Analysis和Analytics的中文翻譯都是”分析”但意思大不同 / What is the difference between Analysis and Analytics

來看看以下影片的解釋吧!

[影片逐字稿與中文翻譯/Transcript and Translation]

Let’s discuss the not so obvious differences between the terms ANALYSIS and ANALYTICS. Due to the similarity of the words some people believe they share the same meaning and thus use them interchangeably. Technically, this isn’t correct. There is in fact a distinct difference between the two. And the reason for one often being used instead of the other is the lack of a transparent understanding of both. So, let’s clear this up, shall we?

讓我們來討論ANALYSIS和ANALYTICS這兩個詞之間不太明顯的差別。因為這兩個單字很相似，一些人會以為它們有相同的意思，因此可以互換使用。嚴格來說，這是不對的。實際上，這兩個字有明顯的區別。經常使用某一個字而不用另一個，就是因為對它們的理解不夠清楚。所以，讓我們把這個問題弄清楚吧!

First, we will start with Analysis. Consider the following, you have a huge data set containing data of various types. Instead of tracking the entire data set and running the risk of becoming overwhelmed, you separated into easier to digest chunks and study them individually and examine how they relate to other parts, and that’s analysis in a nutshell. One important thing to remember however is that you perform analyses on things that have already happened in the past. Such as using an analysis to explain how a story ended the way it did or how there was a decrease in the cells last summer. All this means that we do analyses to explain how and or why something happened.

首先，我們從ANALYSIS開始說。考慮以下狀況，你有一個包含各種類型數據的大數據庫。為了更方便研究，將資料庫分成一個個容易分析的區塊，並單獨研究這些區塊，還有研究它們與其他數據的關係，簡而言之，這就是ANALYSIS。不過還有一點要記住，你要對過去已經發生的事情進行分析。例如用ANALYSIS來解釋一個故事的結局，或是去年夏天電池銷售量的減少。所有這些意味著我們需要透過分析來解釋(過去)事情是如何發生和為什麼發生。

Great. Now this leads us nicely onto the definition of analytics as you have probably guessed. Analytics generally refers to the future instead of explaining past events. It explores potential future ones. Analytics is essentially the application of logical and computational reasoning to the component parts obtained in an analysis and in doing this you are looking for patterns in exploring what you can do with them in the future. Here analytics branches off into two areas. Qualitative analytics. This is using your intuition and experience in conjunction with the analysis to plan your next business move. And quantitative analytics. This is applying formulas and algorithms to numbers you have gathered from your analysis.

現在我們將進入ANALYTICS的定義，你可能已經猜到了。ANALYTICS通常是指未來，而不是解釋過去的事件。它探討的是未來的潛在事件。ANALYTICS本質上是將邏輯和運算推理應用到分析中，而得到的分析結果，並幫助你找到一些模式去解釋未來可以怎麼做。ANALYTICS分成兩個部分: 質性分析和量性分析。質性分析就是利用你的直覺和經驗，與分析作結合，來規畫你的下一步業務行動。量性分析是將公式和演算法應用到你從分析中收集到的數字上。

Here are a couple of examples. Say you are an owner of an online clothing store. You are ahead of the competition and have a great understanding of what your customer’s needs and wants are. You’ve performed a very detailed analysis from women’s clothing articles and feel sure about which fashion trends to follow. You may use this intuition to decide on which styles of clothing to start selling. This would be qualitative analytics. But you might not know when to introduce the new collection. In that case relying on past sales data and user experience data you could predict in which month it would be best to do that. This is an example of using quantitative analytics.

這裡有幾個例子。假設你是一個線上服飾店的老闆，你在競爭者中處於領先地位，而且對顧客的需求很瞭解。你已經從女裝的報導中進行了非常詳細的分析，並且掌握了時尚的趨勢。你可以利用這種直覺來決定銷售哪種風格的服裝，這是質性分析(Qualitative Analytics)。但是你可能不知道什麼時候該推出新的系列。在這種情況下，根據過去的銷售數據和顧客體驗數據，你可以預測在哪個月份推出最好。這就是一個量性分析(Quantitative Analytics)。

Fantastic, to backtrack a little. You can combine these areas with analyses. Also you could perform qualitative analysis to explain how or why a story ended the way it did. And you can perform quantitative analysis working with past data to explain how sales decrease last summer.

你可以結合質性分析和量性分析，你也可以用質性分析來解釋一個故事是如何或為什麼會結束。你還可以用過去的數據作量性分析，解釋去年夏天的銷售量是如何下降。

Perfect. Now that we have cleared up the differences between analysis and analytics it shouldn’t be too difficult to C-L terms such as data analysis, analytics, business analysis and business analytics can have their unique meanings.

現在，我們已經釐清了ANALYSIS和ANALYTICS之間的區別，那其他的電腦素養(CL, computer literacy)名詞就不困難了，例如Analysis和Analytic分別在"數據分析"和"業務分析"裡都有不同的含意。

理解了Analysis和Analytics定義上的區別，這兩種數據分析需要哪些能力?

Data Analysis : database, data barrows, and programming languages.

Data Analytics (or Data Science) : mathematics, algorithms, programming language or diagnosis or database, data mining.

再以工作角色來區分: Data Analyst, Data Engineer, and Data Scientist