{ "source_file": "0930", "source_platform": "Medium", "author": "Chad Isenberg", "title_listed": "Data Teams as Support Teams", "keywords": "", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/", "publish_date": "2023-01-05", "corpus_stage": "", "notes": "", "id": "a7b0daa1a9c1", "collected_at": "2026-10-02T04:57:15", "title": "Data Teams as Support Teams | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n * Cookie\nBCTempID\n * Duration\n10 minutes\n * Description\nNo description available.\n\n\n * Cookie\n__cf_bm\n * Duration\n1 hour\n * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n * Cookie\nAWSALBCORS\n * Duration\n7 days\n * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n * Cookie\n_cfuvid\n * Duration\nsession\n * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n * Cookie\nli_gc\n * Duration\n6 months\n * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n * Cookie\n__hssrc\n * Duration\nsession\n * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n * Cookie\n__hssc\n * Duration\n1 hour\n * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n * Cookie\nwpEmojiSettingsSupports\n * Duration\nsession\n * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n * Cookie\nBCSessionID\n * Duration\n1 year 1 month 4 days\n * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n * Cookie\n_octo\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\nlogged_in\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\n__Secure-YEC\n * Duration\npast\n * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n * Cookie\n__eoi\n * Duration\n6 months\n * Description\nDescription is currently not available.\n\n\n * Cookie\nAWSALBTGCORS\n * Duration\n7 days\n * Description\nNo description available.\n\n\n * Cookie\nlogin-status-p\n * Duration\npast\n * Description\nDescription is currently not available.\n\n\n * Cookie\nAWSALBTG\n * Duration\n7 days\n * Description\nNo description available.\n\n\n * Cookie\ncsrf_token\n * Duration\nsession\n * Description\nNo description available.\n\n\n * Cookie\ntoken_v2\n * Duration\n1 day\n * Description\nDescription is currently not available.\n\n\n * Cookie\nD\n * Duration\n1 year\n * Description\nDescription is currently not available.\n\n\n * Cookie\nPHPSESSID\n * Duration\nsession\n * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n * Cookie\nVISITOR_PRIVACY_METADATA\n * Duration\n6 months\n * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n * Cookie\ncookietest\n * Duration\nsession\n * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n * Cookie\n__Host-airtable-session\n * Duration\n1 year\n * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n * Cookie\n__Host-airtable-session.sig\n * Duration\n1 year\n * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n * Cookie\nm\n * Duration\n1 year 1 month 4 days\n * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n * Cookie\nBIGipServer*\n * Duration\nsession\n * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n * Cookie\n__cfruid\n * Duration\nsession\n * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n * Cookie\n_GRECAPTCHA\n * Duration\n6 months\n * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n * Cookie\n__Secure-YNID\n * Duration\n6 months\n * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n * Cookie\ncookieyes-consent\n * Duration\n1 year\n * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n * Cookie\nlidc\n * Duration\n1 day\n * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n * Cookie\nbrw\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\nbrwConsent\n * Duration\n5 minutes\n * Description\nDescription is currently not available.\n\n\n * Cookie\nWMF-Uniq\n * Duration\n1 year\n * Description\nDescription is currently not available.\n\n\n * Cookie\nloom_anon_comment\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\nloom_referral_video\n * Duration\nsession\n * Description\nDescription is currently not available.\n\n\n * Cookie\nVISITOR_INFO1_LIVE\n * Duration\n6 months\n * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n * Cookie\nyt-remote-connected-devices\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n * Duration\nNever Expires\n * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n * Cookie\nyt-remote-device-id\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n * Cookie\nyt-remote-session-name\n * Duration\nsession\n * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n * Cookie\nyt-remote-fast-check-period\n * Duration\nsession\n * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n * Cookie\nyt-remote-session-app\n * Duration\nsession\n * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n * Cookie\nyt-remote-cast-available\n * Duration\nsession\n * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n * Cookie\nyt-remote-cast-installed\n * Duration\nsession\n * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n * Cookie\ncp_session\n * Duration\n3 months\n * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n * Cookie\nloid\n * Duration\n1 year 1 month 4 days\n * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n * Cookie\n__hstc\n * Duration\n6 months\n * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n * Cookie\nhubspotutk\n * Duration\n6 months\n * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n * Cookie\n_ga\n * Duration\n1 year 1 month 4 days\n * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n * Cookie\n_ga_*\n * Duration\n1 year 1 month 4 days\n * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n * Cookie\n__Host-psifi.analyticsTrace\n * Duration\n6 hours\n * Description\nDescription is currently not available.\n\n\n * Cookie\n__Host-psifi.analyticsTraceV2\n * Duration\n6 hours\n * Description\nDescription is currently not available.\n\n\n * Cookie\n_gh_sess\n * Duration\nsession\n * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n * Cookie\nYSC\n * Duration\nsession\n * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n * Cookie\najs_anonymous_id\n * Duration\n1 year\n * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n * Cookie\nvuid\n * Duration\n1 year 1 month 4 days\n * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n * Cookie\nAWSALB\n * Duration\n7 days\n * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n * Cookie\nacq\n * Duration\npast\n * Description\nDescription is currently not available.\n\n\n * Cookie\nacq.sig\n * Duration\npast\n * Description\nDescription is currently not available.\n\n\n * Cookie\nptc\n * Duration\n2 years\n * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n * Cookie\nmuc_ads\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n * Cookie\nguest_id_marketing\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n * Cookie\nguest_id_ads\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n * Cookie\npersonalization_id\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n * Cookie\nguest_id\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n * Cookie\nbcookie\n * Duration\n1 year\n * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n * Cookie\n__Secure-ROLLOUT_TOKEN\n * Duration\n6 months\n * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n * Cookie\nyt.innertube::nextId\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n * Cookie\nyt.innertube::requests\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n * Cookie\nsession_tracker\n * Duration\nsession\n * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Analytics](https://towardsdatascience.com/category/data-science/analytics/)\n# Data Teams as Support Teams\nImproving communication, reducing friction, and helping the business win\n[Chad Isenberg](https://towardsdatascience.com/author/cisenbe/)\nJanuary 5, 20237 min read\n![Photo by Annie Spratt on Unsplash](../images/000.jpg)\nPhoto by [Annie Spratt](https://unsplash.com/@anniespratt?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nDuring a recent department meeting, I brought up a pain point of our data team to our VP. Our customers don't seem to know or care how much of a burden their requests put on us. Most people who've worked in data have had a conversation with a stakeholder where the ask is days if not weeks worth of work, but the expectation is that this is trivial. Furthermore, after putting in substantial effort, we find out that the original request was either unnecessary or unrelated to our customer's actual problem. Essentially, my thesis is that there's a misalignment of incentives between data teams and our customers.\nMy VP had a different point of view. He suggested that our business partners should focus on what they do best, and when they need data to execute on their vision, they should put in the request to us. They shouldn't have to make the call as to whether what they need is \"too expensive\" or \"too time-consuming.\"\nAnd my VP isn't wrong. I doubt any of us who have utilized an internal service organization like HR, legal, IT, or procurement have thought about the complexity or cost of our requests. How long could proof of income for a loan possibly take? What does it take to get my broken laptop replaced? These concerns are wholly absorbed by their respective organizations, and the business as a whole runs more efficiently that way.\nEven with all of this in mind, I can't shake the feeling that _data is different_. I'm not suggesting that data as a space is generally more difficult, more valuable, or more complex than these other support functions; quite the contrary, I think because the space is relatively immature, a lot of processes and concepts are still missing or half-baked. On the whole, I think data teams tend to have weak operating models supporting questionable value propositions. And at the core, there are two flaws: the data model and the analytics value proposition.\nLearn this step by step with the interactive [Data Analyst](https://roadmap.sh/data-analyst) roadmap.\n### Data Modeling\nI'm a huge fan of [Chad Sanderson](https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/None), and his constant push for businesses to prioritize data modeling is one of my core beliefs. To be clear, there's always data modeling; whether that's a shared exercise (when done in a warehouse or lakehouse) or an individual one (in a single report or dashboard) is the fundamental question. The lack of core data models leads to multiple understandings of the business, a situation that leads to misalignment across teams.\nFurthermore, without well-defined semantics, the relationship between the data and the business processes they underpin becomes extremely difficult to understand. Even experienced analysts can spend vast amounts of time trying to tease out misalignment between processes, data, and analytical outputs. When most companies are working with dozens or hundreds of sources, many of which are externally-maintained, and all of them have independent (operational) data models, is it any wonder that we frequently fail to paint a coherent picture of the business?\nProper data modeling is a complex exercise amongst product (when applicable), operations, and data teams. Most businesses have under-invested if not outright ignored this critical element of developing valuable data assets. Instead of making collaborative decisions up front, we make individual decisions on the back end; engineering and analyst time gets eaten up re-inventing the wheel for each asset, report, and dashboard. We as data professionals feel this pain, and by extension, our customers do through poor (or non-existent) time-to-value.\n### The Analytics Value Proposition\nIn my experience, many customers struggle with understanding what questions data can answer, and more importantly, how to tie business outcomes to data assets. [XY problems](https://en.wikipedia.org/wiki/XY_problem)¹ are the lingua franca of business teams when communicating with data teams.\nTo some extent, the lack of core data models exacerbates our customers' confusion. They bring (often but not always very reasonable) assumptions about what the data _must_ look like to the table, and because they don't live in the tables and pipelines, they're frequently very wrong. Even when our customers know what data assets exist, they don't necessarily have the expertise to understand whether or not they can be applied to their specific use-cases.\nThe more intractable problem is that we as an industry have a very tenuous grasp on what analytical outputs are valuable, and we have even less insight into how much value any given output produces. We \"know\" that there's something valuable about making data available to the business (look at the success of FAANG+), and there's research suggesting companies that leverage data are more effective than their peers², but the specific mechanisms are poorly understood.\nEssentially, our customers never stood a chance at making good requests, at least not consistently. They don't (nor should they have to) understand the details of their organization's data assets, and they're as much in the dark as the experts about whether or not the report they want will provide any real business value. They can ask for reports all day long, and nobody is the wiser as to whether or not that was a good use of the data team's time. This lack of a feedback loop is frustrating for data teams, and it can leave customers feeling like they didn't get what they asked for: not because the deliverable was wrong per spec, **but** **because the requested deliverable was never able to generate value in the first place**.\n### Data Is Not Information\nAt the core of all of this pain is something really simple. Data is not information. Data is not insight. I've seen business people lose a tremendous amount of their valuable time trying to do their own data analysis. Staring at a spreadsheet and endlessly pivoting and filtering does not generate value.\n[Robert Yi](https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/None) [has an excellent article on alignment](https://www.winwithdata.io/p/alignment-pedantry)³ that covers this better than I ever could, but I'll take a crack at adding some thoughts. Data assets are deeply mired in context that only experts can successfully navigate. The numbers are never just the numbers, which is why analytics is deceptively hard. It's like trying to follow a map, only to realize that sometimes there are roads where none are depicted, and there aren't any where there should be. \"Oh yeah. Old Main was decommissioned two years ago, but we decided to leave it on the map so you would understand why Main Street runs the way it does.\" Travelers would be doomed.\nAnd we end up with these sketchy maps because (to mix metaphors) the sausage-making is especially messy. Analytics is a very complex process by which 1) the business takes information (bookings, billings, purchases) 2) that then gets encoded as data by products and enterprise systems (a record on an account object, a record in an inventory table) 3) that ultimately gets extracted to an analytical data store and re-assembled as information. This last step is always more art than science.\nIt's a game of telephone that we're pretty bad at. Semantics get lost along the way. We fail to understand important distinctions in the way different sources' object models interact when we try to combine them. Humans will adjust to slight deviations in business processes over time, but our analytics systems will not.\nBut there are things we can do, and I'm optimistic about the future. In particular, I think we should focus on:\n 1. Improving our understanding of what analytics activities drive value. Research institutions are a part of this, but we can do a lot of field work in the meantime. In much the same way that product companies conduct experiments to link features to value (churn reduction, expansion), data teams should start making investments in experimentation frameworks. We need to know that our data products are wanted (from customer feedback) and valuable (from metrics).\n 2. Improving our data models. By more closely aligning our core data assets with the business, we can have better agility in answering our customers' questions. Because analytics is frequently time-dependent, being able to deliver answers within days rather than weeks is the difference between providing a lot of value and providing none at all.\n 3. Improving our customer relationships. We need to remind ourselves that our customers shouldn't be expected to understand our incredibly complex systems when asking for help. It's not their fault that analytics is not a solved space. At the same time, they need to be more engaged with our work and its challenges. We can't simply roll our eyes and say, \"That's a huge amount of work,\" and they can't tune out when we elaborate: \"because the field you're asking for only exists in one of our four billings systems.\"\n 4. Improve our operating models. We need to develop systems for identifying valuable data work amidst a flurry of customer requests, communicating requirements effectively, and adding accountability and feedback into delivery. We need to empower our customers to ask the right questions, to know that we're working on things that matter, and to make sure that what we deliver is providing value, and if not, the ability to distinguish where in the process we failed: simply a \"bad\" request? Insufficient requirements? Misunderstood requirements? Poor work quality?\n\n\n### Conclusion - There's No \"I\" in Team (or in Data)\nEspecially in these difficult times, it's easy to point fingers and feel frustrated when we see our counterparts fail. At the end of the day, we're all in this together. Data teams want our companies to succeed, which means that we need our business partners to succeed. Likewise, they're invested in our success. We all need to remind ourselves that we're on the same team, sharing the same challenges and having our own part in overcoming them.\n* * *\n_References_\n[1] Wikipedia contributors, [XY problem](https://en.wikipedia.org/w/index.php?title=XY_problem&oldid=1126742388) (2022, December 11), Wikipedia, The Free Encyclopedia\n[2] Weill, Peter and Woerner, Stephanie L., [Dashboarding Pays Off ](https://cisr.mit.edu/publication/2022_0101_Dashboarding_WeillWoerner)(January 2022), MIT CISR Research Briefings No. XXII-1\n[3] Yi, Robert, [Communication is about alignment](https://www.winwithdata.io/p/alignment-pedantry) (December 2022), Win with Data\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-9e4c3e3de7a22688d8ce.jpeg)\nWritten By\n## [Chad Isenberg](https://towardsdatascience.com/author/cisenbe/)\n[See all from Chad Isenberg](https://towardsdatascience.com/author/cisenbe/)\n[Analytics](https://towardsdatascience.com/tag/analytics/)[Business Intelligence](https://towardsdatascience.com/tag/business-intelligence/)[Data](https://towardsdatascience.com/tag/data/)[Data Team](https://towardsdatascience.com/tag/data-team/)[Notes From Industry](https://towardsdatascience.com/tag/notes-from-industry/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F&title=Data%20Teams%20as%20Support%20Teams)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F&title=Data%20Teams%20as%20Support%20Teams)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F&text=Data%20Teams%20as%20Support%20Teams)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n * [![](../images/001.png)](https://towardsdatascience.com/from-dataslows-to-dataflows-the-gen2-performance-revolution-in-microsoft-fabric/)\n## [From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric](https://towardsdatascience.com/from-dataslows-to-dataflows-the-gen2-performance-revolution-in-microsoft-fabric/)\n[Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/)\nHow the latest Dataflow enhancements change the way we play the game\n[Nikola Ilic](https://towardsdatascience.com/author/datamozart/)\nJanuary 13, 20268 min read\n * [![](../images/002.png)](https://towardsdatascience.com/exploratory-data-analysis-dcb5e7189c4e/)\n## [Exploratory Data Analysis](https://towardsdatascience.com/exploratory-data-analysis-dcb5e7189c4e/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nIncludes a simple case study for better understanding.\n[Pratheesh Shivaprasad](https://towardsdatascience.com/author/iampratheesh/)\nAugust 15, 201910 min read\n * [![](../images/003.jpg)](https://towardsdatascience.com/unique-data-is-better-than-a-perfect-model-6a2fabb51806/)\n## [Unique Data Is Better Than A Perfect Model](https://towardsdatascience.com/unique-data-is-better-than-a-perfect-model-6a2fabb51806/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nYour Goal As A Data Scientist Should Be To Build And Work With Proprietary Datasets\n[Tony Yiu](https://towardsdatascience.com/author/tonester524/)\nJune 15, 20214 min read\n * [![](../images/004.jpg)](https://towardsdatascience.com/the-most-underrated-r-packages-part-2-decdc753205c/)\n## [The Most Underrated R packages: Part 2](https://towardsdatascience.com/the-most-underrated-r-packages-part-2-decdc753205c/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA curated list of awesome and less known R libraries\n[Alessandro Arrigo](https://towardsdatascience.com/author/alearrigo/)\nSeptember 27, 20226 min read\n * [![](../images/body-63beb50f664a055ed739.jpg)](https://towardsdatascience.com/the-three-most-important-statistical-tests-in-business-analytics-fd958a8e2a90/)\n## [The Three Most Important Statistical Tests in Business Analytics](https://towardsdatascience.com/the-three-most-important-statistical-tests-in-business-analytics-fd958a8e2a90/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nThere are more statistical tests than one can count; all of them have a reason to exist and mathematicians are paid to invent new ones all...\n[Elvis](https://towardsdatascience.com/author/the-elvis/)\nMarch 17, 20216 min read\n * [![](../images/006.jpeg)](https://towardsdatascience.com/how-to-convert-a-csv-file-into-an-xlsx-one-with-python-and-pandas-27aabc279d69/)\n## [How to Convert a CSV file into an XLSX one with Python and Pandas](https://towardsdatascience.com/how-to-convert-a-csv-file-into-an-xlsx-one-with-python-and-pandas-27aabc279d69/)\n[](https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/)\nAn example of how to use Python to convert files\n[Federico Trotta](https://towardsdatascience.com/author/federicotrotta/)\nAugust 9, 20223 min read\n * [![](../images/007.jpg)](https://towardsdatascience.com/race-and-ethnicity-in-data-science-138acf1c23b4/)\n## [Race and Ethnicity in Data Science](https://towardsdatascience.com/race-and-ethnicity-in-data-science-138acf1c23b4/)\n[Race](https://towardsdatascience.com/category/society/race/)\nWhy it's important and how we should approach it\n[Lathan Liou](https://towardsdatascience.com/author/lathan-liou/)\nAugust 23, 20219 min read\n * [![](../images/008.png)](https://towardsdatascience.com/node2vec-explained-db86a319e9ab/)\n## [Node2Vec Explained](https://towardsdatascience.com/node2vec-explained-db86a319e9ab/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nExplaining & Implementing the Node2Vec Paper in Python\n[Vatsal](https://towardsdatascience.com/author/vatsal12-p/)\nJanuary 31, 202210 min read\n * [![](../images/009.jpeg)](https://towardsdatascience.com/before-you-get-into-data-science-a-journey-from-a-non-it-or-non-engineer-degree-f789f192ee8b/)\n## [Before you get into Data Science: A journey from a non-IT or non-engineer degree](https://towardsdatascience.com/before-you-get-into-data-science-a-journey-from-a-non-it-or-non-engineer-degree-f789f192ee8b/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nHi there!\n[Cakramurti Ocha](https://towardsdatascience.com/author/cakraocha/)\nDecember 22, 20209 min read\n\n\n * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n * [X](https://x.com/TDataScience)\n * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n * [Threads](https://www.threads.net/@towardsdatascience)\n * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n#### Recommended Articles \nClose\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n", "metadata": { "title": "Data Teams as Support Teams | Towards Data Science", "description": "Improving communication, reducing friction, and helping the business win", "keywords": null, "author": "Chad Isenberg", "og:locale": "en_US", "og:type": "article", "og:title": "Data Teams as Support Teams", "og:description": "Improving communication, reducing friction, and helping the business win", "og:url": "https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/", "og:site_name": "Towards Data Science", "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/0a9uv-yOF4RrWTp2u-scaled.jpg", "og:image:width": "2560", "og:image:height": "1754", "og:image:type": "image/jpeg", "twitter:card": "summary_large_image", "twitter:site": "@TDataScience", "twitter:creator": "@TDataScience", "twitter:label1": "Written by", "twitter:data1": "Chad Isenberg", "twitter:label2": "Est. reading time", "twitter:data2": "7 min read", "article:published_time": "2023-01-05T05:25:14.000Z", "article:modified_time": "2023-01-05T05:25:14.000Z", "article:author": "https://towardsdatascience.com/author/cisenbe/" }, "paywall": { "listed_paid": false, "detected": false, "marker": "", "public_only": false, "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙" }, "crawl": { "success": true, "status_code": 202, "error": "" }, "code_blocks": [], "images": [ { "status": "http_404", "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500", "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500", "alt": "", "downloaded": false, "discovery": "saved_markdown", "retried_at": "2026-10-20T17:37:19Z" }, { "status": "http_404", "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500", "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500", "alt": "", "downloaded": false, "discovery": "saved_markdown", "retried_at": "2026-10-20T17:37:56Z" }, { "status": "http_404", "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500", "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500", "alt": "", "downloaded": false, "discovery": "saved_markdown", "retried_at": "2026-10-20T17:37:56Z" }, { "status": "http_404", "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500", "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500", "alt": "", "downloaded": false, "discovery": "saved_markdown", "retried_at": "2026-10-20T17:37:56Z" } ], "audio": [], "videos": [ { "url": "https://www.youtube.com/c/TowardsDataScience", "kind": "channel_reference", "status": "reference_only" } ], "links": [ { "type": "internal", "href": "https://towardsdatascience.com/" }, { "type": "internal", "href": "https://towardsdatascience.com/latest/" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/editors-pick/" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/the-variable/" }, { "type": "internal", "href": "https://towardsdatascience.com/shipai/" }, { "type": "internal", "href": "https://towardsdatascience.com/submissions/" }, { "type": "internal", "href": "https://towardsdatascience.com/category/data-science/analytics/" }, { "type": "internal", "href": "https://towardsdatascience.com/author/cisenbe/" }, { "type": "internal", "href": "https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/None" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/analytics/" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/business-intelligence/" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/data/" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/data-team/" }, { "type": "internal", "href": "https://towardsdatascience.com/tag/notes-from-industry/" }, { "type": "internal", "href": "https://towardsdatascience.com/from-dataslows-to-dataflows-the-gen2-performance-revolution-in-microsoft-fabric/" }, { "type": "internal", "href": "https://towardsdatascience.com/category/data-science/data-engineering/" }, { "type": "internal", "href": "https://towardsdatascience.com/author/datamozart/" }, { "type": "internal", "href": "https://towardsdatascience.com/exploratory-data-analysis-dcb5e7189c4e/" }, { "type": "internal", "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/" }, { "type": "internal", "href": "https://towardsdatascience.com/author/iampratheesh/" }, { "type": "external", "href": "https://write.insightmediagroup.io" }, { "type": "external", "href": "https://www.linkedin.com/company/towards-data-science/" }, { "type": "external", "href": "https://x.com/TDataScience" }, { "type": "external", "href": "https://unsplash.com/@anniespratt" }, { "type": "external", "href": "https://unsplash.com" }, { "type": "external", "href": "https://roadmap.sh/data-analyst" }, { "type": "external", "href": "https://en.wikipedia.org/wiki/XY_problem" }, { "type": "external", "href": "https://www.winwithdata.io/p/alignment-pedantry" }, { "type": "external", "href": "https://en.wikipedia.org/w/index.php?oldid=1126742388&title=XY_problem" }, { "type": "external", "href": "https://cisr.mit.edu/publication/2022_0101_Dashboarding_WeillWoerner" }, { "type": "external", "href": "https://www.facebook.com/sharer/sharer.php?title=Data+Teams+as+Support+Teams&u=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F" }, { "type": "external", "href": "https://www.linkedin.com/shareArticle?mini=true&title=Data+Teams+as+Support+Teams&url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F" }, { "type": "external", "href": "https://x.com/share?text=Data+Teams+as+Support+Teams&url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F" }, { "type": "external", "href": "https://www.youtube.com/c/TowardsDataScience" }, { "type": "external", "href": "https://www.threads.net/@towardsdatascience" }, { "type": "external", "href": "https://bsky.app/profile/towardsdatascience.com" }, { "type": "external", "href": "https://thenewstack.io" }, { "type": "external", "href": "https://roadmap.sh" }, { "type": "external", "href": "https://insightmediagroup.io/creator-network" } ], "repair_version": 2, "repaired_at": "2026-10-20T17:23:11Z", "source_url": "https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/", "delivery_status": "unverified", "discovery_status": "http_403", "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片", "repair_error": "", "actual_video_files": [], "expected_saved_body_images": 4, "image_discovery": "saved_markdown", "source_body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n * Cookie\nBCTempID\n * Duration\n10 minutes\n * Description\nNo description available.\n\n\n * Cookie\n__cf_bm\n * Duration\n1 hour\n * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n * Cookie\nAWSALBCORS\n * Duration\n7 days\n * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n * Cookie\n_cfuvid\n * Duration\nsession\n * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n * Cookie\nli_gc\n * Duration\n6 months\n * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n * Cookie\n__hssrc\n * Duration\nsession\n * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n * Cookie\n__hssc\n * Duration\n1 hour\n * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n * Cookie\nwpEmojiSettingsSupports\n * Duration\nsession\n * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n * Cookie\nBCSessionID\n * Duration\n1 year 1 month 4 days\n * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n * Cookie\n_octo\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\nlogged_in\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\n__Secure-YEC\n * Duration\npast\n * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n * Cookie\n__eoi\n * Duration\n6 months\n * Description\nDescription is currently not available.\n\n\n * Cookie\nAWSALBTGCORS\n * Duration\n7 days\n * Description\nNo description available.\n\n\n * Cookie\nlogin-status-p\n * Duration\npast\n * Description\nDescription is currently not available.\n\n\n * Cookie\nAWSALBTG\n * Duration\n7 days\n * Description\nNo description available.\n\n\n * Cookie\ncsrf_token\n * Duration\nsession\n * Description\nNo description available.\n\n\n * Cookie\ntoken_v2\n * Duration\n1 day\n * Description\nDescription is currently not available.\n\n\n * Cookie\nD\n * Duration\n1 year\n * Description\nDescription is currently not available.\n\n\n * Cookie\nPHPSESSID\n * Duration\nsession\n * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n * Cookie\nVISITOR_PRIVACY_METADATA\n * Duration\n6 months\n * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n * Cookie\ncookietest\n * Duration\nsession\n * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n * Cookie\n__Host-airtable-session\n * Duration\n1 year\n * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n * Cookie\n__Host-airtable-session.sig\n * Duration\n1 year\n * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n * Cookie\nm\n * Duration\n1 year 1 month 4 days\n * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n * Cookie\nBIGipServer*\n * Duration\nsession\n * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n * Cookie\n__cfruid\n * Duration\nsession\n * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n * Cookie\n_GRECAPTCHA\n * Duration\n6 months\n * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n * Cookie\n__Secure-YNID\n * Duration\n6 months\n * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n * Cookie\ncookieyes-consent\n * Duration\n1 year\n * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n * Cookie\nlidc\n * Duration\n1 day\n * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n * Cookie\nbrw\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\nbrwConsent\n * Duration\n5 minutes\n * Description\nDescription is currently not available.\n\n\n * Cookie\nWMF-Uniq\n * Duration\n1 year\n * Description\nDescription is currently not available.\n\n\n * Cookie\nloom_anon_comment\n * Duration\n1 year\n * Description\nNo description available.\n\n\n * Cookie\nloom_referral_video\n * Duration\nsession\n * Description\nDescription is currently not available.\n\n\n * Cookie\nVISITOR_INFO1_LIVE\n * Duration\n6 months\n * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n * Cookie\nyt-remote-connected-devices\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n * Duration\nNever Expires\n * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n * Cookie\nyt-remote-device-id\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n * Cookie\nyt-remote-session-name\n * Duration\nsession\n * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n * Cookie\nyt-remote-fast-check-period\n * Duration\nsession\n * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n * Cookie\nyt-remote-session-app\n * Duration\nsession\n * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n * Cookie\nyt-remote-cast-available\n * Duration\nsession\n * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n * Cookie\nyt-remote-cast-installed\n * Duration\nsession\n * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n * Cookie\ncp_session\n * Duration\n3 months\n * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n * Cookie\nloid\n * Duration\n1 year 1 month 4 days\n * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n * Cookie\n__hstc\n * Duration\n6 months\n * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n * Cookie\nhubspotutk\n * Duration\n6 months\n * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n * Cookie\n_ga\n * Duration\n1 year 1 month 4 days\n * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n * Cookie\n_ga_*\n * Duration\n1 year 1 month 4 days\n * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n * Cookie\n__Host-psifi.analyticsTrace\n * Duration\n6 hours\n * Description\nDescription is currently not available.\n\n\n * Cookie\n__Host-psifi.analyticsTraceV2\n * Duration\n6 hours\n * Description\nDescription is currently not available.\n\n\n * Cookie\n_gh_sess\n * Duration\nsession\n * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n * Cookie\nYSC\n * Duration\nsession\n * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n * Cookie\najs_anonymous_id\n * Duration\n1 year\n * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n * Cookie\nvuid\n * Duration\n1 year 1 month 4 days\n * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n * Cookie\nAWSALB\n * Duration\n7 days\n * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n * Cookie\nacq\n * Duration\npast\n * Description\nDescription is currently not available.\n\n\n * Cookie\nacq.sig\n * Duration\npast\n * Description\nDescription is currently not available.\n\n\n * Cookie\nptc\n * Duration\n2 years\n * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n * Cookie\nmuc_ads\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n * Cookie\nguest_id_marketing\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n * Cookie\nguest_id_ads\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n * Cookie\npersonalization_id\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n * Cookie\nguest_id\n * Duration\n1 year 1 month 4 days\n * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n * Cookie\nbcookie\n * Duration\n1 year\n * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n * Cookie\n__Secure-ROLLOUT_TOKEN\n * Duration\n6 months\n * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n * Cookie\nyt.innertube::nextId\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n * Cookie\nyt.innertube::requests\n * Duration\nNever Expires\n * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n * Cookie\nsession_tracker\n * Duration\nsession\n * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Analytics](https://towardsdatascience.com/category/data-science/analytics/)\n# Data Teams as Support Teams\nImproving communication, reducing friction, and helping the business win\n[Chad Isenberg](https://towardsdatascience.com/author/cisenbe/)\nJanuary 5, 20237 min read\n![Photo by Annie Spratt on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/0a9uv-yOF4RrWTp2u-scaled.jpg)\nPhoto by [Annie Spratt](https://unsplash.com/@anniespratt?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nDuring a recent department meeting, I brought up a pain point of our data team to our VP. Our customers don't seem to know or care how much of a burden their requests put on us. Most people who've worked in data have had a conversation with a stakeholder where the ask is days if not weeks worth of work, but the expectation is that this is trivial. Furthermore, after putting in substantial effort, we find out that the original request was either unnecessary or unrelated to our customer's actual problem. Essentially, my thesis is that there's a misalignment of incentives between data teams and our customers.\nMy VP had a different point of view. He suggested that our business partners should focus on what they do best, and when they need data to execute on their vision, they should put in the request to us. They shouldn't have to make the call as to whether what they need is \"too expensive\" or \"too time-consuming.\"\nAnd my VP isn't wrong. I doubt any of us who have utilized an internal service organization like HR, legal, IT, or procurement have thought about the complexity or cost of our requests. How long could proof of income for a loan possibly take? What does it take to get my broken laptop replaced? These concerns are wholly absorbed by their respective organizations, and the business as a whole runs more efficiently that way.\nEven with all of this in mind, I can't shake the feeling that _data is different_. I'm not suggesting that data as a space is generally more difficult, more valuable, or more complex than these other support functions; quite the contrary, I think because the space is relatively immature, a lot of processes and concepts are still missing or half-baked. On the whole, I think data teams tend to have weak operating models supporting questionable value propositions. And at the core, there are two flaws: the data model and the analytics value proposition.\nLearn this step by step with the interactive [Data Analyst](https://roadmap.sh/data-analyst) roadmap.\n### Data Modeling\nI'm a huge fan of [Chad Sanderson](https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/None), and his constant push for businesses to prioritize data modeling is one of my core beliefs. To be clear, there's always data modeling; whether that's a shared exercise (when done in a warehouse or lakehouse) or an individual one (in a single report or dashboard) is the fundamental question. The lack of core data models leads to multiple understandings of the business, a situation that leads to misalignment across teams.\nFurthermore, without well-defined semantics, the relationship between the data and the business processes they underpin becomes extremely difficult to understand. Even experienced analysts can spend vast amounts of time trying to tease out misalignment between processes, data, and analytical outputs. When most companies are working with dozens or hundreds of sources, many of which are externally-maintained, and all of them have independent (operational) data models, is it any wonder that we frequently fail to paint a coherent picture of the business?\nProper data modeling is a complex exercise amongst product (when applicable), operations, and data teams. Most businesses have under-invested if not outright ignored this critical element of developing valuable data assets. Instead of making collaborative decisions up front, we make individual decisions on the back end; engineering and analyst time gets eaten up re-inventing the wheel for each asset, report, and dashboard. We as data professionals feel this pain, and by extension, our customers do through poor (or non-existent) time-to-value.\n### The Analytics Value Proposition\nIn my experience, many customers struggle with understanding what questions data can answer, and more importantly, how to tie business outcomes to data assets. [XY problems](https://en.wikipedia.org/wiki/XY_problem)¹ are the lingua franca of business teams when communicating with data teams.\nTo some extent, the lack of core data models exacerbates our customers' confusion. They bring (often but not always very reasonable) assumptions about what the data _must_ look like to the table, and because they don't live in the tables and pipelines, they're frequently very wrong. Even when our customers know what data assets exist, they don't necessarily have the expertise to understand whether or not they can be applied to their specific use-cases.\nThe more intractable problem is that we as an industry have a very tenuous grasp on what analytical outputs are valuable, and we have even less insight into how much value any given output produces. We \"know\" that there's something valuable about making data available to the business (look at the success of FAANG+), and there's research suggesting companies that leverage data are more effective than their peers², but the specific mechanisms are poorly understood.\nEssentially, our customers never stood a chance at making good requests, at least not consistently. They don't (nor should they have to) understand the details of their organization's data assets, and they're as much in the dark as the experts about whether or not the report they want will provide any real business value. They can ask for reports all day long, and nobody is the wiser as to whether or not that was a good use of the data team's time. This lack of a feedback loop is frustrating for data teams, and it can leave customers feeling like they didn't get what they asked for: not because the deliverable was wrong per spec, **but** **because the requested deliverable was never able to generate value in the first place**.\n### Data Is Not Information\nAt the core of all of this pain is something really simple. Data is not information. Data is not insight. I've seen business people lose a tremendous amount of their valuable time trying to do their own data analysis. Staring at a spreadsheet and endlessly pivoting and filtering does not generate value.\n[Robert Yi](https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/None) [has an excellent article on alignment](https://www.winwithdata.io/p/alignment-pedantry)³ that covers this better than I ever could, but I'll take a crack at adding some thoughts. Data assets are deeply mired in context that only experts can successfully navigate. The numbers are never just the numbers, which is why analytics is deceptively hard. It's like trying to follow a map, only to realize that sometimes there are roads where none are depicted, and there aren't any where there should be. \"Oh yeah. Old Main was decommissioned two years ago, but we decided to leave it on the map so you would understand why Main Street runs the way it does.\" Travelers would be doomed.\nAnd we end up with these sketchy maps because (to mix metaphors) the sausage-making is especially messy. Analytics is a very complex process by which 1) the business takes information (bookings, billings, purchases) 2) that then gets encoded as data by products and enterprise systems (a record on an account object, a record in an inventory table) 3) that ultimately gets extracted to an analytical data store and re-assembled as information. This last step is always more art than science.\nIt's a game of telephone that we're pretty bad at. Semantics get lost along the way. We fail to understand important distinctions in the way different sources' object models interact when we try to combine them. Humans will adjust to slight deviations in business processes over time, but our analytics systems will not.\nBut there are things we can do, and I'm optimistic about the future. In particular, I think we should focus on:\n 1. Improving our understanding of what analytics activities drive value. Research institutions are a part of this, but we can do a lot of field work in the meantime. In much the same way that product companies conduct experiments to link features to value (churn reduction, expansion), data teams should start making investments in experimentation frameworks. We need to know that our data products are wanted (from customer feedback) and valuable (from metrics).\n 2. Improving our data models. By more closely aligning our core data assets with the business, we can have better agility in answering our customers' questions. Because analytics is frequently time-dependent, being able to deliver answers within days rather than weeks is the difference between providing a lot of value and providing none at all.\n 3. Improving our customer relationships. We need to remind ourselves that our customers shouldn't be expected to understand our incredibly complex systems when asking for help. It's not their fault that analytics is not a solved space. At the same time, they need to be more engaged with our work and its challenges. We can't simply roll our eyes and say, \"That's a huge amount of work,\" and they can't tune out when we elaborate: \"because the field you're asking for only exists in one of our four billings systems.\"\n 4. Improve our operating models. We need to develop systems for identifying valuable data work amidst a flurry of customer requests, communicating requirements effectively, and adding accountability and feedback into delivery. We need to empower our customers to ask the right questions, to know that we're working on things that matter, and to make sure that what we deliver is providing value, and if not, the ability to distinguish where in the process we failed: simply a \"bad\" request? Insufficient requirements? Misunderstood requirements? Poor work quality?\n\n\n### Conclusion - There's No \"I\" in Team (or in Data)\nEspecially in these difficult times, it's easy to point fingers and feel frustrated when we see our counterparts fail. At the end of the day, we're all in this together. Data teams want our companies to succeed, which means that we need our business partners to succeed. Likewise, they're invested in our success. We all need to remind ourselves that we're on the same team, sharing the same challenges and having our own part in overcoming them.\n* * *\n_References_\n[1] Wikipedia contributors, [XY problem](https://en.wikipedia.org/w/index.php?title=XY_problem&oldid=1126742388) (2022, December 11), Wikipedia, The Free Encyclopedia\n[2] Weill, Peter and Woerner, Stephanie L., [Dashboarding Pays Off ](https://cisr.mit.edu/publication/2022_0101_Dashboarding_WeillWoerner)(January 2022), MIT CISR Research Briefings No. XXII-1\n[3] Yi, Robert, [Communication is about alignment](https://www.winwithdata.io/p/alignment-pedantry) (December 2022), Win with Data\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657186741_l2ezf9.jpeg)\nWritten By\n## [Chad Isenberg](https://towardsdatascience.com/author/cisenbe/)\n[See all from Chad Isenberg](https://towardsdatascience.com/author/cisenbe/)\n[Analytics](https://towardsdatascience.com/tag/analytics/)[Business Intelligence](https://towardsdatascience.com/tag/business-intelligence/)[Data](https://towardsdatascience.com/tag/data/)[Data Team](https://towardsdatascience.com/tag/data-team/)[Notes From Industry](https://towardsdatascience.com/tag/notes-from-industry/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F&title=Data%20Teams%20as%20Support%20Teams)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F&title=Data%20Teams%20as%20Support%20Teams)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-teams-as-support-teams-2bb1f1ed31b%2F&text=Data%20Teams%20as%20Support%20Teams)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/01/df-main.png)](https://towardsdatascience.com/from-dataslows-to-dataflows-the-gen2-performance-revolution-in-microsoft-fabric/)\n## [From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric](https://towardsdatascience.com/from-dataslows-to-dataflows-the-gen2-performance-revolution-in-microsoft-fabric/)\n[Data Engineering](https://towardsdatascience.com/category/data-science/data-engineering/)\nHow the latest Dataflow enhancements change the way we play the game\n[Nikola Ilic](https://towardsdatascience.com/author/datamozart/)\nJanuary 13, 20268 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2019/08/15wuTZb85g6XR4ftF6nONLw.png)](https://towardsdatascience.com/exploratory-data-analysis-dcb5e7189c4e/)\n## [Exploratory Data Analysis](https://towardsdatascience.com/exploratory-data-analysis-dcb5e7189c4e/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nIncludes a simple case study for better understanding.\n[Pratheesh Shivaprasad](https://towardsdatascience.com/author/iampratheesh/)\nAugust 15, 201910 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/06/0q0sy5Z6ypD17qUb5-scaled.jpg)](https://towardsdatascience.com/unique-data-is-better-than-a-perfect-model-6a2fabb51806/)\n## [Unique Data Is Better Than A Perfect Model](https://towardsdatascience.com/unique-data-is-better-than-a-perfect-model-6a2fabb51806/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nYour Goal As A Data Scientist Should Be To Build And Work With Proprietary Datasets\n[Tony Yiu](https://towardsdatascience.com/author/tonester524/)\nJune 15, 20214 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/09/06_wfQYaMjgvcIrg1-scaled.jpg)](https://towardsdatascience.com/the-most-underrated-r-packages-part-2-decdc753205c/)\n## [The Most Underrated R packages: Part 2](https://towardsdatascience.com/the-most-underrated-r-packages-part-2-decdc753205c/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA curated list of awesome and less known R libraries\n[Alessandro Arrigo](https://towardsdatascience.com/author/alearrigo/)\nSeptember 27, 20226 min read\n * [![](https://towardsdatascience.com/images/fallback-image.jpg)](https://towardsdatascience.com/the-three-most-important-statistical-tests-in-business-analytics-fd958a8e2a90/)\n## [The Three Most Important Statistical Tests in Business Analytics](https://towardsdatascience.com/the-three-most-important-statistical-tests-in-business-analytics-fd958a8e2a90/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nThere are more statistical tests than one can count; all of them have a reason to exist and mathematicians are paid to invent new ones all...\n[Elvis](https://towardsdatascience.com/author/the-elvis/)\nMarch 17, 20216 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/08/1rGElibzwzjvaO_5I60xg9g-scaled.jpeg)](https://towardsdatascience.com/how-to-convert-a-csv-file-into-an-xlsx-one-with-python-and-pandas-27aabc279d69/)\n## [How to Convert a CSV file into an XLSX one with Python and Pandas](https://towardsdatascience.com/how-to-convert-a-csv-file-into-an-xlsx-one-with-python-and-pandas-27aabc279d69/)\n[](https://towardsdatascience.com/data-teams-as-support-teams-2bb1f1ed31b/)\nAn example of how to use Python to convert files\n[Federico Trotta](https://towardsdatascience.com/author/federicotrotta/)\nAugust 9, 20223 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/001MCVG6eTE1iPZbV-scaled.jpg)](https://towardsdatascience.com/race-and-ethnicity-in-data-science-138acf1c23b4/)\n## [Race and Ethnicity in Data Science](https://towardsdatascience.com/race-and-ethnicity-in-data-science-138acf1c23b4/)\n[Race](https://towardsdatascience.com/category/society/race/)\nWhy it's important and how we should approach it\n[Lathan Liou](https://towardsdatascience.com/author/lathan-liou/)\nAugust 23, 20219 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/1kbtXtKWYD-3-SoeWH-91Q.png)](https://towardsdatascience.com/node2vec-explained-db86a319e9ab/)\n## [Node2Vec Explained](https://towardsdatascience.com/node2vec-explained-db86a319e9ab/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nExplaining & Implementing the Node2Vec Paper in Python\n[Vatsal](https://towardsdatascience.com/author/vatsal12-p/)\nJanuary 31, 202210 min read\n * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/12/1LiDgPrmooxiPHbsPmrnS5g-scaled.jpeg)](https://towardsdatascience.com/before-you-get-into-data-science-a-journey-from-a-non-it-or-non-engineer-degree-f789f192ee8b/)\n## [Before you get into Data Science: A journey from a non-IT or non-engineer degree](https://towardsdatascience.com/before-you-get-into-data-science-a-journey-from-a-non-it-or-non-engineer-degree-f789f192ee8b/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nHi there!\n[Cakramurti Ocha](https://towardsdatascience.com/author/cakraocha/)\nDecember 22, 20209 min read\n\n\n * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n * [X](https://x.com/TDataScience)\n * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n * [Threads](https://www.threads.net/@towardsdatascience)\n * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n#### Recommended Articles \nClose\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n", "body_base_path": "attachments/a7b0daa1a9c1/pages/", "body_markdown_path": "attachments/a7b0daa1a9c1/pages/archived-body.md", "local_html": "attachments/a7b0daa1a9c1/pages/archived-body.html", "body_source": "prior_capture" }