up failed campaigns and repairing trust with stakeholders. Early data integration gives everyone the chance to pivot without embarrassment, and it usually leads to better decisions and stronger outcomes.

Tiankai shared that his biggest career disagreements have often centered on the tension between pursuing commercial success and acting responsibly for society or the environment. He frequently uncovered consumer data showing that people cared about sustainability and inclusivity, yet companies still chose the cheapest production methods and heavy discounting. He used to push relentlessly to convince teams to do the right thing, which sometimes made him seem difficult to work with. Over time, he learned that timing and openness matter as much as the argument itself. He realized that waiting for a better moment often leads to more thoughtful decisions, and trying to force change before others are ready rarely ends well.

Key takeaway: Bring data into the creative process before you launch anything. Share clear recommendations alongside evidence, and speak in practical terms that help everyone see how to improve. When you position data as an early partner rather than a postmortem judge, you protect relationships, build trust, and make it easier to adapt creative ideas to real audience behavior. If you advocate for bigger changes, like sustainability or inclusive practices, be thoughtful about timing and team readiness. The strongest argument often succeeds when you wait for the right moment to share it.

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How to Use Shadowing to Fix Broken Marketing Alignment

Most marketing teams think more campaigns will solve every revenue problem. Tiankai has observed this pattern repeatedly while working with brands like Adidas and Volkswagen. Each department builds its campaigns in isolation, convinced that more launches will drive more sales. Everyone wants to hit their quarterly numbers, so they layer campaign after campaign without checking how it all stacks up in the real world. Customers see a noisy parade of disconnected messages and lose track of why they should care.

Tiankai decided to spend one day every week shadowing the teams responsible for these launches. He did not schedule a polished workshop or a tidy presentation. He sat in meetings, watched people struggle with trade-offs, and took notes on everything that shaped their choices. He shared that this practice only works if leaders agree to let you step away from your own deliverables. He explained that the payoff was worth the trade. That investment built real understanding that reduced arguments later. He put it plainly:

“The time you do not spend learning about your colleagues will always show up later as friction you have to clean up.”

He described how he used survey data to challenge the assumption that more campaigns lead to more impact. He would lay out a comparison of search volumes for different launches. In many cases, ten campaigns running side by side created confusion instead of energy. Customers could not remember which product mattered. When teams saw this data, they finally recognized how a scattered calendar could erase months of creative effort. Tiankai recommended using a blend of research sources, including:

• Brand trackers to measure perception over time.
• Social listening to see which themes resonate.
• Search behavior to gauge what customers actually want to learn.
• Web analytics to confirm where attention flows.

He explained that combining these signals helped him build a story that teams could believe. He showed how competing priorities fractured attention into thin slices. When everyone launches at once, no one wins. He encouraged marketers to pick a smaller number of campaigns and build momentum behind them in sequence. That way you can create a clear story customers will remember and share.

Key takeaway: Shadowing teams for a day each week helps you understand the hidden pressures behind disjointed campaigns. Use clear data from search trends, social listening, and brand tracking to demonstrate when competing launches fragment customer attention. Align with stakeholders early to sequence fewer, stronger campaigns. That way you can keep your brand top of mind without exhausting your audience.

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AI’s Impact on Data Quality, Analytics Careers and Composability

The Comeback of Data Quality

Data quality finally has its moment in the spotlight, and Tiankai calls it the “comeback of data quality.” For years, marketing teams shrugged at duplicates clogging CRMs and shrugged harder when someone tried to budget for cleanup. Everyone complained, but almost nobody wanted to be the person vacuuming up bad records. Now AI has arrived, and the stakes are no longer theoretical. When a large language model trains on sloppy inputs, it delivers sloppy outputs, and the executives who once dismissed data hygiene as a side project are suddenly paying attention.

Tiankai points out that pre-trained AI models still need your company’s clean, contextual data to deliver anything useful. Even the most sophisticated system cannot compensate for broken IDs and partial records. When leadership starts demanding an AI-driven customer experience, the conversation shifts from abstract potential to hard limitations. No one wants to admit their data is the bottleneck. Yet that’s exactly what happens when a model built to predict churn or recommend the next best action trips over the same misspelled names and half-complete profiles that have been ignored for a decade.

A big reason this problem persists comes down to how customer data gets created in the first place. Tiankai describes the process as “highly manual.” Humans type their own names wrong, sales reps slap in quick entries to hit activity quotas, and marketing ops inherits the mess without a reliable reference point. If a customer mistypes their last name, no AI will divine the correct version later. The error moves downstream into every report and every model. You can see how quickly this snowballs into an expensive liability.

“The more you can help the people that enter the data for the first time to already put in the right data, the less you have to deal with all of the rest,” Tiankai said.

Fixing this starts with two actions. First, train everyone who touches data to slow down and validate what they input. This means showing them why it matters, not just telling them to care more. Second, add guardrails that flag obvious mistakes before they get saved. Tiankai shared examples like prompts that ask, “Are you sure this is the correct address?” or warnings when a name looks suspicious. You can combine both strategies so records get a second look before becoming permanent. That way you can spend your time building smarter workflows instead of cleaning up after everyone else.

Key takeaway: Treat data quality as a nonnegotiable prerequisite for any AI initiative. Build guardrails into your data capture workflows so errors get caught when they happen. Train your teams to slow down and check inputs instead of relying on cleanup projects later. Clean data at the source so your AI models work as promised, your reports stay trustworthy, and you reclaim hours every week that would otherwise be wasted on fixing the same problems over and over again.

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How Natural Language BI Tools Change Data Analyst Work

AI panic has turned the analyst role into a lightning rod for every fear about career obsolescence. Tiankai calls out how shallow this panic can be. He hears the same story recycled at every conference and on every LinkedIn soapbox. The version goes like this: analysts only write SQL queries, so as soon as a chatbot spits out a bar chart, the job dissolves. That version ignores every messy, unglamorous step that happens before a dashboard exists.

Translating a business question into data is not a mechanical task. It demands curiosity and the patience to chase clarity when people are vague about what they want. Tiankai describes the thinking work that happens long before the first line of code. He says you have to start by figuring out what you are even looking for, which tables hold the right fragments, and which assumptions you need to validate. You do not get that from an auto-generated widget, no matter how fancy the interface looks.

“Coming up with the query itself takes a lot of thinking work and communication work to even get there because you need to first know what you’re even looking for,” Tiankai says.

AI has made visualizations easier. Natural language tools now let anyone ask plain questions and get a quick graph. Tiankai sees a future where this “vibe coding” becomes standard. You will have a bot that builds dashboards on command. That sounds slick, but the real work shifts further upstream. Someone still has to create the semantic layer so the bot can function. Without clean definitions, the AI churns out shiny nonsense.

He predicts analysts will spend more time as translators, but not in the old sense of turning raw data into a PowerPoint. They will define the knowledge graph and translate business language into structured logic. That way business users can query data without breaking everything. The role will involve fewer clicks and more stewardship. The skills stay the same, but the focus tilts toward enabling self-service without surrendering rigor.

Key takeaway: Analysts will not vanish when AI handles front-end reporting. Their work will evolve into designing semantic layers and translating business needs into machine-readable structures. You can future-proof your career by learning how to build and maintain these frameworks, because the best natural language BI tools still depend on thoughtful human definition behind the scenes.

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How Composable Data Management Works in Marketing

Composable data management has become the default strategy for teams tired of waiting for the central data department to crank out reports that arrive too late. Tiankai described how everything started with giant warehouses where data sat behind strict permissions. People had to file tickets, sit in queues, and cross their fingers that their priorities would not expire before the report showed up. Centralization created control, but it also smothered agility when business teams needed fast answers.

Data lakes came next and promised freedom. Companies stored everything in an unstructured swamp, assuming this would somehow unlock creativity. In reality, teams still relied on specialists to clean and shape the data into something usable. Tiankai saw marketers rummaging through incomplete tables and duct-taped dashboards. He noticed how the term “self-service” often meant “do it yourself with little support.” When leaders started exploring data mesh and data fabric, they wanted a better path. These frameworks gave ownership to individual teams, letting them create and maintain their own data products.

“The main driving force behind all this is that centralized teams became bottlenecks,” Tiankai said. “Business functions want to use data quicker and for more timely decisions.”

Today, most companies run a hybrid of old and new systems. They use semantic layers to protect definitions from drifting apart. They deploy real-time dashboards so marketers can grab fresh metrics without waiting for an extract. They also rely on governance platforms that flag suspicious queries or accidental compliance risks. This mix feels messy, but it supports fast decision-making when people understand the rules. The energy in these teams comes from knowing they control their own data destiny rather than waiting on a distant group to hand them reports.

AI is heading in the same direction. Companies build central innovation hubs to experiment with models, then distribute those learnings across business units. Tiankai expects AI to follow the same arc as data management. Each department will run its own models and workflows. The cycle will repeat because speed wins when everyone demands autonomy. Data professionals who want to stay relevant will need to embed themselves in the teams they serve. You will need to learn the language of marketing, sales, or operations so you can help shape data that actually drives decisions.

Key takeaway: Composable data management grows when teams claim ownership and build accountability around their own data products. Learn the business context of the teams you support. That way you can design systems that scale, build trust in your numbers, and keep your work relevant when the next wave of technology hits.

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How to Use Authentic Communication to Build Influence in Marketing Ops

Solo marketing ops professionals often feel like their week blurs into a haze of Slack pings, fire drills, and half-finished dashboards. Tiankai believes most people underestimate the value buried inside that chaos. You might spend hours extinguishing one crisis after another, but that does not mean your work lacks substance. He argues you need to reframe those reactive efforts as real achievements. If you dismiss everything as random noise, you will have nothing to share when you sit down for coffee with a stakeholder.

Instead of adopting a copy-paste playbook for self-promotion, Tiankai recommends getting deliberate about how you communicate. He suggests you dedicate at least 30% of your time to relationship building and visibility. He has seen too many operators latch onto a single tactic, like blasting a newsletter to everyone, and then wonder why it feels hollow. That happens because it clashes with their personality. Tiankai puts it simply:

“Be out of your comfort zone, but do not try to be a different person.”

He encourages you to pick a style that feels natural. If you prefer one-on-one conversations over all-hands meetings, schedule weekly coffees with key decision-makers. If you get energy from small group discussions, start a monthly working group. Avoid forcing yourself into formats you hate. That way you can build relationships without draining your motivation.

A lot of marketing ops folks assume they have nothing worth sharing. Tiankai challenges this idea. He believes you need to study your own patterns. If you spend every day fixing problems, document how you do it. Maybe you have a knack for prioritizing under pressure or creating quick patches that stabilize broken processes. Even the way you triage is a skill. When you can describe those skills clearly, you will stop feeling like you are making excuses for your existence.

He also points out that your mindset shapes how others perceive you. If you show up to every conversation saying you have no idea what you are doing, people will start to believe you. Instead, build a habit of storytelling:

• Pick one or two concrete examples each week.
• Explain the problem, what you did, and the impact.
• Share them in your own words without jargon.

That rhythm builds credibility. It also helps you see progress in work that often feels invisible.

Key takeaway: Set aside 30% of your time for authentic relationship building. Choose communication methods that match your personality, whether that means one-on-one chats or small groups. Document patterns in your reactive work so you can share them confidently. When you talk about your work as meaningful contributions, you create influence without pretending to be someone else.

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How Sci-Fi Passions Inspire Work Life Balance

Tiankai manages to keep a grip on his sanity while driving AI strategy, sitting on boards, writing books, and raising two kids. He builds his work around curiosity that refuses to disappear, even when professional demands keep stacking up. He described his fixation on Back to the Future and said he feels drawn to the Broadway musical adaptation. He wants to hear the new songs layered onto the story he grew up with because it feels like combining nostalgia with discovery. He plans to make time for it.

He has been toying with the idea of a sci-fi novel for years. When asked if he would ever merge data and time travel into a book, he lit up and started sketching ideas out loud. He said it could be a story about an AI backup solution, the kind of thing that works like a MacBook Time Machine but expands into a bigger narrative about how teams protect information. He also suggested bringing AI agents into the plot. They could travel across decades to see which tools survive and which ones fade into the archives.

“I have thought about adding AI agents into the story,” Tiankai said. “Imagine an AI that can travel back in time to see how technology evolves and which tools survive.”

We shared our own version of the idea, maybe writing a story where a marketing operations manager goes back in time to a period when there was no Martech. The protagonist would discover automation for the first time and debate which tools would still be around fifteen years later. Tiankai nodded and said that sounded like a great idea. He never pretended these ideas were trivial distractions. He treated them like seeds worth planting.

He also talked about why he wrote Humanizing Data Strategy. He explained that most data failures begin with human issues. Teams fall apart because they struggle with communication, collaboration, or building competence. He designed the Five Cs framework to help organizations focus on people first. The Five Cs include:

• Competence
• Collaboration
• Communication
• Creativity
• Conscience

He filled the book with stories and examples so readers could apply the ideas without getting lost in jargon. He laughed that the colorful cover might be the real reason someone picks it up, but he cares deeply about the message inside.

Key takeaway: You can lead AI strategy and still let your creative ideas run wild. Tiankai shows that making space for side projects and personal interests keeps you motivated and connected to your work. When you stop treating curiosity like a liability, you give yourself permission to build something more sustainable and human.

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Episode Recap

Data governance feels like the Jedi Council, calm and steady with its rules, while marketing ops races around like the Rebel Alliance, ready to tweak the plan when perfect data never shows up. Tiankai believes the real progress starts when you blend that discipline with a little creative rebellion. He’s seen teams transform when they stop treating data as an afterthought and start inviting it in early, not as a critic but as a collaborator who helps shape ideas before they hit the market.

Structure sets the tone. Centralizing analysts keeps reports clean but slows everything down. Embedding them in marketing teams speeds things up but creates clashing definitions. Tiankai suggests choosing your trade-offs upfront, documenting how everyone connects, and building shared goals so no one ends up pointing fingers when something breaks.

Data quality drives everything. He’s watched companies drown in cleanup because they trusted AI to polish bad inputs. Guardrails, slow double-checks, and clear accountability save more time than any fancy dashboard. Even as natural language BI tools promise effortless answers, someone still has to build the frameworks behind them.

Ownership matters. When teams treat their data products like something worth caring about, they design systems that adapt as tools evolve. Tiankai pairs this mindset with genuine relationship-building. He blocks time to talk to people, shares what he’s learned in plain language, and treats his work like it matters.

When you mix discipline, curiosity, and a little bit of nerve, data stops feeling like a burden. It becomes the backbone that lets marketing stand up to hard questions and still look proud.

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Follow Tiankai 👇

• LinkedIn
• Humanizing Data Strategy: Leading Data with the Head and the Heart

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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