That title probably got your attention and now you think I have some explaining to do! The key word in the title is the word “A”. Self-service analytics isn’t a thing if “a thing” means a single, distinct corporate initiative or set of requirements. Many companies make the mistake of assuming self-service is “a” thing. In reality, it is many things to many people, depending on their skill level and role. In other words, self-service is actually many things. Let’s discuss why!

該標題可能引起了您的注意，現在您認為我需要做一些解釋！ 標題中的關鍵詞是單詞“ A”。 如果“事物”表示單個，不同的公司計劃或一組要求，那么自助服務分析就不是事物。 許多公司錯誤地認為自助服務是“一件事”。 實際上，對于許多人來說，這取決于許多人的技能水平和角色。 換句話說，自助服務實際上是很多事情。 讓我們討論原因！

自助服務分析的典型狹義定義 (The Typical Narrow Definition Of Self-Service Analytics)

Many organizations think of self-service analytics as being the enablement of business users to handle more analytics on their own. Even within this narrow view, self-service really isn’t “a” thing. It could be argued that companies are pursuing the single, consistent concept of “enabling business users to handle more analytics” . . . and at a high level that is true. However, different business users have different levels of skill and different needs, which leads to different self-service functionality for different people. Enabling business users to have self-service capabilities will require multiple different deployments with varying levels of functionality and complexity. Let’s look at what this means through a couple examples.

許多組織認為自助服務分析是使業務用戶能夠自行處理更多分析的功能。 即使在這種狹narrow的視野內，自助服務實際上也不是“一件事”。 可以爭辯說，公司正在追求一個統一的概念，即“使業務用戶能夠處理更多分析”。 。 。 在高水平上是正確的。 但是，不同的業務用戶具有不同的技能水平和不同的需求，這導致針對不同人員的不同自助服務功能。 要使業務用戶具有自助服務功能，將需要進行多種不同的部署，并具有不同級別的功能和復雜性。 讓我們通過幾個例子看看這意味著什么。

Self-service analytics for a marketing manager might mean having access to the ability to report on churn and marketing campaign results without assistance. It might also mean an ability to ask for updated churn or response model output to be generated for an upcoming campaign and for the results of those efforts to be automatically tracked. Self-service analytics for a manufacturing plant supervisor might mean being able to drill into the performance statistics of the equipment in the facility, view and update predictive maintenance scores, and identify patterns that appear abnormal. In both the marketing and manufacturing examples, the same underlying tools and technology might be used, but the specific self-service analytics, reports, and user interfaces created with those underlying tools and technologies will be very different.

對于營銷經理的自助服務分析可能意味著可以在沒有幫助的情況下獲得報告客戶流失和營銷活動結果的能力。 這也可能意味著能夠要求為即將到來的戰役生成更新的客戶流失或響應模型輸出，并能夠自動跟蹤這些工作的結果。 對于制造工廠主管的自助服務分析可能意味著能夠深入研究設施中設備的性能統計信息，查看和更新??預測性維護得分，并確定出現異常的模式。 在營銷和制造示例中，都可以使用相同的基礎工具和技術，但是使用這些基礎工具和技術創建的特定自助服務分析，報告和用戶界面將大不相同。

Once you think about what was stated in the prior paragraphs, it seems pretty obvious. However, it is not uncommon for organizations to discuss self-service capabilities as though it is a single set of needs until well into the planning process. If expectations and budgets are set under the assumption of a single self-service effort, then one of two things is likely to happen. Either:

一旦考慮了前面幾段中提到的內容，這似乎就很明顯了。 但是，對于組織自助服務功能的討論并不罕見，就好像在規劃過程中這是一組需求一樣。 如果期望和預算是在一次自助服務的假設下設定的，那么很可能會發生兩件事之一。 要么：

1. The budget and timeline are greatly exceeded once all of the different requirements are finally understood and implemented, or
   一旦最終理解并實施了所有不同的要求，預算或時間表就會大大超出，或者
2. What is achieved within the allocated time and budget is very much incomplete and underwhelming to the targeted user communities
   在分配的時間和預算范圍內所取得的成就非常不完整，并且對目標用戶群體毫無幫助

In either case, it is a black eye to the analytics and IT organizations.

無論哪種情況，對于分析和IT組織都是黑眼。

自助服務的范圍超過業務用戶，需要IT合作 (Self-Service Is Broader Than Business Users And Requires IT Partnership)

Even sophisticated users want the ability to have self-service, though it will mean different things to them. For example, data scientists want self-service capability when it comes to accessing data to feed their models and push out results. They don’t want to have to rely on IT to execute these core functions. Data engineers want self-service capabilities to access, ingest, and process a wide range of data on their own. They similarly don’t want to have to rely on IT for these basic functions.

即使是老練的用戶也希望擁有自助服務的能力，盡管這對他們而言意味著不同的事情。 例如，在訪問數據以饋送其模型并推出結果時，數據科學家需要自助服務功能。 他們不想依靠IT來執行這些核心功能。 數據工程師希望自助服務功能能夠自己訪問，提取和處理各種數據。 同樣，他們也不想依靠IT來實現這些基本功能。

Almost everyone across the entire supply chain of analytics wants self-service capabilities that make them more efficient in getting their work done. Users must actively partner with IT to work out the details. It isn’t fair to expect IT to have all the answers about what users need. Rather, users of all skill levels and roles need to let IT know what they are looking for so that IT can implement the right tools to enable that functionality. Only by accurately capturing all of the requirements, and related investment needed to meet those requirements, can realistic costs and timing for self-service implementations be determined.

整個分析供應鏈中的幾乎每個人都需要自助服務功能，這些功能可以使他們更高效地完成工作。 用戶必須積極與IT合作制定細節。 期望IT獲得有關用戶需求的所有答案是不公平的。 而是，各種技能和角色的用戶都需要讓IT知道他們在尋找什么，以便IT可以實施正確的工具來實現該功能。 只有準確地捕獲所有需求以及滿足這些需求所需的相關投資，才能確定實現自助服務的實際成本和時機。

The ability to have self-service efforts viewed as a success will also increase as people become comfortable their needs have been heard and will be met, even if it will occur over time and in phases. Solid partnering with IT is as important as ever when it comes to self-service.

自助服務被視為成功的能力也將隨著人們逐漸適應并滿足他們的需求而增強，即使這種需求會隨著時間的推移而逐步出現。 在自助服務方面，與IT保持牢固的合作關系比以往任何時候都重要。

自助服務仍不完全自給自足！ (Self-Service Is Still Not Full Self-Sufficiency!)

Another mistake to avoid is the assumption that once self-service capabilities are implemented, the need for support will drop substantively. The reality is that someone will still need to provide training and ongoing technical support for the self-service environments. It is also necessary to have resources available for the inevitable changes and additions that are needed once the environments are rolled out. Aside from that, many issues will arise that simply can’t be addressed solely through a self-service environment. The need for custom and / or more complex processes will always be there.

另一個要避免的錯誤是假設一旦實現了自助服務功能，對支持的需求就會大大下降。 現實情況是，仍然有人需要為自助服務環境提供培訓和持續的技術支持。 部署環境后，還必須有可用的資源來進行不可避免的更改和添加。 除此之外，還會出現許多無法僅通過自助服務環境解決的問題。 始終需要定制和/或更復雜的流程。

With a robust self-service infrastructure, more and better analytics can be produced. However, it is unrealistic to expect that everything can be handled through self-service. Neither the users nor their IT support resources should make the mistake of thinking that self-service and self-sufficiency are one and the same.

借助強大的自助服務基礎架構，可以產生更多，更好的分析。 但是，期望一切都可以通過自助服務來處理是不現實的。 用戶及其IT支持資源都不應誤以為自助服務和自給自足是一回事。

In summary, self-service is not “a” thing but a collection of things. Self-service does not equate to self-sufficiency. Self-service can only succeed through a strong user base partnership with IT. In the end, by keeping realistic expectations and working together, organizations can achieve a significant degree of progress toward creating more and better analytics through multiple, user-targeted self-service initiatives.

總而言之，自助服務不是“一件事”，而是一系列事物。 自助服務不等于自給自足。 自助服務只有通過與IT強大的用戶基礎合作才能成功。 最后，通過保持切合實際的期望并共同努力，組織可以通過多個以用戶為目標的自助服務計劃，在創建更多更好的分析方面取得重大進展。

Originally published by the International Institute for Analytics

最初由 國際分析學會(International Institute for Analytics)發布