数据管理与商业智能_商业智能与数据科学

数据管理与商业智能

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.

在这个术语繁多的行业中，这些词通常相互重叠，导致对这些想法的理解不足或感到困惑。大数据vs分析或计算vs机器学习vs认知智能可以互换使用，而BI vs Data Science则是其中最重要的一个。

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.

毫无疑问，BI和数据科学已经成熟，成为许多行业的公司所希望的工作，他们希望大多数行业的公司能够控制自己的竞争对手。更重要的是，BI和数据科学已成为这些组织不可或缺的一部分，因为数据已比以往任何时候都更大。因此，分析，商业智能和数据科学的广泛采用。

简要背景 (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.

如果从现在起数年内我们倾向于进入闪回状态，则企业没有数据科学的职位，但是仍然扮演着分析的角色-这些在大多数情况下被称为数据分析师。由于最新的数据科学家的角色先驱，它会被标记。
在我们倾向于在分析行业中区分这两种风格的词之前，BI和数据科学会在手头上Swift发表演讲。

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会说“发生了什么，应该改变什么？”，而数据科学则提出了“为什么发生和将来会发生什么？”。这是“什么”，“为什么”和“如何”这两个术语之间的区别。

基本区别 (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.

虽然BI可能是一个更简单的版本，但数据科学方面的其他高级功能。 BI涉及仪表板，数据管理，转录数据和来自数据的制造数据。数据科学全都涉及开发统计数据和用于预测或分析可能发生的数据的复杂工具。

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.

数据科学可以方便地表示为BI的发展，但是可以基于一组高级模型，统计数据的应用和用例。为了改变常数，BI分析师早先将重点放在问题的“什么”方面，开始开发工具包和算法，以帮助他们感知和预测业务绩效。提到业务分析师和数据科学家一起努力将数据显示为有用的数据，这没错。

技术比较 (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.

随着越来越多的高级商业问题和驱动创新，市场正越来越多地变为竞争性企业，为了推动创新，公司应将重点从古老的BI转移到数据科学。

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.

这并不能消除业务分析师的重要性，因为他们是那些可以确定一家公司的历史数据的模式和趋势的人。 BI分析师探索过去的趋势，而数据科学家则可以找到这些趋势背后的预测因素和意义。通过这种方式，数据科学家可以通过为公司提供有价值的信息(如收入，成本，风险预测等)来帮助公司减轻长期的不确定性。
BI与受访者有关在一个非常业务部门中可能不那么明显的查询有关。它们有助于查看各种变量之间的关系，但是由于提到了BI，因此并未明确预测它们，因为BI涉及业务的“什么”部分，而不仅仅是获取新的含义或对新数据应用见解。依赖于相关数据库中保存的记录，仓库的结构本身与它可能会回答的查询类别有关，BI通常以当前或后向的重点进行操作。

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，数据科学比BI具有独特的路径，因为它依赖于预见性分析，许多方法都明确地利用了该方法。与简单地检查模式相反，数据科学家进行实验和假设以在拖曳的“为什么”和“如何”方面取得成功。某人的知识档案将统计，IT和业务理解结合在一起。然而，下一个目标是应用统计数据。

职业比较 (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.

谈到BI的职业，与数据科学家相比，它需要的资格相对较少。 BI所需的正式专业知识少于数据科学专业，因此BI的最目标是帮助战略业务选择。甚至具有数据管理或IT连接领域背景的人也将相对轻松地转到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.

由于数据科学家推导了选择支持的预言算法，因此选择这些职位的应聘者可能需要统计学，机器学习和编程等学科的大量技术技能。这将共同需要对SQL，R，Python或Scala等语言的理解。

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.

使用这些语言，不仅是数据科学家会产生一个利用历史数据的框架，但是会Swift地预测业务成果。数据科学涉及无缝和可攀升的集成，这将需要多个工程师在多个应用程序中部署知识科学家的模型。

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分析师需要精通知识处理工具，因此需要大量诸如Tableau，Qlik和SQL之类的BI工具。最近出现了其他BI连接工具，例如Sisense，Pentaho，Yellowfin等。大量的报道和BI仍然脱颖而出，没有几个人会记得关于在MS excel上可以完成的全部操作的便利性。对于BI熟练者来说，精通SQL可能是一个不错的选择。

最后一点 (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.

简而言之，数据科学和BI是每个方面的促进者，并且可能与将BI结合最好地执行数据科学相同。他们每个人都需要对隐藏在海量数据中的业务趋势有一个经济的了解。尽管BI是合乎逻辑的起点，但数据科学紧随其后，以寻求更深刻的见解。