Cuyler Hansen

We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

31

3 Comments

Dr. Ramla Jarrar, an MMM expert, shares an important perspective every marketer using MMM should read. The customer journey is complex, and while MMM is a powerful tool for understanding how spend impacts that journey, its limitations are often overlooked and nuances brushed over once results are packaged in polished decks. The tension I often see: marketers need one strong KPI to track large marketing investments, but the complexity of measurement means no single framework provides that holy grail metric. Measurement methodologies are sophisticated and technically robust, yet each has constraints. Triangulating across multiple frameworks—MMM, incrementality testing, attribution, brand tracking—provides better insights, though interpretation remains as much art as science. This requires partnership. Marketers need to understand what each methodology can and cannot deliver. Analytics partners, whether internal teams or external consultancies, have the responsibility to explain assumptions, acknowledge limitations, and help interpret results responsibly. When both sides engage with this complexity honestly, we make better decisions. A question for marketers: where do you find the biggest gaps between marketing performance measurement and the questions you need answered? #MarketingMixModeling #MMM #MarketingAnalytics #MarketingMeasurement #MarketingROI #DataDrivenMarketing #MarketingStrategy

1

Building a modern data platform isn't just about technology, but having the right partners. Rittman Analytics just published a case study on our work together at BARK. They didn't just migrate us to BigQuery and Looker—they embedded with our team and fundamentally elevated how we work. Through hands-on coaching, skills transfer, and establishing best practices, they ensured we could independently manage and scale the platform long-term. The technology transformation was table stakes. The real value was building a more capable, confident data team with modern processes we now own completely. The result: a single source of truth, self-service analytics across the business, and a strong foundation layer for AI. If you're evaluating data consultancies, this is the model to look for—partners who work themselves out of a job by making your team better. Thanks to Mark Rittman Lydia Blackley Bailey Sharp-Ledger Lewis Baker Olivier Dupuis https://lnkd.in/ehu6PhxJ

37

5 Comments

Excited to share a little side project I've been tinkering with: statlingua! 🗣️ For those of us who spend a good chunk of our time translating statistical concepts into plain English (whether it's for stakeholders, collaborators, or even just our future selves!), I thought it would be neat to have a tool that could help automate some of that. statlingua is a pretty simple R package that aims to generate natural language descriptions of common statistical models and results. Think of it as a way to get a first draft of an explanation for things like regression coefficients, model summaries, and maybe even some basic visualizations down the line! It's still very much in development, so expect rough edges, potential inaccuracies, and a healthy dose of "coming soon" features. However, I'm putting it out there in the open in case anyone else finds the idea intriguing or wants to contribute. You can check out the (very basic!) code and contribute at https://lnkd.in/g3py6d6f. #rstats #datascience #statistics #opensource #genai #LLM

55

2 Comments

Why Nonparametric Stats Might Be the Most Underrated Tool in Your Quality Toolbox Most statistical tools assume your data is "normal"—but your processes rarely are. Skewed scrap rates? Highly variable cycle times? Small sample sizes? That’s real-world data—and that’s where nonparametric statistics shine. They don’t care about bell curves. They work with medians instead of means. They let you compare, rank, and test when your data breaks the rules traditional stats rely on. If you're a manufacturing professional making decisions with messy, non-normal, or hard-to-quantify data, it's time to learn the tools designed for exactly that. Practical. Powerful. Surprisingly simple. Nonparametric Statistics for Manufacturing Decision Makers. Because sometimes, "non-normal" is just normal. #Manufacturing #QualityEngineering #ContinuousImprovement #ProcessImprovement #LeanSixSigma #DataDriven #StatisticalAnalysis #NonparametricStatistics #DataAnalytics #Upskilling https://lnkd.in/eSwk7YYn

2

https://lnkd.in/gMiv7ZQB Special thanks and sincere appreciation to Vijay Mehrotra and Kara Tucker for publishing a highly favorable review of Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, Without the Hype, the new book that I co-authored with Evan Shellshear, and my new book The Art of Data Science: A Practitioner's Guide in INFORMS Analytics online magazine. Both of these books provide invaluable insights delivered in the context of real-world case studies in the form of lessons learned, through both failure and success, best practices, and practical tools to increase Analytics, Data Science, and AI project success rates. As the AI hype train risks de-railing due to a dearth of wide spread, significant, tangible business value and economic impact delivery, now is the time to understand the fundamental elements of risk that so often jeopardize these projects and initiatives. Contrary to the instincts of many leaders and practitioners, the primary challenges lie not with the AI technology or mathematics--- but rather "human factors" and "business factors" are the most salient determinants of success and failure. Change management, a focus on the underlying business problems & processes, prioritizing and selecting projects based on the most relevant business problems and opportunities and their commensurate business value potential, overcoming data issues, committed leadership, a culture of getting and doing better, and delivering models from the "sandbox" into production systems, all play a meaningful and vital part in achieving success, and sidestepping failure.

18

Dashboard fatigue is real. Staring at one can create a glazed, near data blindness—like staring into the sun. I hear so many clients saying "What I really need is someone who can have a real conversation about what the data means and what to do about it." After 20 years of trying to automate the human out of research, are we on the cusp of clients paying a premium to get the human back in? My Magic 8-Ball says 'Signs point to YES' (but with conditions!). As an industry, we've gotten very good at delivering data fast and cheap. Platforms are proliferating. Self-serve tools are abundant and ostensibly democratize access. Anyone—literally ANYONE—can run their own surveys, pull their own reports, query their own data lakes. (In most states, you need a license to cut someone's hair... but I digress) With all of that access, I'm seeing more uncertainty, not less. You still have to figure out what it all means, and that can be a very risky sport when played alone. The latest GRIT data is striking: 42% of buyers now prioritize consultative storytelling over raw data delivery, a 15-point jump from two years ago. In my mind, that's a bit of a boomerang effect. Is it possible that in the rush to speed, economy, and autonomy, some research buyers over-rotated? Probably. So what does that mean for 'consulting'? It's not methodology expertise. Clients can look that up, and frankly, AI can explain most methodologies better than many of us can. It's not simply data access. They already have more data than they know what to do with. But because consultants typically have access to a much more diverse swath of data, they are often better at pattern recognition across contexts that a client is unlikely to ever see internally. When you've watched 50 brands navigate the same challenge, you bring perspective that no amount of internal data can replicate. The best consultants are willing to say 'you're asking the wrong question' when a dashboard would simply serve up 43.7%. And then there is the challenge of translating insight into action. "Here's what consumers said" is data. "If I were in your shoes, here's what I would do Monday morning and why" is consulting. I wrote a few months ago about the shift from "projects to decisions" as the unit of value in our industry. Companies making this shift successfully are changing who they hire, how they structure teams, how they measure success, and even their fundamental economic models. It's a holistic transformation, not a minor repositioning. Success in this new world order requires pairing speed with judgment. Speed is necessary, but not sufficient. If it's your only calling card, you'll be competing with tools that are getting faster (and cheaper!) by the hour. If your value proposition is "we help you make better decisions, faster, with more confidence," the market for that is growing. Thoughts? Concerns? Expressions of emotion?

75

24 Comments

One insight that stood out to me from this week’s #AnalyticsLeadershipProgram session: 👉 Analytics success starts with defining the why — not the data, not the technology, and not the model. I was surprised by how often use cases fail simply because the business problem isn’t clearly defined or aligned across teams. The Analytics Use Case Canvas made this very visible — it forces clarity around the challenge, stakeholders, value, and success criteria before anyone starts building. 💡 How I’m applying it: Before moving forward with any use case, I’ll start by asking: “What decision will this influence — and what will change because of it?” 🧪 What I tried for the first time: Framing value in three dimensions — economic, tangible, and intangible. This helped shift the conversation from features and models to outcomes and impact. Question for you: When evaluating an analytics or AI use case, what do you look at first? 🔹 Business problem 🔹 Data readiness 🔹 Technical feasibility 🔹 Value potential 🔹 Something else? Curious to hear your perspective. #AnalyticsLeadershipProgram #learnSAS #SASVoice #AILeadership #AnalyticsStrategy #DataDrivenDecisionMaking

25

2 Comments

I’d have to agree with this take. Too often, data teams get stuck in a reactive “service desk” model—fielding requests, building dashboards, and moving on. That approach might keep things moving, but it rarely drives real business impact. We should be embedded in the business—anticipating needs, bringing the right data into the conversation at the right time, and helping shape decisions, not just respond to them. Answering questions is part of the job, but it shouldn’t define the job. The challenge is that most organizations aren’t set up this way today. If we suddenly stopped taking requests, things would break quickly. The real opportunity is in how we evolve: – Establish clear guardrails around what we take on – Prioritize work based on business value and strategic alignment – Shift time away from low-impact requests toward proactive, decision-driven analytics That’s how we move from order-takers to true business partners—and start delivering the kind of impact data teams are actually capable of.

12

1 Comment

Innovation > Supervision. Always. When challenges pile up, too many organizations reach for the same tool: add another manager, schedule another meeting, request another report. But piling on layers of oversight rarely fixes the core issue—it just multiplies complexity. Innovation, not supervision, is what scales impact. 🔹 The printing press didn’t succeed by hiring more scribes. 🔹 Factories didn’t grow by stacking supervisors. 🔹 They advanced through new machines, electricity, and automation. Fast-forward to today: the equivalent leap comes from AI and modern technologies, not thicker chains of command. Disruption can feel uncomfortable. But it’s also what gets the wheels unstuck and drives real progress forward. 👉 The question is: are we investing in tools that transform, or in structures that only constrain? Would love to hear your thoughts—do you see companies still defaulting to control instead of innovation? #AI #Innovation #DigitalTransformation #Leadership #FutureOfWork

10

The best insight I've had recently about data leadership came from catching up on my read-it-later list during a recent family trip to the snow. Nicholas Carr's concept of "ambient overload" perfectly describes what every Head of Data or CDO faces today. The problem isn't finding the one thing worth doing. It's being confronted by hundreds of things that could all be done, all technically feasible, many genuinely useful. The question is no longer "can we build it?" It's "should we?" New essay on why that shift changes everything about the data leader's role. Link in the comments.

22

18 Comments

👉 New blog post! Recently I was asked how to create a modified version of a bar chart in Tableau. My gut was saying that this would be an easy request. My gut was also dead wrong.... I ended up getting the view pulled together but it required a lot of tricks to build. Each on their own isn't too complicated. You should try to work through this visualization. I suspect you'll pick up a couple of tricks as well. Check out the link in the comments. #Tableau #dataviz #tutorial

7

1 Comment

If your dashboards haven’t changed a decision, they’ve already failed. Executives don’t need more charts. They need clarity. Before approving any dashboard, ask: → What decision will this influence? → Who owns that decision? → What action changes if the number moves? If nothing changes, the dashboard is noise — not insight.

14

I've been thinking about something Nate Nichols said on the latest Data Neighbor Podcast episode - dashboards weren't what anyone actually wanted, they were just the best we could do. Nate is VP of Product at Tableau, and he put it pretty plainly: only about 30% of orgs actually use data on a regular basis. The rest? Data sitting in dashboards no one acts on. That's the last mile problem. Data exists, insights exist, action doesn't. What he's seeing change now is agentic analytics. Not just surfacing insights, actually doing something about them. He walked through a sales example that was fascinating. Win rate drops. An AI agent spots it, runs the exploratory analysis, narrows it down - it's concentrated in California. Then it doesn't hand you a report. It tells the AEs: reach out to these accounts, schedule executive business reviews. All of that happening inside a single Slack conversation. Data comes in, action goes out the other end. I think what I liked most was his framing on where humans still fit. He used the Iron Man metaphor - Tony Stark is the hero. His judgment, experience, intuition. The suit just makes him more effective. He also said something I keep thinking about: "AI advances more quickly than you expect, even when you expect AI to advance more quickly than you expect." I definitely see that with the Claude Opus 4.6 tests we've been doing with our AI Analyst System. Give it a watch here: https://lnkd.in/gmk9tJWm

13

2 Comments

Most analytics systems have a blind spot. We'll talk a lot about data quality and model accuracy -- but very little about the layer between forecasts and operations. That layer is where: - units quietly change - constraints are assumed instead of enforced - costs are treated as symmetric when they're not - outputs become "guidance" rather than decisions Nothing crashes. Dashboards still update. Models still score well. But decisions slowly drift out of alignment with reality. More to come.

11

Everyone was watching Amazon. Meanwhile, Walmart Connect quietly built a juggernaut. Their retail media revenue is exploding for one simple reason: They cracked the code on omnichannel. This changes the entire playbook. Legacy retail media was clean but siloed. You knew what happened online. The end. Walmart's new world? ✓ Closed-loop measurement. ✓ Offline, in-store attribution. ✓ First-party data from millions of weekly shoppers. It's no longer about tracking clicks. It's about tracking customers from a mobile ad to the physical checkout aisle. The technical upgrades - faster data refreshes, better audience models - aren't just tweaks. They are fundamental shifts in campaign agility. We're helping our CPG clients plug into this. The scale of Walmart's first-party data is a goldmine, and they just handed everyone a much, much bigger shovel.

12

This is the third of the three-part introduction to season four of the Behavioral Data Science Podcast! During this season, Jacob Sosine and I discuss the intersection of behavior science and data science as they relate to client outcomes in Applied Behavior Analysis service delivery. In this episode, we discuss the importance and challenges of contextualizing outcome measures based on the unique clinical presentation of each client. Happy listening! https://lnkd.in/eXjQtfYA

2