Should we work together?
Here's who I like to work with.
Because chocolate 🍫 and steak 🥩 are both delightful, but not together.
On paper, you might be awesome at your thing, and maybe I am awesome at my thing. However, it doesn't mean I'm right for you, and vice versa. It's the chocolate steak problem: separately, they're great, but not so good mixed together (IMHO!) If you got this far, you may be thinking about working with me, or you're just curious what types of clients I work with. No need to guess! Here's what my best-fit clients usually look like:
Traits of my favorite clients:
- You know a lot about the world of data...but you also know that your customers, users, and stakeholders may look at your world differently and that you need to help connect those dots
- You've probably got great technical/data science/analytics/engineering talent, but are beginning to sense that "technically right, effectively wrong" solutions aren't cutting it. You're realizing the "human in the loop" part has to be useful, valuable, easy to use, and trustworthy.
- You are responsible to deliver business value with data, not just ML models, dashboards, apps and technology. In other words, you have to—or want to start to—deliver outcomes, not just outputs.
- You get frustrated when customers don't use the solutions you or your team made—and you think better design might help close the gap.
- You're ready to be candid about where you are, and where you want to go—so I can do my best work to help get you there.
- You know you or your team need help to better surface the unarticulated problems of your users/customers—so you don't have to guess what to build.
- You see working with me as a collaboration where value is exchanged in both directions.
- You believe design matters—or you're open to see how it can work for you—even if it means changing how you or your team have always done things.
- You're ready to make changes to your product, team, org, or self. Whatever is necessary to move forward.
- ML and AI is not a fad for you, but expectations around it are high. You want to deliver.
Your job title might be:
You can scan my Client Testimonials and get an idea who I have worked with, but in short, most of you will be:
- Product, data science, UX or engineering leader @ software and SAAS companies
Directors/VP/SVP/CxOs responsible for delivering business value
You likely have product, engineering, UX, or data science in your title, but the main thread that connects you all is that you're responsible for ensuring that your data product or service delivers values to your customers. You probably manage a team and want to make sure you're building the right product. Most of the time, you'll probably find my Design Blueprint or UI/UX Audit to be the most relevant starting point for working with me.
- Internal data product, data science, ML/AI, analytics, or DT leader @ a large enterprise:
Directors, VP/SVP/Heads, GMs, CDO/CAOs serving internal and/or external users
You are tasked with delivering "value" with data, not just models, dashboards, apps, and reports. You may have little experience with "product" or "design," but you're getting the sense that having your team simply building data outputs and widgets isn't cutting it. You're looking for ways to improve your team's chops on the "human side"—from problem discovery with internal customers, to making data products that are simple, useful, and valuable. Usually, you're interested in private training Seminar, and most of you work at traditional companies where software isn't your main business. While you're not a "digital native," your executive leaders are looking to you to deliver on AI/ML in particular—even if some of them don't really "get it" yet or understand what's possible with AI.
- Leader and partners @ ML/AI/analytics/engineering consultancies
You're similar to the folks above, except you work at data science, ML/AI, or analytics consultancy and you're trying to make your clients happier— the first time they see your work. You're probably mainly looking at my Video Course or Seminar, but may need consultative help.