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src="//images.ctfassets.net/9ncizv60xc5y/whS0tFuIZKiGZ1ME0Pyj9/30dbc27c21581c9f2427ac9745617e5d/roundtable3-1.jpg" alt="" class="w-full rounded-lg mt-0 mb-6"/><h3 class="__className_4bc053 text-4xl font-bold my-2">Snowflake Summit 2024 After-action Report and Workshopping: Biz x Dev Roundtable</h3><div class="flex items-center gap-3"></div><div class="flex items-center justify-between"><div class="flex items-center space-x-2 text-muted-foreground"><span>2024-06-20</span></div><div class="flex gap-4 items-center flex-wrap my-2"><button class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="40" height="40"><circle cx="32" cy="32" r="32" fill="#00aced"></circle><path d="M48,22.1c-1.2,0.5-2.4,0.9-3.8,1c1.4-0.8,2.4-2.1,2.9-3.6c-1.3,0.8-2.7,1.3-4.2,1.6 C41.7,19.8,40,19,38.2,19c-3.6,0-6.6,2.9-6.6,6.6c0,0.5,0.1,1,0.2,1.5c-5.5-0.3-10.3-2.9-13.5-6.9c-0.6,1-0.9,2.1-0.9,3.3 c0,2.3,1.2,4.3,2.9,5.5c-1.1,0-2.1-0.3-3-0.8c0,0,0,0.1,0,0.1c0,3.2,2.3,5.8,5.3,6.4c-0.6,0.1-1.1,0.2-1.7,0.2c-0.4,0-0.8,0-1.2-0.1 c0.8,2.6,3.3,4.5,6.1,4.6c-2.2,1.8-5.1,2.8-8.2,2.8c-0.5,0-1.1,0-1.6-0.1c2.9,1.9,6.4,2.9,10.1,2.9c12.1,0,18.7-10,18.7-18.7 c0-0.3,0-0.6,0-0.8C46,24.5,47.1,23.4,48,22.1z" fill="white"></path></svg></button><button class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="40" height="40"><circle cx="32" cy="32" r="32" fill="#0077B5"></circle><path d="M20.4,44h5.4V26.6h-5.4V44z M23.1,18c-1.7,0-3.1,1.4-3.1,3.1c0,1.7,1.4,3.1,3.1,3.1 c1.7,0,3.1-1.4,3.1-3.1C26.2,19.4,24.8,18,23.1,18z M39.5,26.2c-2.6,0-4.4,1.4-5.1,2.8h-0.1v-2.4h-5.2V44h5.4v-8.6 c0-2.3,0.4-4.5,3.2-4.5c2.8,0,2.8,2.6,2.8,4.6V44H46v-9.5C46,29.8,45,26.2,39.5,26.2z" fill="white"></path></svg></button><button class="react-share__ShareButton" style="background-color:transparent;border:none;padding:0;font:inherit;color:inherit;cursor:pointer"><svg viewBox="0 0 64 64" width="40" height="40"><circle cx="32" cy="32" r="32" fill="#0965FE"></circle><path d="M34.1,47V33.3h4.6l0.7-5.3h-5.3v-3.4c0-1.5,0.4-2.6,2.6-2.6l2.8,0v-4.8c-0.5-0.1-2.2-0.2-4.1-0.2 c-4.1,0-6.9,2.5-6.9,7V28H24v5.3h4.6V47H34.1z" fill="white"></path></svg></button></div></div><div class="flex flex-row items-center space-x-4"><img src="//images.ctfassets.net/9ncizv60xc5y/5smgawyx0Jp59cqCSTG1xZ/2744fb5128d87b873d7ed55c4e4d9b68/onodera.jpg" class="w-16 h-16 rounded-full my-0" width="260" height="260"/><div><div class="text-lg font-bold">Hayato Onodera</div><div class="text-base">Sales Manager</div></div></div><div class="prose dark:prose-invert w-full max-w-full my-8"><div class="morph-page w-full max-w-full"><p>The Biz x Dev Roundtable series will provide solutions to data-related problems and trending topics in the form of a conversation between software engineers and business professionals.</p> <h2>Today's Attendees.</h2> <h3>Hayato Onodera - Sales Manager @Morph</h3> <p>Manager of Corporate Sales at Morph.</p> <h3>Naoto Shibata - CEO/Backend Engineer @Morph</h3> <p>He is our CEO, but also the lead backend engineer at Morph.</p> <hr/> <p><strong>Onodera:</strong></p> <p>The Snowflake Summit 2024 was recently held, and there were many announcements related to AI.</p> <p>Today, I'd like to discuss how to actually use Snowflake's Cortex in our work.</p> <p>Was there anything from the Snowflake Summit 2024 that caught your attention?</p> <p><strong>Shibata:</strong></p> <p>First of all, I’m happy about dark mode!</p> <p>And of course, there were many AI-related announcements, and they strongly emphasized the concept of the "AI Data Cloud."</p> <p><img src="//images.ctfassets.net/9ncizv60xc5y/2tGeXXLOm6Ap6Z26XVDz0o/998f00c38cc578c58425e5fed143f596/roundtable3-2.jpg" alt="roundtable3-2"/></p> <p><strong>Onodera:</strong></p> <p>I had used the Notebook before, so I found the announcements easy to understand, but there were many features that made me think, "We can do this now."</p> <p><strong>Shibata:</strong></p> <p>It's great that we can use AI features without having to build a local environment. We can do a lot on Snowflake.</p> <p>Regarding the Notebook, it’s nice that we can execute Streamlit commands, allowing us to do everything from AI processing to simple applications with one tool.</p> <p><strong>Onodera:</strong></p> <p>Seeing those announcements inspired me, and today I'd like to discuss while actually using Cortex.</p> <h2>Cleansing Sales Data Using LLMs</h2> <p><strong>Onodera:</strong></p> <p>I’ve prepared some E-commerce data. Looking at the product information, it mixes physical products and fan club payments.</p> <p>This is problematic for analysis, so I used LLM to classify the categories.</p> <p>Here is the actual code.</p> <p><img src="//images.ctfassets.net/9ncizv60xc5y/68pwOzAQaOPtUg4wLkcGNe/d6b220d48f1c0fb8e79893bbf2689dff/roundtable3-3.jpg" alt="roundtable3-3"/></p> <p><strong>Shibata:</strong></p> <p>You can call LLM within the SELECT statement.</p> <p><strong>Onodera:</strong></p> <p>That's right. The results were good, and I think it can be used in actual customer projects.</p> <p><strong>Shibata:</strong></p> <p>Previously, we would have to query with SQL, call an API like ChatGPT from Python to process it, and then return it to Snowflake, so it simplifies things a lot.</p> <p><strong>Onodera:</strong></p> <p>As someone from the business side, I’m familiar with SQL, so it’s easy to handle.</p> <p>But I wanted to ask, even with such tools, are there still cases where you need to use Python?</p> <p><strong>Shibata:</strong></p> <p>With this much functionality, many cases are covered.</p> <p>However, the advantage of using Python is that you can use the latest models immediately. With an integrated tool solution, you have to wait until the tool, in this case Snowflake, supports it. Using Python to directly access the API gives you more flexibility.</p> <p>Also, in practice, tasks are rarely completed with just LLM processing, so there is still strength in writing code.</p> <p>I’m curious how much Cortex can handle commands like <a href="https://platform.openai.com/docs/guides/function-calling">Function Calling</a>, which restricts the output format of LLM.</p> <p>In this case, we’re only trying it with data output from the EC site, but there might be situations where you want to combine it with other data stored in Excel, for example.</p> <p><strong>Onodera:</strong></p> <p>That's true. In actual projects, it seems like Python processing will also be necessary.</p> <p><img src="//images.ctfassets.net/9ncizv60xc5y/64jBZ3wbS1CazM68rE5lpl/13ba8ed64f96d5e1f7f6c2c1d08ebde6/roundtable3-4.jpg" alt="roundtable3-4"/></p> <h2>Scoring Salesforce Prospective Customers</h2> <p><strong>Onodera:</strong></p> <p>Another dataset I brought is Salesforce prospective customer data. Scoring prospective customers is still heavily reliant on qualitative information, so I want to label it more quantitatively.</p> <p>There are many methods for customer scoring that focus on quantitative data, but it’s another matter to build it myself. I thought it might be easy to do with Cortex’s features, so I tried it out.</p> <p>What I did was build a model from the data of customers who actually purchased in the past and then use it to score prospective customers.</p> <p><strong>Shibata:</strong></p> <p>You can do this with SQL too.</p> <p><strong>Shibata:</strong></p> <p>It’s sufficient at least as a prototype. You can improve it while testing with actual data.</p> <p><strong>Onodera:</strong></p> <p>It only took about five minutes to build.</p> <p>With <a href="https://www.snowflake.com/blog/bi-directional-data-sharing-snowflake-salesforce-ga/">the enhanced integration between Salesforce and Snowflake</a>, I feel the possibilities have expanded.</p> <p><img src="//images.ctfassets.net/9ncizv60xc5y/Ffwsv8oyfrGxTOVdCOG2o/0b03bfb53da0b65d98b2946078df1096/roundtable3-5.png" alt="roundtable3-5"/></p> <p>(cited from <a href="https://www.snowflake.com/blog/bi-directional-data-sharing-snowflake-salesforce-ga/">https://www.snowflake.com/blog/bi-directional-data-sharing-snowflake-salesforce-ga/</a>)</p> <p><strong>Shibata:</strong></p> <p>The Zero Copy integration, right? It’s really convenient.</p> <p>As these developments progress, the hurdles for data integration will be lowered. I think the next step will be focusing on data utilization using these tools.</p> <p><strong>Onodera:</strong></p> <p>Actual customer data isn’t always managed only in CRM; there might be offline event application data in spreadsheets or inquiry data stored in the cloud. Lowering the hurdles for data integration could enable more data-driven sales and marketing.</p> <p><strong>Shibata:</strong></p> <p>For example, companies with multiple products could perform comprehensive data analysis for upselling and cross-selling.</p> <p>Having good data could become a competitive advantage in such a trend.</p> <p><img src="//images.ctfassets.net/9ncizv60xc5y/20uRpSWfRDmwDdesUfm9hq/ef1d68fcdd0d23c814df0f396d95f9a2/roundtable3-6.jpg" alt="roundtable3-6"/></p> <h2>Supporting the Creation of ‘Good Data’</h2> <p><strong>Onodera:</strong></p> <p>Considering this, it would be great if Morph could support the creation of ‘good data.’</p> <p><strong>Shibata:</strong></p> <p>Exactly. We want to cover the creation of ‘good data’ and preprocessing for data analysis using an LLM.</p> <p>In data analysis, with the use of both machine learning and LLMs, the requirements for data preprocessing has changed. I think it would be ideal if Morph became complementary to a tool such as Snowflake.</p></div></div><hr/><h3 class="__className_4bc053 text-2xl font-bold my-2">Related Articles</h3><div class="mb-20"><div class="grid grid-cols-1 lg:grid-cols-2 w-full gap-4"><a class="no-underline " href="/blog/biz-x-dev-roundtable-1"><div class="bg-white dark:bg-black py-6 px-4 rounded-xl w-full flex flex-col gap-4 border dark:border-gray-600 transition-all cursor-pointer hover:bg-muted"><img src="//images.ctfassets.net/9ncizv60xc5y/5osaVhN8lMZsjZlz3NowUq/43070bba5013f017da71b289e928885d/biz_x_dev_1-1.jpg" width="1080" height="600" alt="Hero" class="w-full rounded-xl my-0"/><div class="text-xl font-bold">How can I know which features activate the data stored in Snowflake? : Biz x Dev Roundtable</div><div class="text-base text-muted-foreground">The Biz x Dev Roundtable series focus on data analysis with Snowflake data.</div><div class="text-sm text-muted-foreground">2024-06-11</div></div></a><a 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Sales Manager @Morph\n\nManager of Corporate Sales at Morph.\n\n### Naoto Shibata - CEO/Backend Engineer @Morph\n\nHe is our CEO, but also the lead backend engineer at Morph.\n\n---\n\n**Onodera:**\n\nThe Snowflake Summit 2024 was recently held, and there were many announcements related to AI.\n\nToday, I'd like to discuss how to actually use Snowflake's Cortex in our work.\n\nWas there anything from the Snowflake Summit 2024 that caught your attention?\n\n**Shibata:**\n\nFirst of all, I’m happy about dark mode!\n\nAnd of course, there were many AI-related announcements, and they strongly emphasized the concept of the \"AI Data Cloud.\"\n\n![roundtable3-2](//images.ctfassets.net/9ncizv60xc5y/2tGeXXLOm6Ap6Z26XVDz0o/998f00c38cc578c58425e5fed143f596/roundtable3-2.jpg)\n\n**Onodera:**\n\nI had used the Notebook before, so I found the announcements easy to understand, but there were many features that made me think, \"We can do this now.\"\n\n**Shibata:**\n\nIt's great that we can use AI features without having to build a local environment. We can do a lot on Snowflake.\n\nRegarding the Notebook, it’s nice that we can execute Streamlit commands, allowing us to do everything from AI processing to simple applications with one tool.\n\n**Onodera:**\n\nSeeing those announcements inspired me, and today I'd like to discuss while actually using Cortex.\n\n## Cleansing Sales Data Using LLMs\n\n**Onodera:**\n\nI’ve prepared some E-commerce data. Looking at the product information, it mixes physical products and fan club payments.\n\nThis is problematic for analysis, so I used LLM to classify the categories.\n\nHere is the actual code.\n\n![roundtable3-3](//images.ctfassets.net/9ncizv60xc5y/68pwOzAQaOPtUg4wLkcGNe/d6b220d48f1c0fb8e79893bbf2689dff/roundtable3-3.jpg)\n\n**Shibata:**\n\nYou can call LLM within the SELECT statement.\n\n**Onodera:**\n\nThat's right. The results were good, and I think it can be used in actual customer projects.\n\n**Shibata:**\n\nPreviously, we would have to query with SQL, call an API like ChatGPT from Python to process it, and then return it to Snowflake, so it simplifies things a lot.\n\n**Onodera:**\n\nAs someone from the business side, I’m familiar with SQL, so it’s easy to handle.\n\nBut I wanted to ask, even with such tools, are there still cases where you need to use Python?\n\n**Shibata:**\n\nWith this much functionality, many cases are covered.\n\nHowever, the advantage of using Python is that you can use the latest models immediately. With an integrated tool solution, you have to wait until the tool, in this case Snowflake, supports it. Using Python to directly access the API gives you more flexibility.\n\nAlso, in practice, tasks are rarely completed with just LLM processing, so there is still strength in writing code.\n\nI’m curious how much Cortex can handle commands like [Function Calling](https://platform.openai.com/docs/guides/function-calling), which restricts the output format of LLM.\n\nIn this case, we’re only trying it with data output from the EC site, but there might be situations where you want to combine it with other data stored in Excel, for example.\n\n**Onodera:**\n\nThat's true. In actual projects, it seems like Python processing will also be necessary.\n\n![roundtable3-4](//images.ctfassets.net/9ncizv60xc5y/64jBZ3wbS1CazM68rE5lpl/13ba8ed64f96d5e1f7f6c2c1d08ebde6/roundtable3-4.jpg)\n\n## Scoring Salesforce Prospective Customers\n\n**Onodera:**\n\nAnother dataset I brought is Salesforce prospective customer data. Scoring prospective customers is still heavily reliant on qualitative information, so I want to label it more quantitatively.\n\nThere are many methods for customer scoring that focus on quantitative data, but it’s another matter to build it myself. I thought it might be easy to do with Cortex’s features, so I tried it out.\n\nWhat I did was build a model from the data of customers who actually purchased in the past and then use it to score prospective customers.\n\n**Shibata:**\n\nYou can do this with SQL too.\n\n**Shibata:**\n\nIt’s sufficient at least as a prototype. You can improve it while testing with actual data.\n\n**Onodera:**\n\nIt only took about five minutes to build.\n\nWith [the enhanced integration between Salesforce and Snowflake](https://www.snowflake.com/blog/bi-directional-data-sharing-snowflake-salesforce-ga/), I feel the possibilities have expanded.\n\n![roundtable3-5](//images.ctfassets.net/9ncizv60xc5y/Ffwsv8oyfrGxTOVdCOG2o/0b03bfb53da0b65d98b2946078df1096/roundtable3-5.png)\n\n(cited from https://www.snowflake.com/blog/bi-directional-data-sharing-snowflake-salesforce-ga/)\n\n**Shibata:**\n\nThe Zero Copy integration, right? It’s really convenient.\n\nAs these developments progress, the hurdles for data integration will be lowered. I think the next step will be focusing on data utilization using these tools.\n\n**Onodera:**\n\nActual customer data isn’t always managed only in CRM; there might be offline event application data in spreadsheets or inquiry data stored in the cloud. Lowering the hurdles for data integration could enable more data-driven sales and marketing.\n\n**Shibata:**\n\nFor example, companies with multiple products could perform comprehensive data analysis for upselling and cross-selling.\n\nHaving good data could become a competitive advantage in such a trend.\n\n![roundtable3-6](//images.ctfassets.net/9ncizv60xc5y/20uRpSWfRDmwDdesUfm9hq/ef1d68fcdd0d23c814df0f396d95f9a2/roundtable3-6.jpg)\n\n## Supporting the Creation of ‘Good Data’\n\n**Onodera:**\n\nConsidering this, it would be great if Morph could support the creation of ‘good data.’\n\n**Shibata:**\n\nExactly. We want to cover the creation of ‘good data’ and preprocessing for data analysis using an LLM.\n\nIn data analysis, with the use of both machine learning and LLMs, the requirements for data preprocessing has changed. I think it would be ideal if Morph became complementary to a tool such as Snowflake.\n"])</script><script>self.__next_f.push([1,"6:[\"$\",\"div\",null,{\"className\":\"grid grid-cols-1 gap-3\",\"children\":[[\"$\",\"img\",null,{\"src\":\"//images.ctfassets.net/9ncizv60xc5y/whS0tFuIZKiGZ1ME0Pyj9/30dbc27c21581c9f2427ac9745617e5d/roundtable3-1.jpg\",\"alt\":\"\",\"className\":\"w-full rounded-lg mt-0 mb-6\"}],[\"$\",\"h3\",null,{\"children\":\"Snowflake Summit 2024 After-action Report and Workshopping: Biz x Dev Roundtable\",\"className\":\"__className_4bc053 text-4xl font-bold my-2\"}],[\"$\",\"div\",null,{\"className\":\"flex items-center gap-3\",\"children\":\"$undefined\"}],[\"$\",\"div\",null,{\"className\":\"flex items-center justify-between\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex items-center space-x-2 text-muted-foreground\",\"children\":[\"$\",\"span\",null,{\"children\":\"2024-06-20\"}]}],[\"$\",\"$L19\",null,{\"url\":\"https://www.morph-data.io/blog/biz-x-dev-roundtable-3\",\"hashTags\":[\"morph\"],\"title\":\"Snowflake Summit 2024 After-action Report and Workshopping: Biz x Dev Roundtable\"}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-row items-center space-x-4\",\"children\":[[\"$\",\"img\",null,{\"src\":\"//images.ctfassets.net/9ncizv60xc5y/5smgawyx0Jp59cqCSTG1xZ/2744fb5128d87b873d7ed55c4e4d9b68/onodera.jpg\",\"className\":\"w-16 h-16 rounded-full my-0\",\"width\":260,\"height\":260}],[\"$\",\"div\",null,{\"children\":[[\"$\",\"div\",null,{\"className\":\"text-lg font-bold\",\"children\":\"Hayato Onodera\"}],[\"$\",\"div\",null,{\"className\":\"text-base\",\"children\":\"Sales Manager\"}]]}]]}],[\"$\",\"div\",null,{\"className\":\"prose dark:prose-invert w-full max-w-full my-8\",\"children\":[\"$\",\"$L1a\",null,{\"source\":\"$1b\"}]}],[\"$\",\"hr\",null,{}],[\"$\",\"h3\",null,{\"children\":\"Related Articles\",\"className\":\"__className_4bc053 text-2xl font-bold my-2\"}],[\"$\",\"div\",null,{\"className\":\"mb-20\",\"children\":[\"$\",\"div\",null,{\"className\":\"grid grid-cols-1 lg:grid-cols-2 w-full gap-4\",\"children\":[[\"$\",\"$L10\",\"biz-x-dev-roundtable-1\",{\"href\":\"/blog/biz-x-dev-roundtable-1\",\"className\":\"no-underline \",\"children\":[\"$\",\"div\",null,{\"className\":\"bg-white dark:bg-black py-6 px-4 rounded-xl w-full flex flex-col gap-4 border dark:border-gray-600 transition-all cursor-pointer hover:bg-muted\",\"children\":[[\"$\",\"img\",null,{\"src\":\"//images.ctfassets.net/9ncizv60xc5y/5osaVhN8lMZsjZlz3NowUq/43070bba5013f017da71b289e928885d/biz_x_dev_1-1.jpg\",\"width\":1080,\"height\":600,\"alt\":\"Hero\",\"className\":\"w-full rounded-xl my-0\"}],[\"$\",\"div\",null,{\"className\":\"text-xl font-bold\",\"children\":\"How can I know which features activate the data stored in Snowflake? 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