大數(shù)據(jù)被很多人吹捧成了大企業(yè)的救星：有人說它能預言未來，照亮我們的道路,，給古老的商業(yè)模式帶來新的生機,。但是在現(xiàn)實世界中，數(shù)據(jù)是會殺人的,。它能殺死項目,，殺死金錢，甚至殺死時間,。25年前,，數(shù)據(jù)的增長速度大約只有每天100GB，而現(xiàn)在,，數(shù)據(jù)的增長速率差不多已達到50,000GB每秒,。隨著數(shù)據(jù)量的海量增長,，企業(yè)也越來越難以憑借自身的能力進行數(shù)據(jù)分析，從而加大而不是減小了企業(yè)戰(zhàn)略決策的難度,。

時間是我們最寶貴的資源,，而數(shù)據(jù)偷走了我們大量寶貴的時間。我們的感觀早已被各種各樣的數(shù)據(jù)淹沒,。每天我們都會收到數(shù)不清的電子郵件,、手機短信和提醒消息，每一條信息都會讓人分心,，降低我們的工作效率,。它們將我們抽離了原本該做的事情，迫使我們將注意力放在也許重要,、也許不重要的事情上,。同理，企業(yè)的業(yè)務數(shù)據(jù)也同樣多得令人窒息,，牽扯了我們的大量精力,，已經(jīng)成了影響企業(yè)高效決策的攔路虎。

不妨想象一下,，如果有一天,，你只會收到對你來說真正重要的信息，而且這些信息還能在正確的時間,、在正確的地點找到你,，世界將是什么樣子。那么你每天至少能多做多少事情,？我們將大量的時間耗費在被動消化這些海量信息上,，真正用來主動謀劃企業(yè)發(fā)展的時間少之又少。這樣既令人心力交瘁,，又削弱了企業(yè)效能,。

更重要的是，數(shù)據(jù)會令企業(yè)喪失精準度,。光靠捕捉更多信息并不會自動使企業(yè)產(chǎn)生更多價值,。有人可能會想，我們收集的數(shù)據(jù)越多,，就越能從中獲得好的見解,。這種自欺欺人的心態(tài)是很危險的。只有當數(shù)據(jù)能帶來準確而重要的見解時,，它才是好的數(shù)據(jù),。

另外，只有與你息息相關的信息才是有用的信息,。好的信息必須具備時效性和真實性,。然而不幸的是,，當企業(yè)想從大數(shù)據(jù)中提取有用的見解時，卻經(jīng)常會起到反效果,。舉個真實的例子,，美國有一個叫麥克·西伊的人是辦公用品超市OfficeMax的常客,，他的女兒不幸和男友死于一場車禍,。OfficeMax不知怎么得知了這個消息，在發(fā)給麥克·西伊的自動促銷郵件中竟然出現(xiàn)了這樣的抬頭：“麥克·西伊（女兒死于車禍）,?！边@并非大數(shù)據(jù)有意作孽，而是它的相關性（和適宜性）的問題,。一個企業(yè)要想只收集其確實需要的數(shù)據(jù)幾乎是不可能的,，很多時候你收集到的是那些原本不該看到的東西。對于一家公司來說,，你收集到的數(shù)據(jù)很可能是誤導性甚至是毀滅性的,。大數(shù)據(jù)雖然能將很多不相關的點連接起來，呈現(xiàn)一幅完整的圖畫,，但是要確保數(shù)據(jù)的相關性,、及時性和真實性，你首先還要正確理解它的背景,。

現(xiàn)在,，全球每天的數(shù)據(jù)總量都能達到250萬的三次方字節(jié)，要想通過大數(shù)據(jù)獲得全面的見解是很難的,。你要么會陷入無力分析的境地（因此無法獲得見解），要么就更糟糕,，你可能會在有限的甚至是被錯誤解讀的數(shù)據(jù)基礎上獲得錯誤的見解,。如果沒有正確地理解數(shù)據(jù)的背景，將不啻于椽木求魚,。一些看似有希望改變游戲規(guī)則的見解,，在實際中卻很有可能導致你從游戲中出局。

數(shù)據(jù)也會扼制你的靈活性,。傳統(tǒng)的數(shù)據(jù)分析方法,，是將交易系統(tǒng)中的所有數(shù)據(jù)存放到一個數(shù)據(jù)倉庫里（也有的叫數(shù)據(jù)湖或數(shù)據(jù)池），然后運行幾套業(yè)務智能系統(tǒng),，叫幾個或十幾個分析師分析上一周的時間,，然后把數(shù)據(jù)導到Excel里，或者做一個PPT,。周而復始,，得到的見解始終是滯后的,。這種數(shù)據(jù)處理方法其實是一種浪費。由于要處理的數(shù)據(jù)很多,，你得需要很長的時間才能獲得有用的或是有可操作性的見解,。你需要找到一種透過能繁雜的數(shù)據(jù)，得到為你的公司量身定制的信息的方法,。

當我開車進城的時候,，我想知道路上的交通堵不堵，需要多久才能達到目的地,。如果有人給我的建議跟我同事上次開車走這條路時一樣準確,，那我就會不那么依賴GPS應用了。Waze就是這個領域的一款非常強大的應用,，因為它截取了所有司機的一個巨大的時間斷面的信息,。這種全球數(shù)據(jù)的集中化使得所有用戶都能獲得與背景環(huán)境相關的見解。大數(shù)據(jù)也需要采取類似的做法,。企業(yè)現(xiàn)在應該停止在自己公司的范圍內(nèi)積攢業(yè)務數(shù)據(jù)了,，而是應該真正利用云計算的規(guī)模經(jīng)濟效益，不僅僅做到基礎設施與應用的共享,，更重要的是做到數(shù)據(jù)的共享,。

如果你想將大量數(shù)據(jù)變成有價值的見解，你就應該利用一個集中化的全球性平臺,，因為這樣一個平臺可以借助大量內(nèi)部和外部資源消化海量信息,。企業(yè)將數(shù)據(jù)收集、管理和分析工作外包出去,，就可以使這種通用平臺專心研究數(shù)據(jù)科學,，而你只需要集中精力，將它為你量身打造的見解應用在提高企業(yè)核心能力,、強化企業(yè)競爭優(yōu)勢上,。

20年前的一場“無軟件”運動將世界從線下帶到了云端。而今天,，我們也需要掀起一場“數(shù)據(jù)有罪”運動?，F(xiàn)在已經(jīng)到了從收集數(shù)據(jù)轉向讓這些數(shù)據(jù)切實發(fā)揮作用的時候了。這將的話,，在別人還在空談“大數(shù)據(jù)”或疲于內(nèi)部業(yè)務智能項目的時候,，我們就能夠解放精力進行創(chuàng)新。（財富中文網(wǎng)）

本文作者Nader Mikhail是Elementum公司的創(chuàng)始人兼CEO,。

譯者：樸成奎

Big data has been anointed the savior of big business: it divines the future, reveals our path, and breathes new life into our venerable business models. But in reality, data kills. It kills projects, it kills money, and it kills time. Twenty-five years ago, data was growing at a rate of 100GB a day. Now, data grows at a rate of almost 50,000GB a second. And as the volume of data grows, the ability of companies to make sense of it diminishes, confounding rather than illuminating strategic decisions.

Time is our most valuable resource, and data drains it. We are on sensory overload. Every one of the thousands of emails, text messages, notifications, and alerts we receive daily are a distractionand therefore kills productivity. They inherently take us away from what we’re doing and force our attention to issues that may or may not concern us. In the same way, our business data is overwhelming and distracting us—throwing up barriers to productive decision-making.

Imagine a world in which every piece of information you receive would not only be relevant to you, it would find you at the right place and right time. How much more would you be able to get done every day? We expend massive amounts of energy just trying to keep up with all this information, leaving little time or energy for us to actually move the needle for our organizations. It’s overwhelming, and it’s crippling.

What’s more, data kills accuracy. Capturing more data will not automatically generate more value for a company. The more we collect data, the more we convince ourselves that we will be able to glean good insights from it. This modern take on the sunk cost fallacy is corporate quicksand. Data is only good when it results in accurate and relevant insights.

To be useful, information has to pertain to you, it has to be timely, and it has to be true. Unfortunately, when it comes to gleaning insights out of big data, the odds are stacked against you. Take for example the OfficeMax coupon that was addressed to “Mike Seay, Daughter Killed in Car Crash.” It’s not the quality of data that lies at the source of the blunder, but it's relevance (and appropriateness). It’s virtually impossible to collect only the data you really need—and therefore, you are much more likely to be using data that you shouldn't. Data that, in the context of what you’re trying to do, is mistaken or even damaging. Big data is good for connecting dots that would otherwise go unconnected. But in order for information to be pertinent, timely, and true, you need to understand its context.

And with 2.5 quintillion bytes of data accumulating every day, the likelihood of achieving a broad purview is low. You will either fall victim to analysis paralysis (and therefore, never unlock insights), or worse, you will glean false insights based on limited or misunderstood data. Without context, you run a high risk of chasing red herrings. Insights that seem game-changing can, in reality, be game-ending.

Data also kills agility. The traditional approach: suck all the data from your transactional systems into a data warehouse (or data lake or data pond), slap a few business intelligence systems on top, throw a few (dozen) analysts at it for a week, and dump everything back into Excel and Powerpoint. Rinse, repeat, and continue to fall behind. This type of data processing is a waste. With so much data to handle, it takes way too long to get any useful or actionable insights. There’s simply too much irrelevant data sitting between you and your decisions. You need to find a path through all that data to receive information that is tailored and customized to your business.

When I get in my car to head to the city, I want to know if there’s traffic on the way and how long it will take to get to my destination. I’d be a lot less inclined to use GPS apps if the recommendations were only as accurate as the last time one of my co-workers drove that route. An app such as Waze is powerful because it pools information from a large cross-section of all drivers. This centralizing of global data allows for contextual insights that benefit all users. Big data requires a similar approach. It’s time to stop accumulating business data within the four walls of your company and to start taking advantage of the true economies of scale of the cloud: not just shared infrastructure and applications, but shared data.

If you want to turn data points into valuable insights, you need to leverage a centralized, global platform that can ingest information from a multitude of internal and external sources. Outsourcing all this data collection, management, and analysis will allow this common platform to focus on the data science, while you focus on applying its tailored insights towards strengthening your core competencies and sharpening your competitive edge.

Two decades ago, there was a “No Software” movement that took the world from on premise to cloud. Today, we all need to embrace the “Data Kills” movement. It's time to transition from collecting data to making it useful. It will free us to innovate while others are tangled in internal business intelligence projects, drowning in their own data lakes and "big data" water cooler prattle.