To create the maximum value, urgency, and leverage in a data partnership, you must present the data available for sale or partnership in a clear and comprehensive way. Partnerships are based upon the concept that you are offering value for value, whether paid or traded. Friendship might need no reasons, but partnerships require some understanding of the exchange of value.

要在數據合作伙伴關系中創造最大價值，緊迫性和杠桿作用，您必須以清晰，全面的方式顯示可用于銷售或合作伙伴關系的數據。 伙伴關系基于您提供物有所值的概念，無論是付費還是交易。 友誼可能不需要任何理由，但是伙伴關系需要對價值交換有所了解。

The most common way to demonstrate the value of data is to share three different files or documents, each with a slightly different view of your data assets. The first file, called the data brief, is a presentation or document describing your data assets highlighting their best qualities and potential uses, or case studies of actual use. Your best strategy is to present this in person or, at if that’s not possible, in a conversation. The second file is a comprehensive document called a data catalog that outlines all of the facts about your data assets. The last file is a sample data file to assist would-be partners to test your data. The brief, catalog, and sample files need to represent accurately and clearly the value of your data.

證明數據價值的最常見方法是共享三個不同的文件或文檔，每個文件或文檔的數據資產視圖略有不同。 第一個文件稱為數據摘要，是一個描述或文檔，描述了您的數據資產，突出顯示了它們的最佳質量和潛在用途，或實際用途的案例研究。 最好的策略是親自展示，或者在不可能的情況下通過對話展示。 第二個文件是一個稱為數據目錄的綜合文檔，概述了有關數據資產的所有事實。 最后一個文件是樣本數據文件 ，可幫助潛在的合作伙伴測試您的數據。 簡要，目錄和樣本文件需要準確，清楚地表示數據的價值。

Companies that fail to present data assets effectively will also fail to attract partnerships and receive fair compensation for their data. The value of your data is not obvious — just ask anyone in your company who doesn’t directly work with a given dataset to evaluate its purpose and worth to see what we mean. And those who do work directly with the data often can’t give a solid business case, either. This means, unfortunately, that those closest to the data, meaning you and your team, are also those most likely to become frustrated with a potential partner’s misunderstanding the value of your data. To prevent that frustration, invest the time to create the brief, the catalog, and the sample file.

未能有效展示數據資產的公司也將無法吸引合作伙伴，也不會為其數據獲得公平的補償。 數據的價值并不明顯-只需讓公司中不直接使用給定數據集的任何人評估其目的并值得了解我們的意思。 而那些誰直接做的工作與數據往往不能給出一個穩固的業務情況下，無論是。 不幸的是，這意味著那些最接近數據的人，也就是您和您的團隊，也最有可能因潛在合作伙伴誤解您的數據價值而感到沮喪。 為避免這種麻煩，請花時間創建摘要，目錄和示例文件。

證明數據價值的三個文件 (The Three Files That Prove the Value of your Data)

The brief, the initial presentation file to highlight your data assets, need not be very long. To get potential customers or data partners to value the data appropriately, you should present it in simple terms, with appealing visuals that highlight the data assets clearly. Show the data at its most interesting, intriguing, exciting.

簡短的初始演示文件可以突出顯示您的數據資產，不需要很長。 為了使潛在的客戶或數據合作伙伴適當地評估數據，您應該以簡單的術語來呈現它，并用吸引人的視覺效果清晰地突出顯示數據資產。 以最有趣，最有趣，最令人興奮的方式顯示數據。

The brief needs to start with a simple definition of your dataset that describes it clearly, for example:

簡要說明需要從對數據集的簡單定義開始，以清楚地描述它，例如：

• The full semantic breakdown of the United States Patent library.
  美國專利圖書館的完整語義分類。
• The only user-generated, real-time gas price tracking platform.
  唯一的用戶生成的實時天然氣價格跟蹤平臺。
• The deepest collection of opt-in consumer profiles for luxury sales.
  參與奢侈品銷售的最深入的選擇加入的消費者檔案。
• The highest resolution, hourly drone footage of US retail store locations.
  美國零售商店位置的最高分辨率，每小時無人機畫面。

Each of these sentences describe real datasets in their most appealing value terms. Focusing on the most important attributes like “user-generated,” “real-time,” or “highest resolution” are meant to highlight how they are both unique and valuable.

這些句子中的每一個都以其最吸引人的價值術語描述了真實的數據集。 關注“用戶生成”，“實時”或“最高分辨率”等最重要的屬性是為了突出它們的獨特性和價值。

From here, your presentation should draw out each major section of your data catalog, which is the second file you must create. Use striking graphics or “hero” numbers that clearly demonstrate the highlights in your data, such as the total number of records, how often the data is updated, quality metrics, and geographic or sector breakdowns. Each dataset has its own unique highlights.

從這里，您的演示文稿應該繪制出數據目錄的每個主要部分，這是您必須創建的第二個文件。 使用醒目的圖形或“英雄”數字清楚地顯示數據中的亮點，例如記錄總數，數據更新頻率，質量指標以及地理或部門細分。 每個數據集都有其獨特的亮點。

In the second file, the data catalog, key sections include the Initial Data Definition, the Method of Data Collection, the Refinement Process, the Commitment of Quality, and the Coverage, Fill Rates, and Refresh Rates per major data field. Your partner will appreciate it if you include a Field Definitions Library at the end of the document, showing each field available, its data format (number, Boolean, date, currency, or text string, for example), its fill quantity (the percentage of records in which the field is filled), and a brief definition of what it conveys.

在第二個文件中，數據目錄的關鍵部分包括“初始數據定義”，“數據收集方法”，“細化過程”，“質量承諾”以及每個主要數據字段的覆蓋率，填充率和刷新率。 如果您在文檔末尾包含一個字段定義庫，它會顯示每個可用字段，其數據格式(例如數字，布爾值，日期，貨幣或文本字符串)，填充量(百分比)，那么您的合作伙伴將不勝感激。填充該字段的記錄)，以及對其傳達的內容的簡要定義。

Your business and data teams should outline the initial brief as well as the data catalog, and your marketing department or agency should design them. Of course, for many companies, that means a single person doing the work, and so be prepared to allocate the resources necessary to give that person the support needed. This investment in clearly articulating what data assets you have will drastically shorten the partnership or sales cycle, immediately showcasing your data assets at a higher level than most.

您的業??務和數據團隊應概述初始摘要以及數據目錄，營銷部門或代理商應設計它們。 當然，對于許多公司而言，這意味著只有一個人在從事這項工作，因此要做好分配必要資源的準備，以便為該人提供所需的支持。 明確說明您擁有哪些數據資產的這項投資將大大縮短合作伙伴關系或銷售周期，并立即以高于大多數的水平展示您的數據資產。

Many companies shortchange this effort and as result, never receive full value for their data. When you jump right to “let us send you a sample file” you miss the opportunity to build up your data value and control the dialogue. You also likely keep the decisionmaking at a lower level of authority with your prospective partner — a poorly explained proposal will get handed off to someone who isn’t a key decisionmaker, adding a level of delay and uncertainty to the process.

許多公司都縮短了工作量，結果是永遠無法獲得其數據的全部價值。 當您跳到“讓我們給您發送示例文件”時，您會錯過建立數據價值和控制對話的機會。 您也可能會與潛在的合作伙伴保持較低的決策權限-一個解釋不充分的建議會交給非關鍵決策者，這會增加流程的延遲和不確定性。

You should create the final file, the sample, dynamically based upon the circumstance. While it’s not usually necessary to customize the brief or the catalog, a custom sample data file will give each potential partnership its best chance at success. For example, for the drone footage company mentioned above, if they are meeting with a major electronics retailer, may want to specifically and only show similar footage or population density visuals for similarly situated electronics retailers. If the sample dataset has restaurants and laundromats in it, this will reveal shortcomings to the potential buyer or partner. They may show that the depth or coverage of the data file is inadequate, or maybe signal that the data owners are unable to deliver a custom data set quickly and in a format that is usable.

您應該根據情況動態創建最終文件(樣本)。 盡管通常不需要自定義簡介或目錄，但自定義示例數據文件將為每個潛在的合作伙伴提供最大的成功機會。 例如，對于上述無人機素材公司，如果他們要與一家主要的電子產品零售商會面，則可能希望專門為那些位置相似的電子產品零售商展示類似的素材或人口密度視覺效果。 如果樣本數據集中有餐廳和自助洗衣店，這將向潛在的買家或合作伙伴揭示缺點。 它們可能表明數據文件的深度或覆蓋范圍不足，或者可能表明數據所有者無法以可用格式快速交付自定義數據集。

Most companies that are considering a data partnership or the purchase of data assets will not have any patience for file delivery or formatting issues. This means that you should be prepared with a file prior to your first meeting that is likely to be appropriate to your audience, but also have a plan to quickly generate a different file if the meeting indicates a different need.

大多數正在考慮建立數據合作伙伴關系或購買數據資產的公司將對文件傳遞或格式問題沒有任何耐心。 這意味著您應該在第一次會議之前準備好可能適合您的聽眾的文件，但也要制定計劃，以便在會議表明有不同需求時快速生成另一個文件。

關系映射值 (Relationship Mapping Values)

The process of mapping relationships between your data values and those of the potential partner can cause countless misunderstandings and unnecessary delays. This is typically because each party has very unique ways of looking at a business or person or product in their data; they need a Rosetta Stone of sorts to understand your dataset in their own context. This is central to the entire data partnership strategy for a business because there always needs to be a way to “crosswalk” from one dataset to another. Here, we’ll show how to highlight your data catalog to streamline this process in each partnership discussion.

在數據值與潛在合作伙伴的數據值之間映射關系的過程可能會引起無數的誤解和不必要的延遲。 通常，這是因為各方都有非常獨特的方式來查看其數據中的企業或個人或產品； 他們需要某種Rosetta Stone才能在自己的環境中理解您的數據集。 這對于企業的整個數據合作伙伴關系戰略至關重要，因為始終需要一種從一個數據集“穿越”到另一個數據集的方法。 在這里，我們將在每次合作伙伴討論中展示如何突出顯示數據目錄以簡化此過程。

Data, in any form, is the reduction of the inputs from the world to values or ranges, so we can efficiently understand and analyze information. In other words, data is a description of, or the story of, our world. As such, every dataset can be related to any other dataset by identifying its key value when it comes to the classic questions of Who, What, When, Where, or Why. Just as in your first journalism class or writing class, these five factors are common ways to describe the world around us and to connect all of the elements of a story together.

任何形式的數據都是從世界輸入減少到值或范圍，因此我們可以有效地理解和分析信息。 換句話說，數據是對我們世界的描述或故事。 這樣，在涉及誰，什么，何時，何地或為什么的經典問題時，通過標識其關鍵值，可以將每個數據集與任何其他數據集相關聯。 就像在您的第一個新聞班或寫作班上一樣，這五個因素是描述我們周圍世界并將故事的所有要素聯系在一起的常用方法。

By asking the following questions, both of your own data assets, and then of your potential data partner, you can quickly come to a common language to compare and analyze your data.

通過問您自己的數據資產以及潛在的數據合作伙伴以下問題，您可以快速使用通用語言來比較和分析數據。

• Who is your data about?
  您的資料涉及誰？
• What is your data about?
  您的數據是關于什么的？
• When did your data occur or change?
  您的數據何時出現或更改？
• Where (what location) is your data about?
  您的數據在哪里(什么位置)？
• Why was your data created?
  為什么創建數據？

Is your data about people, products, or places? If so, each of those can be related to other databases with those as central themes. Consumer profiles, business locations, product codes, and medical reimbursement codes are all examples of common data anchors by which different datasets can be matched and compared. This means that Who, What, and Where are the most straightforward questions to answer, and you or your potential data partner can usually match corresponding data elements so that they can compare your data to theirs and analyze its value. Every data record needs to correspond to the same person, product, or place across datasets.

您的數據是關于人，產品或地點的嗎？ 如果是這樣，那么每個主題都可以與其他數據庫相關并以它們為中心主題。 消費者資料，營業地點，產品代碼和醫療報銷代碼都是通用數據錨點的示例，通過它們可以匹配和比較不同的數據集。 這意味著，誰，什么和哪里是最容易回答的問題，您或您的潛在數據伙伴通常可以匹配相應的數據元素，以便他們可以將您的數據與其數據進行比較并分析其價值。 每個數據記錄都需要在數據集中對應同一個人，產品或地點。

Time is another fantastic way to connect data assets. When two data sets don’t describe the same person, product, or place, time of occurrence or change is the next most likely area of correspondence. This is how drone footage and satellite imagery are tied to product sales at a retail store, for example. Both the photos of the cars or pedestrians in the parking lot of a retail store can be compared to transactional data from the store, because both have time stamps. While photos of parking lot density can’t directly be tied to a product SKU, they can be compared to the times a particular product or series of products is purchased. Time is a universal connector that powerfully connects seemingly disparate datasets.

時間是連接數據資產的另一種絕佳方式。 當兩個數據集無法描述同一個人，產品或地點時，發生或更改的時間是下一個最可能的對應區域。 例如，這就是將無人機畫面和衛星圖像與零售商店的產品銷售聯系起來的方式。 零售商店的停車場中的汽車或行人的照片都可以與商店的交易數據進行比較，因為兩者都有時間戳。 雖然不能將停車場密度的照片直接與產品SKU相關聯，但可以將其與購買特定產品或一系列產品的時間進行比較。 時間是一個通用連接器，可以有效地連接看似完全不同的數據集。

The last and most complicated data relationship to map, in both data and in writing, is around the question of “why.” The best way to think about how this matches up with other datasets is to try to match up sentiment or indications of interest around an event, product, or service. For example, many firms like Yelp semantically extract the sentiment of reviews left by patrons at restaurants and hotels, sentiments that can help answer the question of “why.” When a reviewer of a restaurant leaves a comment like, “You have to try the 1-pound meatball appetizer, it’s amazing,” they can relate the object of the meatball to a positive consumer experience and the subsequent recommendation of that business to others. This is one of the hardest elements of data collection, but the increasingly easy access to user interest and sentiment through mobile phone apps has created a whole new world of “why” relationship mapping.

在數據和書面形式上，映射的最后也是最復雜的數據關系是圍繞“為什么”的問題。 考慮這與其他數據集如何匹配的最好方法是嘗試圍繞事件，產品或服務來匹配情緒或興趣指示。 例如，許多類似Yelp的公司從語義上提取了顧客在餐館和酒店留下的評論情緒，這些情緒可以幫助回答“為什么”的問題。 當餐廳的評論者留下諸如“您必須嘗試1磅重的丸子開胃菜，真是太神奇了”的評論時，他們可以將丸子的目標與積極的消費者體驗聯系起來，并將該業務隨后推薦給其他人。 這是數據收集中最困難的要素之一，但是通過手機應用程序越來越容易獲得用戶的興趣和情感，這為“為什么”關系映射開辟了一個全新的世界。

時間數據 (Data Through Time)

When presenting data assets, reviewing changes over time is a powerful magnifier of value. Charts of daily, weekly, monthly, quarterly, or annual shifts put your data assets into a common perspective. Most data briefs and data catalogs show the growth of their data assets — records, fields, or fill rates — as time-based series. Since most data collection efforts are cumulative, they grow over time, which attractively demonstrates their value. For this reason, you should identify each dataset you have, how many, and when they were created to make this very clear. Charting growth over time can also be a great way to highlight that your lead over competing data suppliers, and how long it would take to recreate your data at the same scale. For this reason, your brief and data catalog should convey how your scale or head-start create dominance in your space.

在顯示數據資產時，回顧隨時間的變化是一個強大的價值放大器。 每日，每周，每月，每季度或每年的變化圖表將您的數據資產置于一個共同的角度。 大多數數據摘要和數據目錄以時間為基礎的系列顯示其數據資產(記錄，字段或填充率)的增長。 由于大多數數據收集工作都是累積性的，因此它們會隨著時間的推移而增長，這很有吸引力地證明了它們的價值。 因此，您應該確定每個數據集，數量以及創建時間，以使其更加清晰。 繪制一段時間內的增長圖表也可以很好地說明您在競爭數據供應商方面的領先優勢，以及以相同規模重建數據需要花費多長時間。 因此，您的簡要和數據目錄應該傳達您的規模或先發優勢如何在您的空間中創造支配地位。

Another important temporal view of your data will reveal changes or updates to your data and when they occur. To highlight just how dynamic your data assets are, create scatter plot diagrams that show fields, how often they update, and to what magnitude they change. This insight can visually help a potential partner to understand not just the need for your data, but the need for refreshing their feed of data from you on a timely basis. Some data suppliers will want to convey this early on in a discussion, because their potential data partners will have trouble ingesting data frequently. Partners’ legacy corporate data structures and slow compilation practices can create barriers where, even if your data is amazingly useful, a data partner may not be able to ingest it fast enough to use it properly. This is why a chart of the frequency of updates is very helpful early, enabling you to demonstrate your capabilities to a potential data partner. Frequency charts also help your partner understand just how large of a data inflow they may be purchasing — It is a sad discussion when a data partnership is abandoned after weeks of discovery only because the receiving party realizes they can’t ingest the fire hose of content you might be able to provide.

數據的另一個重要時態視圖將揭示數據的更改或更新以及發生的時間。 要突出顯示數據資產的動態性，請創建散點圖，以顯示字段，字段更新的頻率以及更改的幅度。 這種見解可以從視覺上幫助潛在的合作伙伴不僅了解您的數據需求，而且還需要及時刷新您的數據源。 一些數據提供者將希望在討論中盡早傳達這一點，因為它們的潛在數據合作伙伴將難以頻繁提取數據。 合作伙伴的舊公司數據結構和緩慢的編譯實踐可能會造成障礙，即使您的數據非常有用，數據合作伙伴也可能無法足夠快地提取數據以正確使用它。 這就是為什么更新頻率圖表在早期非常有用的原因，它使您能夠向潛在的數據合作伙伴展示自己的能力。 頻率圖表還可以幫助您的合作伙伴了解他們可能購買了多少數據流入—當發現數周后放棄數據合作伙伴關系而僅僅是因為接收方意識到他們無法攝取內容的消防水帶時，這是一個悲傷的討論您可能能夠提供。

We mentioned the need for storing the date and time for each update in your data. This is where that effort really shines in the data partnership strategy. Work with your technology teams to ensure that creation, updates, changes, confirmations, and deletions of every data field are tracked and time-stamped. This will be a hallmark of your data value presentation.

我們提到需要在數據中存儲每次更新的日期和時間。 這就是這種努力真正體現在數據合作伙伴戰略中的地方。 與您的技術團隊合作，以確保跟蹤和標記每個數據字段的創建，更新，更改，確認和刪除。 這將是您的數據值表示的標志。

Ultimately, the vale of a dataset is only apparent when explained, because anyone who has not directly worked with the data will only recognize the value that is surface-level. It takes time, energy, and creativity to produce an explanation of data that will convince a prospective partner of value. But the investment in making the data brief, data catalog, and sample data file will be well worth the resources required. A weak presentation has been the downfall of many a potential partnership; a strong presentation goes a long way to showing why your data is worth your partner’s time, and yours.

最終，數據集的價值只有在解釋時才顯而易見，因為任何未直接處理數據的人都只會識別表面值。 對數據進行解釋需要花費時間，精力和創造力，這些數據才能使潛在的合作伙伴信服價值。 但是，使數據簡短，數據目錄和樣本數據文件方面的投資非常值得所需的資源。 表現不佳是許多潛在伙伴關系的失敗。 出色的演示對顯示為什么您的數據值得您的伴侶和您的伴侶的時間大有幫助。

Originally published at https://wardpllc.com on September 4, 2020.

最初于 2020年9月4日 在 https://wardpllc.com 上 發布 。

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