大数据问题症结: 短期内数据不适合推测

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BBC新闻 – 你可能熟悉这个统计数据，世界上90%的数据是过去几年创建的。确实，在过去的大约30年中，世界上的数据量每两年就增加10倍左右。

One of the problems with such a rate of information increase is that the present moment will always loom² far larger than even the recent past. Short-sightedness is built into the structure, in the form of an overwhelming tendency to over-estimate short-term trends at the expense of history.

这样的信息增速带来的问题之一是，目前时刻总是比过去时刻，甚至是刚刚过去的时刻显得重要得多。短视成了思维结构的内置功能，表现为以忽略历史为代价高估短期趋势的压倒性倾向。

To understand why this matters, consider the findings from social science about ‘recency bias³’, which describes the tendency to assume that future events will closely resemble recent experience. It’s a version of what is also known as the availability heuristic: the tendency to base your thinking disproportionately on whatever comes most easily to mind.

要理解为什么这很重要，不妨想想社会科学关于“近期偏差”的发现，它描述的就是人们倾向于认为未来事件与近期经历大体相似。它的另一个版本又被称为可用性启发法：不管什么，最容易浮上心头的，人们往往不能对它进行恰如其分的思考。

It’s also a universal psychological attribute. If the last few years have seen exceptionally cold summers where you live, for example, you might be tempted⁴ to state that summers are getting colder – or that your local climate may be cooling. In fact, you would need to take a far, far longer view to learn anything meaningful about climate trends.

这也是一种普遍的心理属性。例如，过去几年你生活的地方出现了格外冷的夏天，你可能会说夏天在变冷 – 或当地气候在变冷。事实上，你可能需要看得很长远很长远，才能了解有关气候趋势的有意义的现象。

The same tends to be true of most complex phenomena⁵ in real life: stock markets, economies, the success or failure of companies, war and peace, relationships, the rise and fall of empires. Short-term analyses aren’t only invalid⁶ – they’re actively⁷ unhelpful and misleading.

同样的道理往往也适用于现实生活中最复杂的现象：股市，经济，公司成败，战争与和平，人际关系，帝国兴衰。短期分析不仅无效，而且越帮越忙，南辕北辙 – 积极却误导。

It’s also worth remembering that novelty tends to be a dominant⁸ consideration when deciding what data to keep or delete. Out with the old and in with the new: that’s the digital trend in a world where search algorithms are intrinsically biased⁹ towards freshness. A bias towards the present is structurally¹⁰ engrained in almost all the technology surrounding us.

还值得记住的是，在决定要保存或删除什么样的数据时，新颖往往是首要考虑。除旧迎新是搜索算法本质上偏重新鲜度的世界中呈现的数字化趋势。对目前的偏重结构化地渗透在我们周围几乎一切技术中。

What to do? This isn’t just a question of being better at preserving old data. More importantly, it’s about determining what is worth preserving in the first place. Mere¹¹ accumulation is no kind of answer. In an era of bigger and bigger data, what you choose not to know matters just as much as what you do.

怎么办呢？这不仅仅是更好地保存旧数据的问题。更重要的是，这首先是确定什么值得保存的问题。一味积累不是什么答案。在数据越来越大的时代，选择不知道什么与选择知道什么同样重要。

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