{"id":18097,"date":"2023-08-17T08:14:29","date_gmt":"2023-08-17T08:14:29","guid":{"rendered":"https:\/\/uxmag.com\/?p=18097"},"modified":"2023-08-17T08:14:31","modified_gmt":"2023-08-17T08:14:31","slug":"data-science-effectiveness-as-a-ux-problem","status":"publish","type":"post","link":"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem","title":{"rendered":"Data Science Effectiveness as a UX Problem"},"content":{"rendered":"\n

Data scientists are users too.<\/p>\n\n\n\n

There are many instances<\/a> where it feels like someone attempted to make a data science tool for data scientists<\/em> without ever having met a live one. If you take that<\/em> product development approach, you remind me of bros trying to break into the tampon market without ever consulting a woman. What could possibly go wrong\u2026?<\/p>\n\n\n\n

If you\u2019re a toolmaker who\u2019s never heard of user experience (UX) design, I\u2019m happy to welcome you to the 21st century. Stop reading this and trundle over to Wikipedia<\/a>, you\u2019re in for a treat! So much has happened while you were sleeping.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u201cIt\u2019s important to understand how the end user uses the product!\u201d (I found this image here<\/a>.)<\/em><\/p>\n\n\n\n

Personas<\/h3>\n\n\n\n

UX101 mentions user personas right out of the gate. These will be hard to generate if you\u2019ve never met all the real-world versions of people from this list<\/a>. To design nice things for us, you need to take the time to build that empathy.<\/p>\n\n\n\n

I\u2019m sorry it\u2019s hard to wrap your heads around us, but we\u2019re not the typical software engineer. For starters, if you look at the list<\/a>, you\u2019ll notice that we come in different flavors. Surprise! There are different kinds of data science professionals. Which one are you designing for? Have you taken the time to understand why<\/a> an analyst doesn\u2019t care if a tool is production-worthy but an ML engineer does? (That puzzle piece will come in handy if you\u2019re confused by the R vs Python fuss.) Do you know why statisticians<\/a> might flip a table if you tricked them into using a tool optimized for speedy analytics<\/a>? If not, those are two great places to start your detective work.<\/p>\n\n\n\n

What good design looks like<\/h3>\n\n\n\n

A collaboration that I\u2019m proud to be part of is the What-If Tool<\/a>, as in \u201c<\/em>What if<\/em><\/strong> getting a look at your model performance and data during ML\/AI development wasn\u2019t such a royal pain in the butt?\u201d<\/em> Being able to get a grip on your progress is the key to speedy iteration towards an awesome ML\/AI<\/a> solution, so good tools designed for analysts<\/a> working in the machine learning<\/a> space help them help you meet ambitious targets<\/a> and catch problems like AI bias<\/a> before it hurts your users.<\/p>\n\n\n\n

Something I love about the What-If Tool\u2019s approach is that data science UX was not an afterthought \u2014 the project included a visual designer and UX engineer from the start. The first version (released in late 2018) was designed for analysts supporting teams committed to TensorFlow<\/a> development. We knew TensorFlow\u2019s grumpy opacity<\/a> would frustrate analytics enthusiasts, so we started there.<\/p>\n\n\n\n

We gradually expanded our target user group to any ML\/AI analyst working with models in Python<\/a>, culminating in What-If Tool v1.0<\/a> announced at TensorFlow Dev Summit 2019<\/a> earlier this month, along with groundbreaking news about TensorFlow\u2019s stronger overall commitment to user experience, which I\u2019ll cover in a separate post very soon.<\/p>\n\n\n\n

That\u2019s right: model understanding and quick data exploration for feature selection\/preprocessing insights even if you\u2019re allergic to TensorFlow. <\/em>Complete with handy AI bias<\/a> detection because that\u2019s often an ML\/AI analyst\u2019s first question. Does it work with Jupyter<\/a> notebooks? You bet! Built-in Facets<\/a>? Sure thing!<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Take the What-If Tool for a test drive here<\/a>.<\/em><\/p>\n\n\n\n

We knew we wanted this to be a great tool for ML\/AI analysts, so we observed real analysts<\/a> using the tool in their natural habitats and<\/em> in usability workshops. We incorporated their screams of frustration to drive better design until the sobbing subsided and the scowls turned into smiles (mostly \u2014 it\u2019s not perfect yet, but we\u2019re working on it). This tool isn\u2019t some accidental roadkill that we\u2019re foisting on the unsuspecting data scientist<\/a>. We made it for you<\/em> and we hope you\u2019ll like it. (And please do give us feedback on the site so we can keep making it better.)<\/p>\n\n\n\n

We\u2019re also aware of who\u2019s not<\/em><\/strong> in our intended user group. Statisticians<\/a> won\u2019t be fans unless they moonlight as analysts<\/a>. Researchers<\/a> have probably already cobbled together their own niche version. Complete beginners might be better off learning the basics elsewhere first.<\/p>\n\n\n\n

Whatever else you might say about the What-If Tool<\/a>, the part I\u2019m most proud of is that we took UX design seriously and put the effort to understand our data science users. (We even know why you\u2019re annoyed that we had to compromise for the sake of our other target user group and keep the TensorFlow<\/a> legacy lingo that makes traditional data scientists want to punch something. Yeah, that \u201cinference\u201d isn\u2019t inference<\/em><\/a>. We feel you.)<\/p>\n\n\n\n

If you\u2019re eager to see the What-If Tool<\/a> in action, you don\u2019t have to install anything \u2014 just go here<\/a>. We\u2019ve got dazzling demos and docs aplenty. If you want to start using it for realsies, you don\u2019t even need to install TensorFlow<\/a>. Simply pip install with widget<\/em>.<\/p>\n\n\n\n

What\u2019s my point?<\/strong><\/h3>\n\n\n\n

The moral of the story is that if you want happy data scientists<\/a>, you have to understand us. It\u2019s sad to see how rarely non-data-scientists take the time. If you\u2019re one of us, check that whoever you\u2019re about to trust with your career understands you and your needs. Ask potential employers pointed questions<\/a> about data, decision-makers, and tools. If you\u2019re hiring us, make sure you have what we need to be happy and effective. If you\u2019re designing tools for us, learn who we are and how we think.<\/p>\n\n\n\n

\n

If you want happy data scientists<\/a>, you have to understand us.<\/p>\n<\/blockquote>\n\n\n\n

Sure, that\u2019s hard work if you\u2019ve spent your life avoiding us because someone yelled at you for confusing correlation with causation once upon a time\u2026 The question is: are we worth the effort?<\/p>\n\n\n\n

Are data scientists worth it?<\/strong><\/h3>\n\n\n\n

If you\u2019re a product manager, engineer, or user experience designer thinking about just sitting this whole thing out and not bothering to get to know what makes your data scientist comrades tick, you\u2019re taking a bet on data science being a bubble<\/a> and AI being a fad<\/a>. It\u2019s not a bet I would recommend, because though the names may evolve, data science and AI are fundamentally about making data useful and I can\u2019t imagine your having less data in the future than today.<\/p>\n\n\n\n

I\u2019ve argued often that information is valuable<\/a>, as has anyone who has ever said, \u201cKnowledge is power.\u201d Investing in professionals whose skills are geared at helping you make the most of information is a great way to get or keep your edge in the market. Whatever you call the professionals who make your data useful for you, that role is only going to become more prevalent in your industry. You\u2019re going to have to understand who we are sooner or later. You may as well get the early bird special and investigate us while your colleagues are still snoozing. Your empathy and ability to collaborate with us will be an incredible advantage in your careers too.<\/p>\n\n\n\n

I believe we are<\/em> worth your time. Making data useful<\/a> is the future and that\u2019s what we do for you. Let\u2019s be friends!<\/p>\n\n\n\n

<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Data scientists are users too. There are many instances where it feels like someone attempted to make a data science tool for data scientists without ever having met a live one. If you take that product development approach, you remind me of bros trying to break into the tampon market without ever consulting a woman. What could possibly go wrong\u2026? If<\/p>\n","protected":false},"author":2621,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[1],"tags":[],"topics":[26,82,146,120,122,2910],"class_list":{"0":"post-18097","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-uncategorized","7":"topics-data-visualization","8":"topics-personas","9":"topics-things-ux-people-like","10":"topics-ux-business-news","11":"topics-ux-magazine","12":"topics-ux-world-changing-ideas","13":"entry"},"yoast_head":"\nData Science Effectiveness as a UX Problem - UX Magazine<\/title>\n<meta name=\"description\" content=\"We\u00a0data scientists\u00a0spend so much of our effort helping you understand your users that\u2026\u00a0you forget that\u00a0we are users too.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Science Effectiveness as a UX Problem\" \/>\n<meta property=\"og:description\" content=\"We\u00a0data scientists\u00a0spend so much of our effort helping you understand your users that\u2026\u00a0you forget that\u00a0we are users too.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem\" \/>\n<meta property=\"og:site_name\" content=\"UX Magazine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uxmag\" \/>\n<meta property=\"article:published_time\" content=\"2023-08-17T08:14:29+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-08-17T08:14:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/uxmag.com\/wp-content\/uploads\/2023\/08\/0D7G47hQq4-vzO0ls.jpg\" \/>\n<meta name=\"author\" content=\"Cassie Kozyrkov\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@uxmag\" \/>\n<meta name=\"twitter:site\" content=\"@uxmag\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Cassie Kozyrkov\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem#article\",\"isPartOf\":{\"@id\":\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem\"},\"author\":{\"name\":\"Cassie Kozyrkov\",\"@id\":\"https:\/\/uxmag.com\/#\/schema\/person\/4074c4969bfe38be30422a5d7053da2f\"},\"headline\":\"Data Science Effectiveness as a UX Problem\",\"datePublished\":\"2023-08-17T08:14:29+00:00\",\"dateModified\":\"2023-08-17T08:14:31+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem\"},\"wordCount\":1307,\"publisher\":{\"@id\":\"https:\/\/uxmag.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem#primaryimage\"},\"thumbnailUrl\":\"https:\/\/uxmag.com\/wp-content\/uploads\/2023\/08\/0D7G47hQq4-vzO0ls.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem\",\"url\":\"https:\/\/uxmag.com\/articles\/data-science-effectiveness-as-a-ux-problem\",\"name\":\"Data Science Effectiveness as a UX Problem - 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