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原創譯文 | 大數據分析:最難的不是分析,而是大數據

原創譯文 | 大數據分析:最難的不是分析,而是大數據

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從先進的BI工具到機器學習,人工智慧,現代企業擁有著各式各樣整理分析數據的方法和途徑。數據科學家和企業領導人都關注著這些新技術的巨大潛力,然而,當我們將焦點放在分析工具身上時,我們也可能忽略了數據本身的重要性。畢竟如果沒有正確的數據,視覺化和預測分析也沒有任何用處。文末更多往期譯文推薦

每一個企業需要將他們的基礎數據進行分析和甄別,在此基礎上,對數據進行不同層次和結構的分類。原因如下:

數據深度融入在商業的各個環節

現代企業逐漸意識到,紛繁複雜的數據固然重要,而這些數據是否真的被企業職工運用,並對其工作產生了相關性的影響,才是企業領導所看重的。不同的層級崗位和職位角色都需要做出正確的決策,而良好的決策必須是基於用戶數據所提出的。因此,不僅僅是數據科學團隊,從產品部門到客戶服務部門,再到銷售等各個部門都應該獲得這些數據資源和信息。

在現代企業中,對數據的處理還僅僅是在每個月的全體會議上查看各項指標還遠遠不夠。組織必須要將數據驅動納入到決策制定中。以現代營銷團隊為例。營銷人員有大量的豐富的數據可供他們自由支配,尤其是在智能手機,平板電腦,社交媒體平台爆炸式普及的今天,這樣,一個品牌可以遠距離地與觀眾互動,並了解顧客的相關信息。如果所有的這些數據被收集到一個中心位置,進行數據分析,那麼對客戶的長期行為分析並進行消費預判則成為了可能。同樣地,根據這樣的方法,其他部門,如銷售、產品和客戶服務部門也能獲得前所未有的數據量。

零碎數據共同形成宏觀趨勢判斷

如今,數據在各個行業和企業扮演著越來越重要的角色,企業應該將數據視為機會。每個數據集——CRM、CMS、ERP、營銷軟體,都包含大量信息和基礎數據。現在或許看起來很微小,可是對數據深入的挖掘和分析將會給企業帶來巨大的財富。而在現實生活當中,由於不可能預先知道哪些數據很重要,所以企業需要收集儘可能多的數據,這樣即使市場環境發生大的改變,企業也能夠做出合理的預判和儘可能貼近市場的決策。

基礎數據和數據分析同樣重要

數據質量是重中之重,傾斜的數據會導致錯誤的結果。如果你的判斷來源於不完整的數據基礎,你的決策便會產生一定的偏差甚至產生錯誤,而這最終將會侵蝕在數據驅動文化背景下人們對數據分析的信心。因此,簡潔、完整和正確的數據是有效決策產生的必要前提。

2016年美國總統大選的預測分析,很好地證明了數據質量的重要性。在當時的預測中,大多數數據是基於州級和國家級的電話投票進行的。但是電話調查中很容易出現無人接聽的現象,而各州無人接聽的佔比率也存在著很大的區別,這會很大程度上影響選舉團的預測(選舉團制度是美國特有的一種選舉方式, 選民在大選日投票時,不僅要在總統候選人當中選擇,而且要選出代表50個州和華盛頓特區的538名選舉人,以組成選舉團。當選的選舉人必須宣誓在選舉團投票時把票投給在該州獲勝的候選人。美國總統由選舉團選舉產生,並非由選民直接選舉產生,獲得半數以上選舉人票者當選總統),結果就是,傾斜的數據產生錯誤的預測。

如今,機器學習已經受到了大量的炒作。而機器依據大數據分析出來的預判,是否真的能符合事實情況,很大程度上決定於是否擁有堅實的數據基礎:一個將數據驅動納入到組織文化的企業,採集到的簡介、完整和正確的數據。」數據驅動」一詞已存在多年,但在今天快節奏和迅猛發展的數字經濟中,它將成為當代企業的文化使命。

英文原文

The Hardest Part of Analytics Isn』tAnalysis. It』s Data

From advanced BI tooling to machinelearning and artificial intelligence, modern businesses have more ways thanever to slice and dice their data. As data scientists and business leaders alikefixate on the great potential of these new technologies, we risk losing sightof what』s most important: the data itself. After all, fancy visualizations andpredictive analytics don』t matter without the right data powering them.

Every single business needs to prioritizecollecting and structuring their underlying data over the analysis they use tounderstand it. Here』s why:

Data will be ingrained in every part ofhow we do business

Companies have just begun to grasp not onlythe complexity of data, but also the depth of its relationship with their ownemployees. All business roles and levels need to make good decisions, and thebest decisions are made with user data. Thus, every department – not just thedata science team – should have access to that information, from product tocustomer service to sales.

It』s no longer enough to just reviewtopline metrics at a monthly all-hands meeting. Organizations must infusedata-driven processes into their decision-making. Take a modern marketing team,for example. Marketers today have a multitude of rich data sources at theirdisposal, especially with the explosion of smartphones, tablets, social mediaplatforms and digital touchpoints through which a brand can interact with itsaudience. If all of this data is collected into a central place, it opens uppowerful new ways of understanding long-term customer behavior. Otherdepartments like sales, product, and customer success similarly have access toan unprecedented amount of data.

Every bit of data contributes to thebigger picture

As data plays a bigger role across everydepartment and level, businesses must consider all of its data as a growingcollection of opportunities. Every dataset – CRM, CMS, ERP, marketing software– contains a multitude of possible insights. Findings that seem insignificantnow might matter a great deal down the road. It』s impossible to know upfrontwhat data matters, so businesses need to collect as much of it as they can.This lets companies retroactively unearth insights, even if their priorities ormarket conditions change.

Insights are only as good as theunderlying data

Data quality is king. Bad data leads to badresults. If you base your decisions on incomplete data, it becomes harder totrust the results, and it ultimately erodes confidence in a data-drivenculture. Clean, complete, and correct data is necessary for generatingactionable insights.

We saw this with the 2016 presidentialelection. Most predictions were based on national and state-level pollingresults conducted over the phone. But phone surveys are especially susceptibleto nonresponse bias, which itself varies wildly from state to state. Thisaffects the forecast for the Electoral College more than the overall popularvote, yet the Electoral College is what wins elections. The result? Skewed dataproducing the wrong prediction.

Machinelearning has received a great deal of hype, and for good reason. But it cannotlive up to its bold potential unless it』s informed by a strong foundation:clean, complete data produced by an organization that ingrains data into itsculture. The term 「data-driven」 has been around for years, but in today』sfast-paced and increasingly digital economy, it will need to become a culturalmandate for companies everywhere.

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