大數據問題癥結: 短期內數據不適合推測
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新聞 – 你可能熟悉這個統計數據，世界上90%的數據是過去幾年創建的。確實，在過去的大約30年中，世界上的數據量每兩年就增加10倍左右。
One of the problems with such a rate of information increase is that the present moment will always 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.
這樣的信息增速帶來的問題之一是，目前時刻總是比過去時刻，甚至是剛剛過去的時刻顯得重要得多。短視成了思維結構的內置功能，表現爲以忽略歷史爲代價高估短期趨勢的壓倒性傾向。
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.
要理解爲什麼這很重要，不妨想想社會科學關於“近期偏差”的發現，它描述的就是人們傾向於認爲未來事件與近期經歷大體相似。它的另一個版本又被稱爲可用性啓發法：不管什麼，最容易浮上心頭的，人們往往不能對它進行恰如其分的思考。
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 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.
這也是一種普遍的心理屬性。例如，過去幾年你生活的地方出現了格外冷的夏天，你可能會說夏天在變冷 – 或當地氣候在變冷。事實上，你可能需要看得很長遠很長遠，才能瞭解有關氣候趨勢的有意義的現象。
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.
同樣的道理往往也適用於現實生活中最複雜的現象：股市，經濟，公司成敗，戰爭與和平，人際關係，帝國興衰。短期分析不僅無效，而且越幫越忙，南轅北轍 – 積極卻誤導。
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.
還值得記住的是，在決定要保存或刪除什麼樣的數據時，新穎往往是首要考慮。除舊迎新是搜索算法本質上偏重新鮮度的世界中呈現的數字化趨勢。對目前的偏重結構化地滲透在我們周圍幾乎一切技術中。
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.
怎麼辦呢？這不僅僅是更好地保存舊數據的問題。更重要的是，這首先是確定什麼值得保存的問題。一味積累不是什麼答案。在數據越來越大的時代，選擇不知道什麼與選擇知道什麼同樣重要。
