In 1990 the Virginia based bank “Signet Bank” decided to trust two smart people, Richard Fairbanks and Nigel Morris, and make a major investment into data. They decided to turn the customer credit department into a large laboratory, “testing” out different kinds of credit terms on different credit taker characteristics and thus collected data for years.

1990年，弗吉尼亚州的“ Signet银行”银行决定信任两个聪明人，即Richard Fairbanks和Nigel Morris，并在数据方面进行了重大投资。 他们决定将客户信贷部门变成一个大型实验室，根据不同的信贷接受者特征“测试”不同种类的信贷条款，从而收集了多年的数据。

This was a huge investment, and the department “lost money” for quite some time. But what they were really doing was acquiring data, not just because they thought it’s a good investment, what Fairbanks and Morris collected was Good Data. Data integrated into a good data strategy aligned with the company strategy.

这是一笔巨大的投资，并且该部门“损失了很多时间”。 但是他们真正在做的是获取数据，不仅因为他们认为这是一项很好的投资，而且费尔班克斯和莫里斯收集到的都是好数据 。 将数据集成到与公司战略保持一致的良好数据 战略中 。

They collected data with the clear focus of improving the decision making capability of the credit department of Signet Bank.

他们收集数据的重点明确在于提高Signet Bank信贷部门的决策能力。

Good data is just that. Good data is what you have with a good data strategy. It is data you collect, clean/enrich/transform, make insightful, with the sole goal of improving decision making.

好的数据就是这样。 好的数据就是好的数据策略所具有的。 它是您收集，清理/丰富/转换，洞察力强的数据，其唯一目标是改善决策制定。

The problem only is, out there is a lot of Bad Data! You will see bad data everywhere you go. It is data touched for any other reason, without having a larger goal in mind.

唯一的问题是，那里有很多不良数据 ！ 随处可见错误数据。 它是出于其他任何原因而触动的数据，而没有考虑更大的目标。

I’m not sure it's fair to judge data just on this scale, but in my experience, it seems to be the only good measure. The only value in data is to improve the decision making capability of a company and thus help it take better actions. I do think this is a lesson that’s already been understood by many great thinkers. For instance, Provost & Facett make that point in “Data Science for Business: What you need to know about data mining and data-analytic thinking”.

我不确定仅以此规模判断数据是否公平，但以我的经验来看，这似乎是唯一的好方法。 数据的唯一价值是提高公司的决策能力，从而帮助公司采取更好的行动 。 我确实认为这是许多伟大的思想家已经理解的一课。 例如，Provost＆Facett在“ 商业数据科学：您需要了解的有关数据挖掘和数据分析思维的知识”中指出了这一点。

Fairbank & Morris understood this, applied it, and turned the little “Signet Bank” into Capital One, one of the largest banks in America. David Velez, co-founder of the billion-dollar Brazilian unicorn Nubank also intuitively understood this from the beginning. He is now translating his key insights into other markets around the world.

Fairbank＆Morris理解并运用了这一点，并将小小的“ Signet银行”变成了美国最大的银行之一Capital One。 拥有十亿美元资产的巴西独角兽公司努班克(Nubank)的联合创始人戴维· 韦雷斯( David Velez)从一开始就很直观地理解了这一点。 现在，他正在将他的主要见解转化为全球其他市场。

Now let’s see what Bad Data looks like and then walk through these two examples of Good Data.

现在，让我们看一下不良数据的外观，然后逐步介绍这两个良好数据的示例。

I’ve written this article to help you see the dramatic differences between Good Data and Bad Data. To help you see through the forest of techniques that can be applied in both contexts. To give you the chance to reexamine your data strategy and turn Bad Data into Good Data before your competition does it.

我写这篇文章是为了帮助您了解“好数据”和“坏数据”之间的巨大差异。 为了帮助您了解可以在这两种情况下应用的技术之林。 为了让您有机会在竞争之前重新检查数据策略并将不良数据转变为良好数据。

不良数据 (Bad Data)

There are a great many examples of Bad Data. Bad Data happens when you try to let the data shape the data strategy, not the other way around. Fairbanks and Morris didn’t hire a hundred data scientists to “come up with something great” or collected lots of random data throughout the company. They collected data with the goal of establishing a profitable credit division and then hired upon data scientists to use & collect the right data and support this strategy.

有许多不良数据的例子。 当您尝试让数据影响数据策略时，就会发生不良数据，而不是相反。 费尔班克斯(Fairbanks)和莫里斯(Morris)并没有聘请一百名数据科学家来“想出一些很棒的东西”，也没有在整个公司范围内收集大量随机数据。 他们收集数据的目的是建立有利可图的信贷部门，然后聘请数据科学家使用和收集正确的数据并支持该策略。

And albeit some evidence that some tech companies actually do simply “hire smart people to come up with something great” and are successful with it, I do feel that usually, they do have a data strategy behind it to back that up.

尽管有证据表明一些科技公司实际上只是在“聘请聪明的人来想出一些伟大的事情”并取得了成功，但我确实认为，通常来说，他们确实有数据策略来支持这一点。

Bad Data is ….

错误数据 是…。

… when a company invests in building up a data lake to “get insights from the data” without understanding whether anyone in the company will actually use this data to make better or faster decisions than before.

…当一家公司投资建立一个数据湖以“从数据中获取见解”而又不了解公司中的任何人是否会实际使用该数据来做出比以前更好或更快速的决策时。

… when a company hires some data scientists to “take advantage of big data”, without having actual outcomes in mind.

……当一家公司雇用一些数据科学家来“利用大数据”而又没有考虑到实际结果时。

…when a company hires a bunch of machine learning engineers & data scientists to “come up with something great”, without integrating them into their company vision.

…当一家公司雇用大量机器学习工程师和数据科学家来“想出一些很棒的东西”时，却没有将他们纳入公司的愿景。

…when a company decides to build a “data mesh” because the central data collecting team is having problems, without understanding whether this will result in better actions takes by its employees.

…当一家公司由于中央数据收集团队遇到问题而决定建立“数据网格”时，却不了解这是否会导致其员工采取更好的行动。

…when an analytics department produces report upon report because people ask for them, without understanding what people do with these reports differently than what they did before.

……当分析部门由于人们要报告而产生报告报告时，却不了解人们对这些报告的处理方式与以前不同。

…when a machine learning team spends a quarter building the latest recommendation engine for the website, without thinking about whether a simple compiled “Top items” list would do just as fine.

……当机器学习团队花了四分之一的时间为网站构建最新的推荐引擎时，却没有考虑过一个简单的已编译的“热门项目”列表是否也可以做到。

…when a marketing department installs the latest marketing automation tool, without already having lots of e-mail campaigns and data in place.

…当市场营销部门安装最新的市场营销自动化工具时，却没有大量的电子邮件活动和数据。

…when a company spends a quarter to upgrade to the latest version of “AwesomeDataTool.X” to “follow the pace of technology”, without understanding how people use their AwesomeDataTool to decide.

…当一家公司花了四分之一的时间升级到最新版本的“ AwesomeDataTool.X”以“跟随技术步伐”时，却不了解人们如何使用他们的AwesomeDataTool进行决策。

… it’s not Good Data. Good Data is derived from a data strategy, integrated into the company strategy ….

……这不是好数据。 优质数据源自数据策略，并已整合到公司策略中……

Signet银行(然后是Capital One)的优质数据 (Good Data at Signet Bank (and then Capital One))

All the techniques mentioned above resulting in Bad Data can be used to result in Good Data. And maybe in your case they are. But to get to see if that is true, you’ve got to ask why, why, and why.

上面提到的所有导致不良数据的技术都可以用来产生良好数据。 也许就您而言。 但是要弄清这是否成立，您必须问为什么，为什么以及为什么 。

To get Good Data, you have to start from the top. Start with the data strategy, the company strategy really. You have to start with the goal of taking better actions, making better decisions, and then work our way down the road. What does this road look like? Nowadays I tend to think about the road of data really like the flow used by ThoughtWorks:

要获得良好数据，您必须从头开始。 首先从数据策略开始，然后是公司策略。 您必须以采取更好的行动，做出更好的决策为目标，然后再继续前进。 这条路是什么样的？ 如今，我倾向于考虑数据之路，就像ThoughtWorks使用的流程一样：

So the stages, if we start with our goal “better actions” are:

因此，如果我们从目标“更好的行动”开始，这些阶段就是：

• Taking better actions

  采取更好的行动

• Making the decision to take a specific action

  决定采取特定行动

• Having the insights available to make a specific decision

  掌握可用于做出特定决定的见解

• Having the information to derive insights.

  具有信息以获取见解。

• Having collected the data to transform it into information.

  收集数据以将其转换为信息。

• (Having actions emit data to let it be collected… which makes the circle full.)

  (具有动作的数据会被收集起来……使圆圈充满了。)

If we start to analyze the Signet Bank case, we start with the data strategy. In this case, it’s “collecting & experimenting with credit loans to get enough information to skim off the profitable credits larger banks won’t offer”.

如果我们开始分析Signet Bank案例，我们将从数据策略开始。 在这种情况下，它就是“ 收集并尝试信贷贷款，以获取足够的信息以撇除大型银行不会提供的可获利信贷 ”。

This was a pretty unique perspective at the time. In the 1980s the credit market was revolutionized by automatic default probability calculations. As such, credit offerings were kept at a standard rate for the people with low default probability. But in 1990 Fairbanks and Morris decided it was time to bet on both, price discrimination, or in other terms offering different credit terms to different people and a focus on profitability, not just default probability.

当时，这是一个非常独特的观点。 在1980年代，信贷市场因自动违约概率计算而发生了革命。 这样，信贷违约概率就保持在标准利率之内。 但是在1990年，费尔班克斯(Fairbanks)和莫里斯(Morris)决定是时候押注价格歧视，或者用其他术语为不同的人提供不同的信用条件，并着重于获利能力，而不仅仅是违约概率。

So in essence they decided that in the mass of people with a higher default probability there were still profitable loans out there! And in the mass of people with low default probability, there were more profit options available as a lot of these people were actually a losing bet for the credit departments.

因此，从本质上讲，他们认为，在存在较高违约概率的人群中，仍有大量可获利的贷款！ 在违约概率较低的人群中，有更多的获利选择，因为其中许多人实际上是信贷部门的输家。

So we can derive the action, the action Signet Bank was taking and wanted to improve as…

因此，我们可以得出Signet Bank正在采取的行动，并希望将其改进为……

Action — “Offer more profitable loans”. Just as Fairbanks and Morris identified they need to focus on both profitability & default probability. The decision in question then becomes …

行动-“提供更多有利可图的贷款”。 正如费尔班克斯(Fairbanks)和莫里斯(Morris)确定的那样，他们需要同时关注盈利能力和违约概率。 这样的决定就变成……

Decision — “What kind of customer do I offer what kind of loan?”. In the 1980s this decision was simply made based on the insight that some people have higher default probabilities and some lower. What Signet Bank now sought for was a different insight…

决策-“我向什么样的客户提供什么样的贷款？ ”。 在1980年代，仅基于一些人具有较高的违约概率而某些人具有较低的违约概率的见解来做出此决定。 Signet Bank现在寻求的是另一种见解……

Insight — “What specific customers should I provide with what specific kinds of loan conditions?”. To derive these insights we need more information. Unfortunately, this information wasn’t available at all in 1990. The only information provided was the default probability based on socio-demographic data….

洞察力-“我应该为哪些特定客户提供哪些特定种类的贷款条件？” 。 为了获得这些见解，我们需要更多信息。 不幸的是，这些信息在1990年根本无法获得。唯一提供的信息是基于社会人口统计学数据的默认概率……。

Information — “Hard data on how profitable different customer sets are, together with traditional defaulting probabilities and data on what kinds of customers are served by other banks, what conditions they offer!!”. Because all of this information flows into the decision whom to serve with what product package. So finally it comes down to the question of data, raw data which simply wasn’t available….

信息-“关于不同客户群盈利情况的硬性数据，以及传统的违约概率以及有关其他银行为哪些类型的客户提供服务，他们提供何种条件的数据！” 。 因为所有这些信息都会流入决定使用哪种产品包装服务于谁的决定。 因此，最后归结为数据问题，即根本无法获得的原始数据……。

Data — “New data, experimental data on different sets of conditions offered to people with different socio-demographic backgrounds and their respective profitability AND default probability”. So for Signet Bank, it turned out, the Good Data strategy was to collect this kind of data, combine it with the other data sources to walk back up the cycle again, and in turn make better decisions.

数据-“新数据，提供给具有不同社会人口背景的人们及其各自的获利能力和违约概率的不同条件下的实验数据” 。 因此，对于Signet Bank来说，好的数据策略是收集此类数据，将其与其他数据源结合起来，以再次追溯周期，进而做出更好的决策。

Of course, afterward, this wheel keeps on turning, adding more value to each stage. Capital One is reported to run thousands of variations of this kind each year. For a deeper dive into the case, look at “Data Science for Business: What you need to know about data mining and data-analytic thinking” by Provost and Fawcett, 2013.

当然，此后，此轮子会继续转动，为每个阶段增加更多的价值。 据报道，Capital One每年都会运行数千种此类产品。 要更深入地研究此案例，请参阅Provost和Fawcett于2013年撰写的“ 商业数据科学：您需要了解的有关数据挖掘和数据分析思维的知识 ”。

This case is already quite old, and companies tend to not share this kind of data. But I’ve recently stumbled across another related case from the finance industry, that of the Brazilian unicorn “Nubank”.

这种情况已经很久了，公司倾向于不共享此类数据。 但是我最近偶然发现了金融业的另一个相关案例，即巴西独角兽“ Nubank”。

努班克的优质数据 (Good Data at Nubank)

I knew Nubank for their machine learning framework fklearn. The company is creating lots of open source in the realm of machine learning problems. But as it turns out, Nubank ain’t simply investing in machine learning because everyone else in the industry does it, the investment into machine learning and algorithms is deeply tied to the companies strategy and the resulting data strategy.

我知道Nubank的机器学习框架fklearn 。 该公司正在机器学习问题领域创建大量开放源代码。 但事实证明，Nubank并不仅仅是因为行业内其他所有人都在进行机器学习方面的投资，因此对机器学习和算法的投资与公司战略以及由此产生的数据战略紧密相关。

Nubank is breaking into the “unbanked market”, which in Brazil is around 50% of the population. These people simply don’t have access to the banking system. Nubank is changing that by offering products targeted at that specific group. The first product, a mobile-only managed credit card aims to provide bank access & small loans to the unbanked at a speed previously unknown in the Brazilian finance industry, and more products are following in that path.

努班克正在进入“无银行市场”，该市场在巴西约占人口的50％。 这些人根本无法访问银行系统。 Nubank通过提供针对该特定人群的产品来改变这种状况。 第一款产品是仅用于移动设备的托管信用卡，旨在以巴西金融业以前未知的速度向无银行账户提供银行访问和小额贷款，并且更多的产品正在沿用这种方式。

But serving the “unbanked” turns out to come with an obvious problem: How do you know if an unbanked will default, or be profitable for the company? After all, they have no banking history or financial information like credit scoring.

但是，为“没有银行账户的人”服务会带来一个明显的问题：您如何知道没有银行账户的人是否会拖欠债务或对公司有利可图？ 毕竟，他们没有银行历史或信用评分等财务信息。

This is where the data strategy becomes apparent. Nubank’s founder David Velez describes to some extend how they realized exactly that problem and that they had to focus on unconventional ways of looking and collecting their data.

这就是数据策略变得显而易见的地方 。 Nubank的创始人David Velez进一步描述了他们如何确切地认识到该问题，以及他们必须专注于非常规的查找和收集数据方式。

Let’s start from the top: Provide the unbanked, with access to a credit card with reasonable fees and still be profitable. That’s the action we want to take. The data strategy thus turns into “Collect & analyze data to derive which people to offer which kinds of products, the credit card, loan or a debit card“…

让我们从头开始：为没有银行账户的人提供合理费用的信用卡访问权，并且仍然可以盈利。 那就是我们要采取的行动。 因此，数据策略变成了“收集和分析数据以推导哪些人提供哪种产品，信用卡，贷款或借记卡”……

Action — “Which customer should be offered which product? With what kinds of credit limits? To stay profitable.”. After all, just like Signet Bank, Nubank is tackling a group of customers from which the traditional banking system in Brazilian shies away. The key decisions are decisions like….

行动-“应为哪个客户提供哪种产品？ 有哪些信用额度？ 保持盈利。” 。 毕竟，就像Signet银行一样，Nubank正在解决一组客户，而巴西的传统银行业务系统却无法满足这些客户的需求。 关键决策是类似……的决策。

Decision — “Which specific customer can we offer the Mastercard credit card, to whom can we offer a loan and at which rates?”. To make such decisions Nubank needs…

决策-“我们可以向哪些特定客户提供万事达信用卡，我们可以向谁提供贷款以及以什么利率提供贷款？” 。 为了做出这样的决定，Nubank需要……

Insights & information — “the profitability of a customer as well as the default probability. But being a fast-growing start-up they also need the likelihood of people recommending the bank to others just as the possible usage of additional products like the loyalty program.”.

见解和信息-客户的盈利能力以及违约概率。 但是，作为一家快速成长的初创企业，他们还需要人们向其他人推荐银行，就像可能使用诸如忠诚度计划这样的附加产品一样。” 。

And that’s exactly what Nubank builds and is continuing to develop, according to Velez in a CNN interview:

Velez在接受CNN采访时说，这正是Nubank的建设并将继续发展。

“Nubank, however, has built its business on a wholly new foundation: unique data sets and algorithms that are based on “a lot of nontraditional information,” Vélez said.”

“但是，Nubank的业务建立在一个全新的基础上：基于“大量非传统信息”的独特数据集和算法，Vélez说。”

and…

和…

”We look at where you live…how you move, who your friends are, who invited you to Nubank, the type of people that you’re sending money to,” he said. “We look at whether you read the contract of the credit card or whether you don’t — it turns out that people [who “read” the contract] really fast tend to be fraudsters. We look at the type of transactions that you’re doing, if you’re buying groceries or if you are in a bar.”

他说：“我们着眼于您的住所……您的迁徙方式，您的朋友是谁，邀请您加入Nubank的人，向您汇款的人的类型。” “我们会研究您是否阅读信用卡合同，还是不阅读？事实证明，[快速阅读”合同的人”确实非常容易成为骗子。 我们会检查您正在执行的交易类型，是否要购买杂货或是否在酒吧里。”

Indeed, if you look closely Nubank is in a particularly great spot to exploiting these kinds of information because they also collect a lot of data traditional companies do not collect! They are already on your phone, have a recommendation program in place thus collecting key behavioral data.

的确，如果您仔细观察，Nubank正是利用此类信息的绝佳去处，因为它们还收集了许多传统公司无法收集的数据！ 他们已经在您的手机上，并具有推荐程序，从而可以收集关键行为数据。

Indeed this is underpinned by a study by Martens & Provost in 2011 which basically says: for banks figuring & using the information where you live and how old you are, are a good starting point, but using behavioral data like the ones mentioned here provides a substantial lift in profitability and increases the more data is used, whereas the sociodemographic data hits a clear roof.

确实，这是由Martens＆Provost在2011年进行的一项研究所支持的，该研究基本上说： 对于银行来说，了解和使用您所居住的位置以及您的年龄，这是一个很好的起点，但是使用 此处提到的 行为数据 可以提供一个利润率的大幅提升并增加了使用更多数据的机会，而社会人口统计学数据则有了明显的发展 。

You should also notice that Nubank is in the unique position to run experiments very similar to the one Signet Bank conducted! They currently provide 50% of the newly issued credit cards in Brazil, meaning they control a huge share of the unbanked market. By experimenting with the unbanked market they can derive very similar insights based on the data points they currently collect. And they can do that better than anyone else.

您还应该注意，Nubank处于独特的位置，可以进行与Signet Bank进行的实验非常相似的实验！ 他们目前在巴西提供50％的新发行信用卡，这意味着它们控制着非银行市场的巨大份额。 通过试验非银行市场，他们可以根据当前收集的数据点得出非常相似的见解。 他们可以比其他任何人做得更好。

Whether that’s enough to stay profitable remains to be seen, but at least it is enough to turn Nubank into a billion-dollar start-up and let them expand around the world.

是否足以维持盈利仍然有待观察，但至少足以将Nubank变成十亿美元的初创企业，并使它们在全球范围内扩张。

If you want to make a difference in your company with the use of data, I hope this helps you discern Bad Data from Good Data and put you on the right path, focusing on data strategy and not on letting data dictate a “strategy”. Stay tuned. There’s more Good Data to come.

如果您想通过数据的使用来为您的公司带来变化，我希望这可以帮助您从不良数据中识别出不良数据，并使您走上正确的道路，专注于数据战略，而不是让数据决定“战略”。 敬请关注。 还有更多的好数据 。

资源资源 (Resources)

• F. Provost, T. Fawcett, 2013: “Data Science for Business: What you need to know about data mining and data-analytic thinking”. Provost and Fawcett provide & analyze the case of Signet Bank (now Capital One). They also carry forth the idea that data science is about improving the decision making capability of a company.F. Provost，T。Fawcett，2013年：“商业数据科学：您需要了解的有关数据挖掘和数据分析思维的知识”。 Provost和Fawcett提供并分析了Signet银行(现为Capital One)的案例。 他们还提出了数据科学旨在提高公司决策能力的想法。

• J. Pepitone, 2019: CNN, One of Latin America's most valuable startup is changing the way Brazilians bank.

  J.Pepitone，2019年：CNN， 拉丁美洲最有价值的创业公司之一，正在改变巴西人的银行方式 。

• R. Rumelt, Good Strategy Bad Strategy, 2011: The book inspiring some parts of this post.

  R. Rumelt，《好策略，坏策略》，2011年： 这本书启发了这篇文章的某些部分 。

• Fklearn, an open-source functional machine learning framework created by Nubank.

  Fklearn ，Nubank创建的开源功能性机器学习框架。

• D. Martens, F. Provost, 2011: Pseudo-Social Network Targeting from Consumer Transaction Data

  D. Martens，F。Provost，2011年： 基于消费者交易数据的伪社交网络定位

• ThoughtWorks Intelligent Enterprise Series.

  ThoughtWorks智能企业系列 。