{"id":55633,"date":"2024-09-11T11:00:54","date_gmt":"2024-09-11T15:00:54","guid":{"rendered":"https:\/\/sdtimes.com\/?p=55633"},"modified":"2024-10-04T11:04:00","modified_gmt":"2024-10-04T15:04:00","slug":"three-considerations-to-assess-your-datas-readiness-for-ai","status":"publish","type":"post","link":"https:\/\/sdtimes.com\/ai\/three-considerations-to-assess-your-datas-readiness-for-ai\/","title":{"rendered":"Three considerations to assess your data\u2019s readiness for AI"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Organizations are getting caught up in the hype cycle of AI and generative AI, but in so many cases, they don\u2019t have the data foundation needed to execute AI projects. <\/span><a href=\"https:\/\/info.syniti.com\/hfs-report?_gl=1*1t918h9*_gcl_au*MjU1NTMzNDI4LjE3MTYzMzA4OTM.*_ga*MTc5Mzg2MTQ3NC4xNzE2MzMwODkw*_ga_D6ESBR87G0*MTcxODQwNjMxOS43LjEuMTcxODQwNjMxOS42MC4wLjUxOTMyNTQ5Ng..\"><span style=\"font-weight: 400;\">A third of executives<\/span><\/a><span style=\"font-weight: 400;\"> think that less than 50% of <\/span><span style=\"font-weight: 400;\">their organization\u2019s data is consumable, emphasizing the fact that many organizations aren\u2019t prepared for AI.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For this reason, it\u2019s critical to lay the right groundwork before embarking on an AI initiative. As you assess your readiness, here are the primary considerations:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Availability: Where is your data?\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Catalog: How will you document and harmonize your data?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Quality: Having good quality data is key to the success of your AI initiatives.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">AI underscores the garbage in, garbage out problem: if you input data into the AI model that\u2019s poor-quality, inaccurate or irrelevant, your output will be, too. These projects are far too involved and expensive, and the stakes are too high, to start off on the wrong data foot.<\/span><\/p>\n<h5><b>The importance of data for AI<\/b><\/h5>\n<p><span style=\"font-weight: 400;\">Data is AI\u2019s stock-in-trade; it is trained on data and then processes data for a designed purpose. When you\u2019re planning to use AI to help solve a problem \u2013 even when using an existing large language model, such as a generative AI tool like ChatGPT \u00a0 \u2013 you\u2019ll need to feed it the right context for your business (i.e. good data,) to tailor the answers for your business context (e.g. for <\/span><span style=\"font-weight: 400;\">retrieval-augmented generation)<\/span><span style=\"font-weight: 400;\">. It\u2019s not simply a matter of dumping data into a model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">And if you\u2019re building a new model, you have to know what data you\u2019ll use to train it and validate it. That data needs to be separated out so you can train it against a dataset and then validate against a different dataset and determine if it\u2019s working.<\/span><\/p>\n<h5><b>Challenges to establishing the right data foundation<\/b><\/h5>\n<p><span style=\"font-weight: 400;\">For many companies, knowing where their data is and the availability of that data is the first big challenge. If you already have some level of understanding of your data \u2013 what data exists, what systems it exists in, what the rules are for that data and so on \u2013 that\u2019s a good starting point. The fact is, though, that many companies don\u2019t have this level of understanding.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data isn\u2019t always readily available; it may be residing in many systems and silos. Large companies in particular tend to have very complicated data landscapes. They don\u2019t have a single, curated database where everything that the model needs is nicely organized in rows and columns where they can just retrieve it and use it.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Another challenge is that the data is not just in many different systems but in many different formats. There are SQL databases, NoSQL databases, graph databases, data lakes, sometimes data can only be accessed via proprietary application APIs. There\u2019s structured data, and there\u2019s unstructured data. There\u2019s some data sitting in files, and maybe some is coming from your factories\u2019 sensors in real time, and so on. Depending on what industry you\u2019re in, your data can come from a plethora of different systems and formats. Harmonizing that data is difficult; most organizations don\u2019t have the tools or systems to do that.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Even if you can find your data and put it into one common format (canonical model) that the business understands, now you have to think about data quality. Data is messy; it may look fine from a distance, but when you take a closer look, this data has errors and duplications because you&#8217;re getting it from multiple systems and inconsistencies are inevitable. You can\u2019t feed the AI with training data that is of low quality and expect high-quality results.\u00a0<\/span><\/p>\n<h5><b>How to lay the right foundation: <\/b><b>Three steps to success<\/b><\/h5>\n<p><span style=\"font-weight: 400;\">The first brick of the AI project\u2019s foundation is <\/span><span style=\"font-weight: 400;\">understanding your data. You must have the ability to articulate what data your business is capturing, what systems it\u2019s living in, how it\u2019s physically implemented versus the business\u2019s logical definition of it, what the business rules for it are..<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Next, you must be able to evaluate your data. T<\/span><span style=\"font-weight: 400;\">hat comes down to asking, \u201cWhat does good data for my business mean?\u201d <\/span><span style=\"font-weight: 400;\">You need a definition for what good quality looks like, and you need rules in place for validating and cleansing it, and a strategy for maintaining the quality over its lifecycle.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you\u2019re able to get the data in a canonical model from heterogeneous systems and you wrangle with it to improve the quality, you still have to address scalability. This is the third foundational step. Many models require a lot of data to train them; you also need lots of data for retrieval-augmented generation, which is a technique for enhancing generative AI models using information obtained from external sources that weren\u2019t included in training the model.\u00a0 And all of this data is continuously changing and evolving.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You need a methodology for how to create the right data pipeline that scales to handle the load and volume of the data you might feed into it. <\/span><span style=\"font-weight: 400;\">Initially, you&#8217;re so bogged down by figuring out where to get the data from, how to clean it and so on that you might not have fully thought through how challenging it will be when you try to scale it with continuously evolving data. So, you have to consider what platform you\u2019re using to build this project so that that platform is able to then scale up to the volume of data that you&#8217;ll bring into it.<\/span><\/p>\n<h5><b>Creating the environment for trustworthy data<\/b><\/h5>\n<p><span style=\"font-weight: 400;\">When working on an AI project, treating data as an afterthought is a sure recipe for poor business outcomes.\u202f<\/span><span style=\"font-weight: 400;\">Anyone who is serious about building and sustaining a business edge by developing and using\u202f AI\u202fmust start with the data first. <\/span><span style=\"font-weight: 400;\">The complexity and the challenge of cataloging and readying the data to be used for business purposes is a huge concern, especially because time is of the essence. That\u2019s why you don\u2019t have time to do it wrong; a<\/span><span style=\"font-weight: 400;\"> platform and methodology that help you maintain high-quality data is foundational.\u202fUnderstand and evaluate your data, then plan for scalability, and you will be on your way to better business outcomes.<\/span><\/p>\n<!-- AddThis Advanced Settings generic via filter on the_content --><!-- AddThis Share Buttons generic via filter on the_content -->","protected":false},"excerpt":{"rendered":"<p>Organizations are getting caught up in the hype cycle of AI and generative AI, but in so many cases, they don\u2019t have the data foundation needed to execute AI projects. A third of executives think that less than 50% of their organization\u2019s data is consumable, emphasizing the fact that many organizations aren\u2019t prepared for AI.\u00a0  &hellip; <a class=\"read-more\" href=\"https:\/\/sdtimes.com\/ai\/three-considerations-to-assess-your-datas-readiness-for-ai\/\">continue reading<\/a><!-- AddThis Advanced Settings generic via filter on get_the_excerpt --><!-- AddThis Share Buttons generic via filter on get_the_excerpt --><\/p>\n","protected":false},"author":1242,"featured_media":55635,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"cybocfi_hide_featured_image":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[143,2850],"coauthors":[17054],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Three considerations to assess your data\u2019s readiness for AI - 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