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!

该标题可能引起了您的注意，现在您认为我需要做一些解释！ 标题中的关键词是单词“ A”。 如果“事物”表示单个，不同的公司计划或一组要求，那么自助服务分析就不是事物。 许多公司错误地认为自助服务是“一件事”。 实际上，对于许多人来说，这取决于许多人的技能水平和角色。 换句话说，自助服务实际上是很多事情。 让我们讨论原因！

自助服务分析的典型狭义定义 (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.

许多组织认为自助服务分析是使业务用户能够自行处理更多分析的功能。 即使在这种狭narrow的视野内，自助服务实际上也不是“一件事”。 可以争辩说，公司正在追求一个统一的概念，即“使业务用户能够处理更多分析”。 。 。 在高水平上是正确的。 但是，不同的业务用户具有不同的技能水平和不同的需求，这导致针对不同人员的不同自助服务功能。 要使业务用户具有自助服务功能，将需要进行多种不同的部署，并具有不同级别的功能和复杂性。 让我们通过几个例子看看这意味着什么。

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.

对于营销经理的自助服务分析可能意味着可以在没有帮助的情况下获得报告客户流失和营销活动结果的能力。 这也可能意味着能够要求为即将到来的战役生成更新的客户流失或响应模型输出，并能够自动跟踪这些工作的结果。 对于制造工厂主管的自助服务分析可能意味着能够深入研究设施中设备的性能统计信息，查看和更新​​预测性维护得分，并确定出现异常的模式。 在营销和制造示例中，都可以使用相同的基础工具和技术，但是使用这些基础工具和技术创建的特定自助服务分析，报告和用户界面将大不相同。

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:

一旦考虑了前面几段中提到的内容，这似乎就很明显了。 但是，对于组织自助服务功能的讨论并不罕见，就好像在规划过程中这是一组需求一样。 如果期望和预算是在一次自助服务的假设下设定的，那么很可能会发生两件事之一。 要么：

1. The budget and timeline are greatly exceeded once all of the different requirements are finally understood and implemented, or
   一旦最终理解并实施了所有不同的要求，预算或时间表就会大大超出，或者
2. What is achieved within the allocated time and budget is very much incomplete and underwhelming to the targeted user communities
   在分配的时间和预算范围内所取得的成就非常不完整，并且对目标用户群体毫无帮助

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

无论哪种情况，对于分析和IT组织都是黑眼。

自助服务的范围超过业务用户，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.

即使是老练的用户也希望拥有自助服务的能力，尽管这对他们而言意味着不同的事情。 例如，在访问数据以馈送其模型并推出结果时，数据科学家需要自助服务功能。 他们不想依靠IT来执行这些核心功能。 数据工程师希望自助服务功能能够自己访问，提取和处理各种数据。 同样，他们也不想依靠IT来实现这些基本功能。

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.

整个分析供应链中的几乎每个人都需要自助服务功能，这些功能可以使他们更高效地完成工作。 用户必须积极与IT合作制定细节。 期望IT获得有关用户需求的所有答案是不公平的。 而是，各种技能和角色的用户都需要让IT知道他们在寻找什么，以便IT可以实施正确的工具来实现该功能。 只有准确地捕获所有需求以及满足这些需求所需的相关投资，才能确定实现自助服务的实际成本和时机。

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.

自助服务被视为成功的能力也将随着人们逐渐适应并满足他们的需求而增强，即使这种需求会随着时间的推移而逐步出现。 在自助服务方面，与IT保持牢固的合作关系比以往任何时候都重要。

自助服务仍不完全自给自足！ (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.

另一个要避免的错误是假设一旦实现了自助服务功能，对支持的需求就会大大下降。 现实情况是，仍然有人需要为自助服务环境提供培训和持续的技术支持。 部署环境后，还必须有可用的资源来进行不可避免的更改和添加。 除此之外，还会出现许多无法仅通过自助服务环境解决的问题。 始终需要定制和/或更复杂的流程。

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.

借助强大的自助服务基础架构，可以产生更多，更好的分析。 但是，期望一切都可以通过自助服务来处理是不现实的。 用户及其IT支持资源都不应误以为自助服务和自给自足是一回事。

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.

总而言之，自助服务不是“一件事”，而是一系列事物。 自助服务不等于自给自足。 自助服务只有通过与IT强大的用户基础合作才能成功。 最后，通过保持切合实际的期望并共同努力，组织可以通过多个以用户为目标的自助服务计划，在创建更多更好的分析方面取得重大进展。

Originally published by the International Institute for Analytics

最初由 国际分析学会(International Institute for Analytics)发布