{"id":7051,"date":"2024-03-29T14:36:18","date_gmt":"2024-03-29T14:36:18","guid":{"rendered":"https:\/\/tup.kxe.temporary.site\/unlocking-the-potential-of-synthetic-data-a-business-game-changer\/"},"modified":"2024-03-29T14:36:18","modified_gmt":"2024-03-29T14:36:18","slug":"unlocking-the-potential-of-synthetic-data-a-business-game-changer","status":"publish","type":"post","link":"https:\/\/okdesign.ca\/fr\/unlocking-the-potential-of-synthetic-data-a-business-game-changer\/","title":{"rendered":"Unlocking the potential of synthetic data: A business game-changer"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p>The idea of \u201csynthetic data,\u201d or artificially generated information, has recently caused a stir. Data is a huge asset for businesses in this age, and knowledge can often provide a decisive competitive edge. The notion of easily obtaining free data has sparked extravagant claims and controversy.<\/p>\n<p>But, as your mom probably told you \u2014 if something seems too good to be true, it is.<\/p>\n<p>However, the reality is a little more nuanced with synthetic data. While we certainly can\u2019t stop collecting data and \u201cjust ask the model,\u201d some fascinating middle-ground uses of AI-generated data exist. And judicious use of this data can help drive your business forward. In this situation, there\u2019s no free lunch, but there is at least the possibility of a complementary side or two.<\/p>\n<p><!-- \/1038259\/MT_Post-text --><\/p>\n<p>To better understand the opportunities opening up with synthetic data, I will introduce you to three primary modes you can use to generate the new data. These aren\u2019t the only ones available, but are the most common approaches today.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-1-direct-querying-nbsp\">1. Direct querying\u00a0<\/h2>\n<p>The first mode is the one people most commonly associate with the idea of synthetic data \u2014 and that is direct querying. When you first used ChatGPT or one of the other AI chatbots \u2014 there was probably a point when you said to yourself, \u201cWait a second. I can interview this just like I would a research respondent,\u201d and tweak the system prompt (\u201cYou are a Gen Z participant who is passionate about RPGs\u2026\u201d) and proceed with asking the question.<\/p>\n<p>Working with this kind of data can quickly become problematic or un-insightful because training datasets can be old. Responses can be biased or have inappropriate viewpoints that can easily bubble up. Additionally, a large chunk of the training data for these models comes from services like Reddit, which can have spicier takes than you\u2019d want in your own data.<\/p>\n<p>Beyond those red flags, the main issue with this kind of data is that it is boring. By its very nature, it produces plausible answers based on the amalgam of all its training. Therefore, it has a tendency to produce obvious answers \u2014 the very opposite of the kind of insight we are usually looking for. While direct questioning of the LLMs can be interesting, large-scale generation of synthetic data in this way is likely not the best solution.<\/p>\n<p><strong><em>Dig deeper: <a rel=\"nofollow noopener\" href=\"https:\/\/martech.org\/examples-ai-in-marketing\/\" target=\"_blank\">AI in marketing: Examples to help your team today<\/a><\/em><\/strong><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-2-data-augmentation-nbsp\">2. Data augmentation\u00a0<\/h2>\n<p>We can move beyond data querying through the second mode, which is using the models to extract data from data that you bring to them \u2014 often called data augmentation. This method uses the reasoning and summarization power of the LLMs. Still, rather than basing the output solely on the original training data, you leverage models to help analyze your own data to generate perturbation of it as if it were original data.<\/p>\n<p>The process looks something like this. First, you must know the data you are bringing to the table. Perhaps it\u2019s data sourced from an internal system, primary research, a trusted third-party supplier or from segmentation or appended desirable behaviors. After understanding the source of your data, you can then use the LLM to analyze and provide more data with compatible characteristics.<\/p>\n<p>This approach is far more promising and provides you with control you cannot get from the LLMs on their own.<\/p>\n<p>Many in the martech industry might be thinking, \u201cLike look-alikes?\u201d and you would be correct. The new models allow us to generate look-alikes in a way that we have never been able to do before. This allows augmenting or generating data that stays consistent and comparable with the known data we already have.<\/p>\n<p>Often, having a volume of data like this is helpful when testing systems or exploring some of the fringes a system might need to handle. It could also be used to provide truly anonymous data for demonstrations or presentations. Avoid the circular thinking of \u201cLet\u2019s generate a ton of data and analyze it,\u201d when you are better off simply analyzing the root data.\u00a0<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-3-data-retraining-nbsp\">3. Data retraining<strong>\u00a0<\/strong><\/h2>\n<p>Finally, the third mode of generating synthetic data is retaining a model to represent the data we have directly. The \u201choly grail\u201d approach of taking a model and doing custom fine-tuning on a data set has been around for a long time but, until recently, has simply taken too many resources and been far too expensive to be a reasonable option for most.\u00a0<\/p>\n<p>But technologies change. The prevalence of smaller but high-performance models (i.e., LLaMA, Orca and Mistral) together with recent revolutionary approaches to fine-tuning (i.e., Parameter Efficient Fine Tuning, or PEFT, and the LoRa, QLoRa and DoRa sisters) means that we can effectively and efficiently produce highly customized models trained on our data. These are likely to be the techniques that truly make synthetic data shine \u2014 for the near future at least.<\/p>\n<p>While there is no free lunch, and the dangers of bias, boredom and circular thinking are very real \u2014 the opportunities of synthetic data make it highly compelling. And when leveraged correctly, it can create efficiencies and exponential possibilities.\u00a0<\/p>\n<p><strong><em>Dig deeper: <a rel=\"nofollow noopener\" href=\"https:\/\/martech.org\/how-to-make-sure-your-data-is-ai-ready\/\" target=\"_blank\">How to make sure your data is AI-ready<\/a><\/em><\/strong><\/p>\n<p><!-- START INLINE FORM --><\/p>\n<div class=\"nl-inline-form border py-2 px-1 my-2\">\n<div class=\"row align-items-center justify-content-center nl-inline-container\">\n<div class=\"col-12 col-lg-3 col-xl-auto pb-2 pb-lg-0\">\n<p class=\"inline-form-text text-center mb-0\">Get MarTech! Daily. Free. In your inbox.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<p><!-- END INLINE FORM -->\n<\/div>\n<p><em>Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed <a rel=\"nofollow\" href=\"https:\/\/martech.org\/staff\/\">here<\/a>.<\/em><\/p>\n<p><br \/>\n<br \/><a href=\"https:\/\/martech.org\/unlocking-the-potential-of-synthetic-data-a-business-game-changer\/\">Source link <\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>The idea of \u201csynthetic data,\u201d or artificially generated information, has recently caused a stir. Data is a huge asset for businesses in this age, and knowledge can often provide a decisive competitive edge. The notion of easily obtaining free data has sparked extravagant claims and controversy. But, as your mom probably told you \u2014 if [&hellip;]<\/p>","protected":false},"author":3,"featured_media":7052,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5],"tags":[],"class_list":["post-7051","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agency"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.8 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Unlocking the potential of synthetic data: A business game-changer - OK Design<\/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:\/\/okdesign.ca\/fr\/unlocking-the-potential-of-synthetic-data-a-business-game-changer\/\" \/>\n<meta property=\"og:locale\" content=\"fr_CA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Unlocking the potential of synthetic data: A business game-changer\" \/>\n<meta property=\"og:description\" content=\"The idea of \u201csynthetic data,\u201d or artificially generated information, has recently caused a stir. 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