Is there a faster way than MVPs to create data products that actually get used, are simple, and are trustworthy?
Satisfying internal vs. external customers is not the same. What do data science, analytics, and engineering leaders need to know about the messy world of birthing a new commercial data-driven product?
As a designer, I used to say “user-centered”-a lot. It’s terminology we now hear from non-designers now, people like many of you. That’s a good thing. But, I want you and your teams to think bigger. For me, that “user-centered” descriptor is missing something about design, and particularly so if you are working on ML/AI systems. … Read more
If you’re struggling to solve human problems with data, the mindset of your analytics org may be the problem.
Ok, let’s dive into another reader question. This time from Loris via LinkedIn originally: Cheers, Loris My take? Just buy Snowflake – it will fix everything. Just kidding. Actually, one thing I think I’m hearing more from leaders (which is GREAT) is that it’s not the technology that’s the problem. “The technology part is easy.” … Read more
Is it time to stop using dashboards in analytics solutions and data product design?
Are you a leader in charge of creating innovative ML and analytics solutions within a very large enterprise organization? Getting the “makers” of the solutions talking to real end-users can be extremely difficult. Here’s how to navigate the gatekeepers and bureaucracy so that the data products you spend so much time and money building actually are useful, usable, and valuable.
I’m not putting out a long list of 2021 predictions, but I have a couple that I will mention to you that are on my radar. First, AI/Data Product Management Seems to be Picking Up There seem to be more jobs appearing in product management in the AI/ML space, in particular. I am not sure why we don’t … Read more
Data science, analytics, and engineering are in-demand skills, however, when building customer-facing applications and data-driven products, organizations rely on innovation to unlock the power of this data. How can analytical minds practice creativity that leads to innovative solutions?
Today, I’m sharing my impressions of one of Spotify’s analytics touchpoints—a monthly email I receive with a boatload of design choices I mostly hope you will not copy, especially if you’re working in an enterprise capacity. Most of you by now probably know I have another career as a professional musician, and that includes having three recordings I … Read more
Presenting data and evidence isn’t the same thing as providing indispensable decision support, especially when your insights are experienced in a software application with no Powerpoint deck, narrator, or intimate storytelling.
This is an ongoing list of links to articles, slide decks, toolkits, and other resources around designing AI user experiences. I will keep this updated. 7 Steps to AI Products – Allie K. Miller (slide deck) UX in the Age of AI: Where Does Design Fit In? – Carol Smith (slide deck) AIMeets.design PDF tool kit (Nadia Piet) … Read more
Customers want simple, well-designed decision support tools and UX’s that are actionable. Businesses want to see value from data and adoption of data-driven decision making. However, the UX that is afforded to is often simply a byproduct of the analytics team’s engineering, or, at best, “data viz” efforts—and it’s not working. A decade later, success rates for data projects remain unchanged, despite vendor/BI tooling improvements. What are BI/analytics teams still missing? Design.
In many cases, machine learning needs to be deployed to augment human decision making, not automate it. What are you doing to account for this dependency on the success of your data product?
Self-reflecting on #BLM, the makeup of my podcast guests to date, racism, and the responsibilities of consultants with platforms and audiences in fighting injustice.
I performed a rapid UI/UX and data visualization audit on the MITRE Covid-19 Healthcare Coalition Decision Support Dashboard. Watch it here, and see my recommended design changes the team should make.
How to avoid spending 6-8 months on a technically right, effectively wrong model. Are you successful with ML if nobody uses your solution, model, or application?
Covid-19 presents a major disruption to our lives and businesses. However, sanitizing your hands isn’t the only thing you data leaders need to be considering. Your data product, dashboards, or UI may also need to be cleaned up. No hard-to-find Clorox wipes needed; just some good design thinking centered around your customers.
My conversations and research suggest that individual contributor UX designers and data scientists share one thing in common: it’s often a challenge to “get to do the job I was hired for.” People, process, and political roadblocks at every turn—so what can you do about it if this is you? And if you’re managing these people, what are you doing to ensure these people don’t leave?