In 2008 the World Economic Forum reported the beginning of a new age, that of big data. Enter the zettabyte, a 9.57 trillion gigabytes unit of information that we now had available for processing.

2008年，世界经济论坛报道了大数据时代的开始。 输入Zettabyte，这是我们现在可用于处理的9.57万亿千兆字节的信息单位。

A Brief History Of Big Data referred to another article from 2007 that heralded in a new age, The End Of Theory in Wired, where obsolete scientific methods could be replaced with enormous datasets.

《大数据简史》提到了2007年的另一篇文章，它宣告了一个新时代， 《有线理论的终结》 ，其中过时的科学方法可以用庞大的数据集代替。

“Out with every theory of human behaviour, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.”

从语言学到社会学，人类行为的每一种理论都应运而生。 忘记分类法，本体论和心理学。 谁知道为什么人们会做什么？ 关键是他们做到了，我们可以以前所未有的保真度对其进行跟踪和测量。 有了足够的数据，数字就说明了一切。”

The article is over ten years old now; a piece of writing from the era before Google dropped it’s ‘Do No Evil’ police from its code of conduct.

该文章已有十多年历史了； 这是Google从其行为准则中删除“不要做恶”警察之前那个时代的写照。

It was written before politicians here in the UK and elsewhere started the desecration of fields of expertise across health, finance, humanities and anywhere else disagreement with their policies surfaced.

它是在英国和其他地方的政客开始亵渎健康，金融，人文科学以及其他任何与他们的政策分歧的专业领域之前撰写的。

Perhaps the article came from a place of hope but really, what has big data done for most of us?

也许这篇文章来自希望之地，但实际上，对我们大多数人来说，大数据做了什么？

The original Wired article mentions science as a potential benefactor of the ‘correlation is enough’ philosophy that sees the search for causation within models as unnecessary waste.

《连线》杂志的原始文章提到科学是“足够相关”哲学的潜在受益者，该哲学认为在模型内寻找因果关系是不必要的浪费。

Average temperatures continue to increase, and if you’re not feeling the heat just by looking at the pictures of the wildfires in Australia and the USA, then you’ve got an icier constitution than the melting Antarctic.

平均温度持续升高，如果您不只是看澳大利亚和美国野火的照片而感到闷热，那么比融化的南极洲还要冰冷的体质。

On top of that inequality continues to grow and because we’re only mid-cycle through the COVID-19 pandemic, we’re yet to see the outcome for lower economic groups.

最重要的是，不平等现象继续加剧，而且由于我们只是COVID-19大流行的中期周期，因此我们尚未看到低收入群体的后果。

Whatever the promise of big data actually was, it hasn’t landed with most of us.

不管大数据的真正承诺是什么，它对我们大多数人来说都没有。

So why do we look to big data?

那么，为什么我们要看大数据呢？

On principle, the idea that we can buy a set of correlations and turn them into a theory or a product is very appealing.

原则上，我们可以购买一组相关并将其转换为理论或产品的想法非常具有吸引力。

We don’t lose time or money investigating things that don’t work.

我们不会浪费时间或金钱去调查无效的事物。

We’re buying a piece of confidence that ordinarily is gained through trial and a whole load of error.

我们正在获得信心，这通常是通过试用和大量错误获得的。

And of course when the case studies work for big data they are huge. Think of the astronomical profits of Amazon and the celebration of Jeff Bezos crossing the 200 billion dollar net worth recently.

当然，当案例研究适用于大数据时，它们将是巨大的。 想想亚马逊的天价利润和杰夫·贝索斯(Jeff Bezos)最近突破2000亿美元净资产的庆祝活动。

But the snake oil here is that data only works well when you have a problem you set out to solve. You need to be in search of a hypothesis before making use of data; doing it the other way round is not efficient.

但是这里的问题是，只有当您要解决一个问题时，数据才能正常工作。 在使用数据之前，您需要先寻找一个假设。 相反，这样做是没有效率的。

数字不能说明一切。 他们只是数字。 (The numbers can’t speak for themselves. They’re just numbers.)

In The Success and Resounding Failure of Big Data, data scientist Michael Lukanioff investigates the truth underneath the hype of data.

在《大数据的成功与失败》中，数据科学家迈克尔·卢卡尼诺夫( Michael Lukanioff)研究了数据炒作背后的真相。

In our current data quagmire real damage is being done to the fabric of our society by our inability to transform information into knowledge before it can be disseminated as misinformation and propaganda.

在我们目前的数据中，由于无法将信息转化为知识而无法将其传播为错误信息和宣传，因此对我们社会的结构造成了真正的破坏。

He reports a 60–85% failure rate of data set implementation for businesses. That’s not the kind of narrative that we’re used to hearing in relation to data.

他报告说，企业实施数据集的失败率达到60-85％。 那不是我们惯常听到的关于数据的叙述。

The real fruit (or gold) that is at the heart of any successful project is going in with a cause, or a problem to solve.

任何成功项目的核心都是要有原因或有待解决的问题。

Looking around at some of the current, or new success stories, we find this mindset is driving growth and results where merely analyzing data cannot.

环顾一些当前或新的成功案例，我们发现这种思维方式正在推动增长和结果，而仅仅分析数据是无法实现的。

The authors of these other stories are not going in via a direct-to-data route. They share a mission, a problem-solving mindset that starts with a quest and uses data to back up their theory — they speak first and see whether the numbers respond. They don’t let numbers speak for themselves.

这些其他故事的作者并非直接通过数据访问。 他们共同承担任务，一种解决问题的思维方式，从寻求任务开始，并使用数据来支持他们的理论-他们先讲话，然后看看数字是否响应。 他们不让数字代表自己。

Netflix公司 (Netflix)

Of the public success stories, Netflix is one of the biggies on circulation at the moment, and you can’t argue with the numbers or the relevance in COVID-19 environment.

在公众的成功故事中，Netflix是目前发行量最大的公司之一，您不能对数字或COVID-19环境的相关性争论不休。

Since its inception in 1997 as a direct mail DVD service, at a time when few households had evolved beyond VHS rental, it has grown into a giant of data-driven customization.

自从1997年以直接邮寄DVD服务的形式推出以来，在很少有家庭超越VHS租金的时代，它已经发展成为由数据驱动的定制巨人。

Bringing personalized recommendations based on your likes and watching patterns and introducing entirely new genres and markets to viewers.

根据您的喜好和观看方式带来个性化推荐，并向观众介绍全新的流派和市场。

Following on from the release of his book, That Will Never Work Marc Randolph discusses the intersection of ideas and data on developing a new product in an interview with Storius Magazine.

从他的书发布后上，永远不会工作马克·兰多夫讨论了关于在采访中开发新的产品理念和数据的交集Storius杂志。

“I’ve learned that when you’re doing something new (and DVD-rental-by-mail certainly met that definition), you can’t really use data to tell you what to do.”

“我了解到，当您在做新的事情时(而且DVD租借的电子邮件肯定符合该定义)，您实际上无法使用数据来告诉您该怎么做。”

He believes that data needs to follow on swiftly from a new idea but that the initial point of inspiration comes from following your intuition and ultimately trying to solve a problem.

他认为，数据需要从一个新的想法Swift地走出来，但是灵感的最初点来自于您的直觉并最终试图解决问题。

There’s a legendary myth about Randolph’s Netflix co-founder dreaming up the idea after receiving a $40 late fee from Blockbuster and musing over a world of rental entertainment without late fees. The story has not been claimed as true, but it does fit the bill with the evolution of an idea.

伦道夫(Randolph)的Netflix联合创始人在从Blockbuster收取40美元的滞纳金并沉迷于没有滞纳金的租赁娱乐世界中幻想着这个想法，这是一个传奇的神话。 这个故事并没有被认为是真实的，但是它确实与一个想法的演变相吻合。

Randolph goes on to say that problem solving is the true genesis of a fruitful idea though an element of luck will propel you forward.

伦道夫继续说，解决问题的方法是成功的想法的真正起源，尽管运气会推动您前进。

“Ideas come and go — but problems are there forever.”

“想法来来去去，但问题永远存在。”

This human-centered focus strategy has remained in the work of Netflix CEO Reed Hastings and his talent acquisition strategy. He invests in the superstars of the thinking world; rather than relying on technology or data alone. He recruits the people who can take the creative and risk-taking decisions because the numbers alone cannot tell this story.

Netflix CEO里德·黑斯廷斯(Reed Hastings)的工作和人才培养策略一直保留着这种以人为本的专注策略。 他投资于思想界的超级巨星。 而不是仅仅依靠技术或数据。 他招募的人可以做出创造性的决定和冒险的决定，因为仅凭数字并不能说明这个故事。

皮埃尔·奥米迪亚(Pierre Omidyar) (Pierre Omidyar)

Pierre Omidyar, mega philanthropist but more commonly known as the founder of eBay was on a mission to empower people to define their own value when he created his iconic on-line marketplace.

大型慈善家皮埃尔·奥米迪亚(Pierre Omidyar)，通常被称为eBay的创始人，其使命是使人们在创建自己的标志性在线市场时赋予人们定义自己的价值的能力。

He tells the story of trying to invest in IPO for a gaming company and being refused shares at a lower price because he was a regular person.

他讲述了一个尝试为一家游戏公司进行IPO投资并因为他是普通人而被拒绝以较低价格发行股票的故事。

The problem he set out to solve was empowering people to benefit from the theory of efficient markets, instead of being locked out by bigger players.

他着手解决的问题是使人们能够从有效市场理论中受益，而不是被更大的参与者所束缚。

eBay, a market where people could set their own value was born.

eBay诞生了，人们可以自行设定价值。

“Long term sustainable change happens if people discover their own power.”

“如果人们发现自己的力量，那么长期的可持续变化就会发生。”

萨沙·哈科(Sasha Haco) (Sasha Haco)

Sasha Haco, young entrepreneur with a degree in natural sciences and a PhD in theoretical physics created a seed-funded AI moderation app to trawl the internet and complete necessary task that takes its toll emotionally on the humans that currently do the job.

年轻的企业家Sasha Haco拥有自然科学学位，并获得了理论物理学博士学位，他创建了一个种子基金资助的AI审核应用程序，以拖网捕捞互联网并完成必要的任务，这对目前从事这项工作的人类造成了深深的影响。

She was a colleague of the late and much-missed Stephen Hawkings and was listed in a Beauhurst report of the UK’s Top 10 Female Entrepreneurs Under 30.

她是已故且失踪人数众多的史蒂芬·霍金斯(Stephen Hawkings)的同事，并被《伯赫斯特》(Beauhurst)报告列为英国30岁以下十大女性企业家之一。

She talks to Tech Crunch about her mission to make the internet a safer place.

她与Tech Crunch谈了她的使命，使互联网成为一个更安全的地方。

“Regulations are responding to this crisis and putting increasing pressure on platforms to deal with harmful content and protect our children from the worst of the internet. But currently, there is no adequate solution”.

“法规正在应对这场危机，并给平台带来越来越大的压力，要求其处理有害内容并保护我们的孩子免受互联网最恶劣的影响。 但是目前，没有适当的解决方案”。

收获富有想象力的解决方案(Harvesting Imaginative Solutions)

We’re always told to chase the data as makers. Do your research. Get the numbers. But what’s the question? How can you drive change with numbers alone?

我们总是被告知要以制造者身份追逐数据。 做你的研究。 获取数字。 但是有什么问题呢？ 您如何仅凭数字就能推动变革？

And how do we find our power? It’s not in a series of numbers or correlation data. It’s on the road; it’s in the quest, it’s the holy grail at the end of a brave expedition.

我们如何找到我们的力量？ 它不在一系列数字或相关数据中。 在路上在追求中，这是英勇探险队结束时的圣杯。

And it cannot be packed or marketed in the same way that access to data can.

而且，它不能以与访问数据相同的方式打包或销售。

From imagination comes intuition, hope, problem-solving, inspiration and sometimes for those who persevere this can translate into a profitable business.

想象力来自直觉，希望，解决问题的能力，灵感，有时对于那些持之以恒的人来说，可以转化为有利可图的业务。

Whether the ‘data will fix it’ mentality comes from a place where imagination has been forgotten, or it’s of a more sinister marketing tactic to try and sell you something you don’t need — , there is another, better road.

“数据将解决问题”的心态是来自人们遗忘了想象力的地方，还是试图向您出售不需要的东西的一种更险恶的营销策略-还有另一条更好的道路。

Inequality grows, while negative climate change grows, and data grows too.

不平等现象加剧，不利的气候变化也在加剧，数据也在增长。

There’s a correlation you don’t need a computer for but without a mission, how are we going to change this? What’s the question we’re trying to solve as consumers or entrepreneurs? What’s the big mission?

有一个关联，您不需要电脑，但没有任务，我们将如何改变呢？ 作为消费者或企业家，我们要解决什么问题？ 什么是大任务？

While the trends are worrying, we can look around to another artform to provide ideas and guidance. The art of storytelling. Why storytelling? Because all good stories start with a quest and good quests, make for good innovation and promising innovations make for successful businesses.

当趋势令人担忧时，我们可以看看另一种艺术形式来提供想法和指导。 讲故事的艺术。 为什么讲故事？ 因为所有的好故事都是从一个追求和一个好的追求开始的，所以要进行好的创新，而有希望的创新则对成功的企业有所帮助。

So if a data package or database doesn’t work what will? If you’re a writer or entrepreneur, you should start with a question.

那么，如果数据包或数据库不起作用怎么办？ 如果您是作家或企业家，则应该从一个问题开始。

Like Pierre, like Reed, like Sasha, — find your mission and push it forward. Don’t have a mission in life? That’s okay but don’t believe everything about data.

像皮埃尔一样，里德一样，萨莎也一样—找到自己的使命并将其推进。 生活中没有使命吗？ 没关系，但不要相信有关数据的所有信息。

If you’re an entrepreneur or trying to do something different with your life then where do you find your mission?

如果您是企业家或试图做一些与众不同的事情，那么您在哪里找到使命？

It starts with a hunch, a conversation, a good book, a smile you receive from a stranger when you’re walking your dog in the park.

当您在公园里walking狗时，会从一个陌生人的预感，交谈，一本好书，一个微笑开始。

It comes from life and stories and hope.

它来自生活，故事和希望。

I’m a writer and storyteller, investigating the noise and trying to create some of my own too. If you’re interested in receiving a weekly roundup of similar articles, you can join my newsletter here.

我是一名作家和讲故事的人，他研究噪音并试图创造自己的东西。 如果您有兴趣每周收到类似文章的摘要，则可以在此处加入我的时事通讯。

Thanks for reading.

谢谢阅读。