大数据问题症结: 短期内数据不适合推测“ng体育官网入口下载”

BBC News – You may be familiar with the statistic that 90% of the world’s data was created in the last few years. Indeed, every two years for about the last three decades the amount of data in the world has increased by about 10 times.BBC新闻 – 你有可能熟知这个统计数据，世界上90%的数据是过去几年创立的。显然，在过去的约30年中，世界上的数据量每两年就减少10倍左右。One of the problems with such a rate of information increase is that the present moment will always loom 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.这样的信息增长速度带给的问题之一是，目前时刻总是比过去时刻，甚至是刚过去的时刻变得最重要得多。

短视出了思维结构的内置功能，展现出为以忽视历史为代价低估短期趋势的压倒性偏向。To understand why this matters, consider the findings from social science about ‘recency bias’, 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.要解读为什么这很最重要，不妨看看社会科学关于“近期偏差”的找到，它叙述的就是人们偏向于指出未来事件与近期经历大体相近。

它的另一个版本又被称作可用性启发法：不管什么，最更容易浮上心头的，人们往往无法对它展开恰如其分的思维。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 tempted 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.这也是一种广泛的心理属性。

例如，过去几年你生活的地方经常出现了十分冻的夏天，你可能会说道夏天在变冷 – 或当地气候在变冷。事实上，你有可能必须看得很将来很将来，才能理解有关气候趋势的有意义的现象。The same tends to be true of most complex phenomena 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 invalid – they’re actively unhelpful and misleading.某种程度的道理往往也限于于现实生活中最简单的现象：股市，经济，公司胜败，战争与和平，人际关系，帝国兴亡。

短期分析不仅违宪，而且就越老大越忙，南辕北辙 – 大力却误导。It’s also worth remembering that novelty tends to be a dominant 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 biased towards freshness. A bias towards the present is structurally engrained in almost all the technology surrounding us.还有一点忘记的是，在要求要留存或移除什么样的数据时，精致往往是首要考虑到。

除旧迎新是搜索算法本质上注重新鲜度的世界中呈现出的数字化趋势。对目前的注重结构化地渗入在我们周围完全一切技术中。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. Mere 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？这某种程度是更佳地留存原有数据的问题。

更加最重要的是，这首先是确认什么有一点留存的问题。一味累积不是什么答案。在数据更加大的时代，自由选择不告诉什么与自由选择告诉什么某种程度最重要。

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