如何通过数据管理辅助决策者_是决策者被现代数据栈所取代 2020年未来数据 (Future Data 2020) 办公空间 (Office Space) 现代数据球拍 (The Modern Data Racket) 第二定律 (The Second Law) 所以现在怎么办？ (So… What now?)

2020年未来数据 (Future Data 2020)

Earlier this week, I (virtually) attended Future Data 2020, a conference about the next generation of data systems. During the conference, I watched an interesting talk given by Tristan Handy, founder and CEO of Fishtown Analytics, called The Modern Data Stack: Past, Present, and Future. During the talk, Tristan discussed a so-called Cambrian explosion of data products built upon data warehouses, such as Amazon Redshift, between 2012 and 2016, as well as his opinion that we are on the precipice of a similar paradigm shift, which he referred to as “the second Cambrian explosion.”

本周初，我(虚拟地)参加了Future Data 2020，这是有关下一代数据系统的会议。 在会议期间，我观看了Fishtown Analytics创始人兼首席执行官Tristan Handy的有趣演讲，题为“现代数据栈：过去，现在和未来”。 在谈话中，Tristan讨论了2012年至2016年之间建立在数据仓库(例如Amazon Redshift)上的所谓寒武纪数据产品爆炸，以及他认为我们处于类似范式转变的悬崖上，他提到被称为“第二次寒武纪爆炸”。

Tristan’s perspective on the modern data stack provided much food for thought; however, the topic I want to explore here stems from a brief comment made about the future of self-service in data-driven decision-making: How those who are not necessarily on the cutting-edge of data science can best leverage their data to make informed decisions.

特里斯坦(Tristan)对现代数据栈的观点为我们提供了很多思考的机会。 但是，我想在此探讨的主题源于对数据驱动型决策中自助服务的未来的简短评论： 那些不一定处于数据科学前沿的人们如何才能最好地利用其数据来做出明智的决定。

办公空间 (Office Space)

To start this exploration, I will first give a simplified version of a past during which I was not of working age and with which I therefore have no direct experience: Prior to the aforementioned (first) Cambrian explosion, data analysis was primarily carried out using spreadsheets, such as (of course) Microsoft Excel. In many theoretical offices in the 90s and 00s, countless nameless and faceless theoretical analyst/decision-makers spent their Mondays through Fridays bouncing among tens of tens of Excel spreadsheets, adding calculated fields in two-lettered columns and introducing errors for which there would be no record; it was a laugh riot, the analyst/decision-makers earned decent theoretical wages for their time spent, and everyone watched Friends in the evenings without feeling obligated to discuss how problematic it was.

为了开始这一探索，我将首先给出一个过去的简化版本，在此期间我没有工作年龄，因此我没有直接的经验：在上述(第一次)寒武纪爆炸之前，数据分析主要是使用电子表格，例如Microsoft Excel。 在20世纪90年代和20年代的许多理论办公室中，无数不具名的和不露面的理论分析员/决策者将他们的星期一至星期五花在数十个Excel电子表格中，在两个字母的列中添加计算字段，并引入了可能会出现的错误。无记录; 那是一场大闹的骚动，分析师/决策者在他们的时间上赚取了可观的理论工资，每个人都在晚上看《 Friends》而没有义务讨论它有多麻烦。

In more recent times, with the advent of modern data warehouses, data storage was able to be better separated from data analysis, and many, many SaaS companies profited off this division on scales not easily understood by humans. So rather than the happy-go-lucky Friends’ era paradigm, with data tabulated in one program with nice little cells and able to be analyzed in that same program by analyst/decision-makers, a number of new business intelligence platforms began to make their way into offices, raining on everyone’s parade, and just because the new guy attended a “conference” about the “future” in “Des Moines.”

在最近的时间里，随着现代数据仓库的出现，能够更好地将数据存储与数据分析分离，并且许多 SaaS公司从这种区分中获利，而这种规模是人类不容易理解的。 因此，与其用快乐的朋友时代作为范例，在一个程序中将数据列表化为漂亮的小单元格，然后由分析师/决策者在同一程序中进行分析，许多新的商业智能平台开始出现他们进入办公室的路上，每个人的游行队伍都在下雨，只是因为新来的人参加了关于“得梅因”的“未来”的“会议”。

现代数据球拍 (The Modern Data Racket)

Let me take a step back: In or around his talk (source), Tristan made the following comments:

让我退后一步：在或围绕他的谈话( 源 )，特里斯坦提出以下意见：

“How do you democratize self-service? Controversial, but I believe the Modern Data Stack disempowered many decision-makers. Those comfortable w/ Excel feel cut off from the source of truth. What if the spreadsheet interface is actually the correct way?”

“您如何使自助服务民主化？ 有争议，但我相信现代数据栈使许多决策者无能为力。 那些精通Excel的人感到与真相截然不同。 如果电子表格界面实际上是正确的方法怎么办？”

Tristan prefaces his claim that the modern data stack has disempowered decision-makers with the warning that his opinion may be controversial, but I would argue that his statement is not controversial at all, mostly because it is unarguably true. With the shift of data storage and analysis away from the flexible and easy-to-use spreadsheet and toward ecosystems such as data lakes, data rivers, data abysses, and data Charybdises, end-users (i.e., the analyst/decision-makers of yore), many of whom primarily use data tools as a means to an end, have likely lost their way.

特里斯坦(Tristan)在其声称现代数据栈已削弱决策者权能的序言之前，警告说他的观点可能是有争议的，但我要指出，他的说法根本没有争议，主要是因为它的说法无疑是正确的。 随着数据存储和分析从灵活易用的电子表格向数据湖，数据河，数据深渊和数据夏洛狄斯等生态系统转移，最终用户(即，分析人员/决策制定者)过去)，其中许多人主要使用数据工具作为最终手段，但很可能迷路了。

Put simply, it is not as simple to navigate the modern data stack as it was to navigate acres of spreadsheets. Spreadsheets, with all of their flaws, have almost no learning curve: if you can turn on a computer and open a file, you can navigate a spreadsheet. Furthermore, from the start, you are only a few clicks, keystrokes, and neural connections away from mastering formulas, pivot tables, and visualizations. I hate spreadsheets! — but they are a near-perfect balance of usability and flexibility.

简而言之，浏览现代数据栈并不像浏览英制电子表格那样简单。 具有所有缺陷的电子表格几乎没有学习曲线：如果您可以打开计算机并打开文件，则可以浏览电子表格。 此外，从一开始，您只需单击几下，进行击键和建立神经关系，而无需掌握公式，数据透视表和可视化。 我讨厌电子表格！ -但是它们几乎是可用性和灵活性之间的完美平衡。

In contrast, while modern business intelligence tools may be built with end-users with varying levels of technical expertise in mind, they tend to have a steeper learning curve. For example, while today’s decision-maker, now stripped of his or her ‘analyst’ status, can likely navigate a dashboard and make decisions based on the information presented, he or she has lost the almost-tactile experience of sifting through the data with his or her own hands.

相比之下，尽管现代商业智能工具可能是针对最终用户而设计的，但他们具有不同水平的技术专长，但它们的学习曲线往往更陡峭。 例如，尽管如今的决策者现在可以摆脱其“分析师”身份，可以导航仪表板并根据所显示的信息做出决策，但他或她已经失去了在触摸屏上浏览数据的几乎触觉体验。他或她自己的手。

第二定律 (The Second Law)

I know what you are thinking—literal metric tons of decisions were made based on little more than a pie chart from an hours-long presentation that was not put together by the person who had final say in the decision-making process—but please allow me to employ the above generalization to support my next point: Every new data technology moves the decision-maker further downstream from the data source.

我知道您在想什么-数吨的决策仅基于一个小时的演示文稿中的饼图做出，而决策过程中没有最终决定权的人没有将其汇总在一起-但请允许我将采用以上概括来支持我的下一个观点：每种新的数据技术都会使决策者从数据源向下游移动。

Today, the data required by a decision-maker may be located in a neatly designed dashboard, on physical servers, somewhere in the cloud, and/or on the backs of napkins, they may have underwent various transformations and exist in several slightly different forms of varying accuracy and transparency, and most likely, that decision-maker does not know which of these sources contains the data he or she needs to make an optimal decision, nor the processing those data underwent; it is complete chaos, and not the fun and festive Bacchian kind (and I haven’t even spoken of the inherent fuzziness of data).

如今，决策者所需的数据可能位于设计整齐的仪表板中，物理服务器上，云中的某个地方和/或餐巾纸的背面，它们可能已经过各种转换，并且以几种略有不同的形式存在准确性和透明度各不相同，而且决策者很可能不知道这些来源中的哪个包含他或她做出最佳决策所需的数据，也不知道对这些数据进行了处理； 它是完全混乱的，而不是有趣和欢乐的巴克式风格(而且我什至没有提到数据固有的模糊性)。

The more technologies a company implements in its data stack, the more points there are for potential misunderstandings, and the more training individual decision-makers have to undergo to become fluent in the data stack on which they rely. In other words, decision-makers are being disempowered by the increasing complexity of the modern data stack.

公司在其数据堆栈中实施的技术越多，潜在的误解就越多，并且各个决策者必须接受更多的培训才能熟练使用他们依赖的数据堆栈。 换句话说，现代数据堆栈的复杂性越来越高，决策者的权力也随之降低。

所以现在怎么办？ (So… What now?)

I see three possible solutions to the problem of disempowerment:

我看到解决权力不足问题的三种可能的解决方案：

• Stop trying to reinvent the wheel and keep spreadsheets around for the long haul;
  停止尝试重新发明轮子，并将电子表格保留很长一段时间；
• Hire decision-makers who are prepared to use and keep up with the modern data stack; or
  聘请愿意使用并跟上现代数据堆栈的决策者； 要么
• Promote close collaboration between data experts and decision-makers to support decision-making.
  促进数据专家与决策者之间的紧密合作，以支持决策。

All three options have pros and cons, but I am personally a fan of the third. In the last decade or so, there has been a rapid increase in our ability to store and manipulate data, and spreadsheets alone cannot be expected to fulfill all modern data needs. Similarly, in many industries, decision-makers alone cannot be expected to stay on the cutting-edge of data science. Therefore, it follows that close collaboration between data experts and decision-makers is becoming increasingly necessary in the modern office.

所有这三个选项都有优点和缺点，但我个人是第三个选项的粉丝。 在过去的十年左右的时间里，我们存储和处理数据的能力Swift提高，仅凭电子表格无法满足所有现代数据需求。 同样，在许多行业中，不能仅靠决策者来保持数据科学的前沿。 因此，随之而来的是，现代办公室中越来越需要数据专家与决策者之间的紧密合作。

Alternatively, perhaps in time an easy-to-use tool will come along that can be used to both store and analyze data… Oh… wait… that’s the spreadsheet.

或者，也许会及时出现一个易于使用的工具，该工具可用于存储和分析数据……哦……等等……那就是电子表格。