數據管理與商業智能_商業智能與數據科學

數據管理與商業智能

In this heavily jargonized trade, the words typically overlap one another, leading to a scarcity of understanding or a state of confusion around these ideas. whereas big data vs analytics or computing vs machine learning vs cognitive intelligence is used interchangeably repeatedly, BI vs Data Science is additionally one in every of the foremost mentioned.

在這個術語繁多的行業中，這些詞通常相互重疊，導致對這些想法的理解不足或感到困惑。大數據vs分析或計算vs機器學習vs認知智能可以互換使用，而BI vs Data Science則是其中最重要的一個。

It is little question that BI and data science have mature to be a lot of in-demand jobs with corporations in the majority of the industries wishing on them to own a grip over their competitors. More so, BI and data science has become an integral part of these organizations as data has become an even bigger player than ever. Therefore the broader adoption of analytics, business intelligence, and data science.

毫無疑問，BI和數據科學已經成熟，成為許多行業的公司所希望的工作，他們希望大多數行業的公司能夠控制自己的競爭對手。更重要的是，BI和數據科學已成為這些組織不可或缺的一部分，因為數據已比以往任何時候都更大。因此，分析，商業智能和數據科學的廣泛采用。

簡要背景 (A brief background)

If we tend to move into the flashback a number of years from currently, corporations didn't have data science positions however were still engaged in analytics role—these were for the most part known as data analysts. It will alright be tagged because of the precursors to the newest data scientists’ roles.
Before we tend to dive onto differentiating these 2 in style words within the analytics trade, BI and data science having a quick speech over a drag in hand.

如果從現在起數年內我們傾向于進入閃回狀態，則企業沒有數據科學的職位，但是仍然扮演著分析的角色-這些在大多數情況下被稱為數據分析師。由于最新的數據科學家的角色先驅，它會被標記。
在我們傾向于在分析行業中區分這兩種風格的詞之前，BI和數據科學會在手頭上Swift發表演講。

While BI would say "What happened and what ought to be changed?", data science would raise "Why it happened and what will happen in future?" It’s the distinction in "What", "Why" and "How" that differentiates these 2 terms.

BI會說“發生了什么，應該改變什么？”，而數據科學則提出了“為什么發生和將來會發生什么？”。這是“什么”，“為什么”和“如何”這兩個術語之間的區別。

基本區別 (The basic distinction)

While BI could be an easier version, data science in additional advanced. BI is concerning dashboards, data management, transcription data and manufacturing data from data. Whereas data science is all concerning exploitation statistics and sophisticated tools on data to forecast or analyze what might happen.

雖然BI可能是一個更簡單的版本，但數據科學方面的其他高級功能。 BI涉及儀表板，數據管理，轉錄數據和來自數據的制造數據。數據科學全都涉及開發統計數據和用于預測或分析可能發生的數據的復雜工具。

Data science might handily be expressed as an evolution of BI, however, on an advanced set of models, application of statistics and use cases. To alter constant, BI analysts that were earlier centered on the "what" side of the matter, started developing toolkit and algorithms that would facilitate them to perceive and predict business performance. It wouldn't be wrong to mention that business analysts and data scientists work along to show data into helpful data.

數據科學可以方便地表示為BI的發展，但是可以基于一組高級模型，統計數據的應用和用例。為了改變常數，BI分析師早先將重點放在問題的“什么”方面，開始開發工具包和算法，以幫助他們感知和預測業務績效。提到業務分析師和數據科學家一起努力將數據顯示為有用的數據，這沒錯。

技術比較 (Technology comparison)

The market is more and more changing into competitive, with ever-increasing advanced business issues and to drive innovation, corporations should shift their focus from ancient BI to data science.

隨著越來越多的高級商業問題和驅動創新，市場正越來越多地變為競爭性企業，為了推動創新，公司應將重點從古老的BI轉移到數據科學。

That doesn't subtract the importance of business analysts as they're those who would determine patterns and trends in a very business's' historical data. It may be the same that BI analysts explore past trends whereas data scientists find predictors and significance behind those trends. In this manner data scientists facilitate corporations to mitigate the uncertainty of the long run by giving them valuable information—such as topline, cost, risk predictions and all.
BI is concerning respondent the queries that may not appear that obvious in a very business unit. They assist in viewing the relationships between varied variables however not specifically predict them because it was mentioned, BI is concerning the "what” a part of the business and doesn't simply get new which means or applies insights to new data. Since BI historically relied on records hold on in relative databases, the structure of the warehouse was per se tied to the categories of queries it might answer. BI usually operated with a current or backward-looking focus.

這并不能消除業務分析師的重要性，因為他們是那些可以確定一家公司的歷史數據的模式和趨勢的人。 BI分析師探索過去的趨勢，而數據科學家則可以找到這些趨勢背后的預測因素和意義。通過這種方式，數據科學家可以通過為公司提供有價值的信息(如收入，成本，風險預測等)來幫助公司減輕長期的不確定性。
BI與受訪者有關在一個非常業務部門中可能不那么明顯的查詢有關。它們有助于查看各種變量之間的關系，但是由于提到了BI，因此并未明確預測它們，因為BI涉及業務的“什么”部分，而不僅僅是獲取新的含義或對新數據應用見解。依賴于相關數據庫中保存的記錄，倉庫的結構本身與它可能會回答的查詢類別有關，BI通常以當前或后向的重點進行操作。

Data science, on the opposite hand, has a unique path than BI because it depends on prophetical analytics, exploitation the method a lot of expressly. in contrast to simply checking out patterns, data scientists conduct experiments and hypotheses to succeed in the "Why” and "How” side of a drag. A knowledge somebody profile would have a mix of statistics, IT and business understanding. Yet, the next target applied statistics.

相反，數據科學比BI具有獨特的路徑，因為它依賴于預見性分析，許多方法都明確地利用了該方法。與簡單地檢查模式相反，數據科學家進行實驗和假設以在拖曳的“為什么”和“如何”方面取得成功。某人的知識檔案將統計，IT和業務理解結合在一起。然而，下一個目標是應用統計數據。

職業比較 (Career comparison)

Talking concerning the career in BI, it needs relatively lesser qualifications than data scientists. Requiring less formal expertise than a career in data science, the most objective of BI is to help in strategic business selections. Even somebody with a background in data management or IT connected field will pass over to BI with relative ease.

談到BI的職業，與數據科學家相比，它需要的資格相對較少。 BI所需的正式專業知識少于數據科學專業，因此BI的最目標是幫助戰略業務選擇。甚至具有數據管理或IT連接領域背景的人也將相對輕松地轉到BI。

Since data scientists derive selections supported prophetical algorithms, candidates choosing these job roles might need a lot of technical skillsets in subjects like statistics, machine learning, and programming. It's going to conjointly need an understanding of languages like SQL, R, Python or Scala, among others.

由于數據科學家推導了選擇支持的預言算法，因此選擇這些職位的應聘者可能需要統計學，機器學習和編程等學科的大量技術技能。這將共同需要對SQL，R，Python或Scala等語言的理解。

Using these languages, not solely a data scientist will produce a framework that leverages historical data, however, predict business outcomes a lot of expeditiously. Data science is concerning seamless and climbable integration that will need several engineers to deploy a knowledge scientist’s model across multiple applications.

使用這些語言，不僅是數據科學家會產生一個利用歷史數據的框架，但是會Swift地預測業務成果。數據科學涉及無縫和可攀升的集成，這將需要多個工程師在多個應用程序中部署知識科學家的模型。

On the opposite hand, a BI analyst would need proficiency in knowledge handling tools, a lot of therefore on BI tools like Tableau, Qlik, and SQL. Other BI connected tools have emerged recently like Sisense, Pentaho, Yellowfin, among others. A heap of coverage and BI still happens on stand out and not several would remember concerning the facility of what all may be done on MS excel. A proficiency in stand out and SQL could be a should have for BI skilled.

相反，BI分析師需要精通知識處理工具，因此需要大量諸如Tableau，Qlik和SQL之類的BI工具。最近出現了其他BI連接工具，例如Sisense，Pentaho，Yellowfin等。大量的報道和BI仍然脫穎而出，沒有幾個人會記得關于在MS excel上可以完成的全部操作的便利性。對于BI熟練者來說，精通SQL可能是一個不錯的選擇。

最后一點 (On a final note)

In a shell, data science and BI are facilitators of every different and might be the same that data science is best performed in conjunction with BI. Each of them is needed to own an economical understanding of the business trends hidden in massive volumes of data. Whereas BI is the logical start, data science follows to urge deeper insight.

簡而言之，數據科學和BI是每個方面的促進者，并且可能與將BI結合最好地執行數據科學相同。他們每個人都需要對隱藏在海量數據中的業務趨勢有一個經濟的了解。盡管BI是合乎邏輯的起點，但數據科學緊隨其后，以尋求更深刻的見解。