{"id":49678,"date":"2025-01-27T00:04:09","date_gmt":"2025-01-27T00:04:09","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/how-to-summarize-data-using-chatgpt-juice-analytics\/"},"modified":"2025-01-27T00:04:09","modified_gmt":"2025-01-27T00:04:09","slug":"how-to-summarize-data-using-chatgpt-juice-analytics","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=49678","title":{"rendered":"How to Summarize Data using ChatGPT \u2014 Juice Analytics"},"content":{"rendered":"<p> <br \/>\n<br \/><img decoding=\"async\" src=\"https:\/\/images.squarespace-cdn.com\/content\/v1\/66b2cba6e71b42517658a715\/1728389393702-KPOE9PZHAZO5NCIR7VC8\/Summarize+Data+with+ChatGPT+%281%29.png?format=1500w\" \/><\/p>\n<div>\n<blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 2. Change the data field labels to make them more human readable, use proper capitalization, expand out abbreviations, and remove non-alphabet and non-numeric characters.<\/em><\/p>\n<\/blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\">Many data files arrive with column names written by DBAs that are hard to decipher. Take this collection of data fields:<\/p>\n<ul data-rte-list=\"default\">\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>FTResTuition<\/em><\/p>\n<\/li>\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>PTResTuition<\/em><\/p>\n<\/li>\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>FTNonResTuition<\/em><\/p>\n<\/li>\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>PTNonResTuition<\/em><\/p>\n<\/li>\n<\/ul>\n<p class=\"\" style=\"white-space:pre-wrap;\">If you are familiar with the data, these names may be obvious. Fortunately ChatGPT is able to turn those into:<\/p>\n<ul data-rte-list=\"default\">\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\">Full-time Resident Tuition<\/p>\n<\/li>\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\">Part-time Resident Tuition<\/p>\n<\/li>\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\">Full-time Non-Resident Tuition<\/p>\n<\/li>\n<li>\n<p class=\"\" style=\"white-space:pre-wrap;\">Part-time Non-Resident Tuition<\/p>\n<\/li>\n<\/ul>\n<blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 3. Group the data fields by topic or other logical grouping. For each data field, identify if it is a metric, dimension, boolean, or date.<\/em><\/p>\n<\/blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\">Finding similar concepts is another Large Language Model strength. When you are dealing with data tables with dozens of columns, it can be helpful to understand how those data fields fit together. Equally impressive is the ability for ChatGPT to understand different data types.<\/p>\n<blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 4. For each metric data field, show the highest and lowest value in parentheses. For each date field, show the earliest and latest date in parentheses. For each dimension, show the most frequently occurring value in parentheses<\/em><\/p>\n<\/blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\">It can be really helpful to get a sense of your data by seeing the range of values and common values.<\/p>\n<blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 5. Identify any data fields that have many null or empty values. Label these data fields as &#8220;null or empty\u201d. Also, identify any data fields that have all the same value. Describe these data fields as &#8220;uninteresting&#8221;<\/em><\/p>\n<\/blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\">Finally, data tables with lots of columns often have a lot of cruft \u2014 the blank or poorly populated fields that are better to push aside as you thinking about where you want to focus.<\/p>\n<p class=\"\" style=\"white-space:pre-wrap;\">After defining all these steps, I played around with how I wanted it to render the results. I ultimately decided to consolidate steps 2 through 4, and suppress ChatGPT\u2019s inclination to be verbose about the instructions. Here\u2019s the final prompt that I landed on:<\/p>\n<blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>I want you to use the following Data Summarization process on a data set:<\/em><\/p>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 1. Describe what each row in the data set represents<\/em><\/p>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 2. Change the data field labels to make them more human readable, use proper capitalization, expand out abbreviations, and remove non-alphabet and non-numeric characters. Group the data fields by topic or other logical grouping. For each data field, identify if it is a metric, dimension, boolean, or date. For each metric data field, show the highest and lowest value in parentheses. For each date field, show the earliest and latest date in parentheses. For each dimension, show the most frequently occurring value in parentheses<\/em><\/p>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>Step 3. Identify any data fields that have many null or empty values. Label these data fields as &#8220;null or empty\u201d. Also, identify any data fields that have all the same value. Describe these data fields as &#8220;uninteresting&#8221;<\/em><\/p>\n<p class=\"\" style=\"white-space:pre-wrap;\"><em>When you show the results, you can write the Step number but don\u2019t need to include the step description. Are you ready for some data?<\/em><\/p>\n<\/blockquote>\n<p class=\"\" style=\"white-space:pre-wrap;\">After pasting that full prompt into the chat window, I simply copy and pasted a chunk of data from Excel to get a result that looks like this:<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Step 2. Change the data field labels to make them more human readable, use proper capitalization, expand out abbreviations, and remove non-alphabet and non-numeric characters. Many data files arrive with column names written by DBAs that are hard to decipher. Take this collection of data fields: FTResTuition PTResTuition FTNonResTuition PTNonResTuition If you are familiar with [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":49679,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[12765,7432,11603,1993,29531],"dealstore":[],"offerexpiration":[],"class_list":["post-49678","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-analytics","tag-chatgpt","tag-data","tag-juice","tag-summarize"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Summarize Data using ChatGPT \u2014 Juice Analytics - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=49678\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Summarize Data using ChatGPT \u2014 Juice Analytics - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Step 2. 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