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ACCA PM大数据特点：Variety

文章来源：ACCA官网

发布时间：2021-08-17 15:50

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Variety

Some of the variety of information can be seen from the examples listed above.In particular,the following types of information are held:

Browsing activities:sites,pages visited,membership of sites,downloads,searches

Financial transactions

Interests

Buying habits

Reaction to advertisements on the internet or to advertising emails

Geographical information

Information about social and business contacts

Text

Numerical information

Graphical information(such as photographs)

Oral information(such as voice mails)

Technical information,such as jet engine vibration and temperature analysis

This data can be both structured and unstructured:

Structured data:this data is stored within defined fields(numerical,text,date etc)often with defined lengths,within a defined record,in a file of similar records.Structured data requires a model of the types and format of business data that will be recorded and how the data will be stored,processed and accessed.This is called a data model.Designing the model defines and limits the data which can be collected and stored,and the processing that can be performed on it.

An example of structured data is found in banking systems,which record the receipts and payments from your current account:date,amount,receipt/payment,short explanations such as payee or source of the money.

Structured data is easily accessible by well-established database structured query languages.

Unstructured data:refers to information that does not have a pre-defined data-model.It comes in all shapes and sizes and it is this variety and irregularity which makes it difficult to store in a way that will allow it to be analysed,searched or otherwise used.An often quoted statistic is that 80%of business data is unstructured,residing it in word processor documents,spreadsheets,PowerPoint files,audio,video,social media interactions and map data.

Here is an example of unstructured data and an example of its use in a retail environment:

You enter a large store and have your mobile phone with you.That allows your movement round the store to be tracked.The store might or might not know who you are(depending on whether it knows your mobile phone number).The store can record what departments you visit,and how long you spend in each.Security cameras in the ceiling match up your image with the phone,so now they know what you look like and would be able to recognise you on future visits.You pass near a particular product and previous records show that you had looked at that product before,so a text message can be sent perhaps reminding you about it,or advertising a 10%price reduction.Perhaps the store has a marketing campaign that states that it will never be undersold,so when you pass near products you might be making a price comparison and the store has to check prices on other stores websites and message you with a new price.If you buy the product then the store might have further marketing opportunities for related products and consumables and this data has to be recorded also.You pay with an affinity credit card(a card with associations with another organisations such as a charity or an airline),so now the store has some insight into your interests.Perhaps you buy several products and the store will want to discover if these items are generally bought together.

So just walking round a store can generate a vast quantity of data which will be very different in size and nature for every individual.

翻译参考

种类

从上面列出的示例中可以看出一些种类的信息。特别是，持有以下类型的信息：

浏览活动：站点、访问的页面、站点成员、下载、搜索

金融交易

兴趣

购买习惯

对互联网广告或广告电子邮件的反应

地理信息

有关社交和业务联系的信息

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数值信息

图形信息（如照片）

口头信息（如语音邮件）

技术信息，例如喷气发动机振动和温度分析

这些数据既可以是结构化的，也可以是非结构化的：

结构化数据：此数据存储在定义的字段（数字、文本、日期等）中，通常具有定义的长度，在定义的记录内，在类似记录的文件中。结构化数据需要一个关于将被记录的业务数据的类型和格式以及如何存储、处理和访问数据的模型。这称为数据模型。设计模型定义和限制可以收集和存储的数据，以及可以对其执行的处理。

在银行系统中可以找到结构化数据的一个例子，它记录您当前账户的收款和付款：日期、金额、收款/付款、收款人或资金来源等简短说明。

结构化数据可以通过完善的数据库结构化查询语言轻松访问。

非结构化数据：指没有预定义数据模型的信息。它有各种形状和大小，正是这种多样性和不规则性使得难以以允许对其进行分析、搜索或以其他方式使用的方式进行存储。一个经常被引用的统计数据是80%的业务数据是非结构化的，它们存在于文字处理器文档、电子表格、PowerPoint文件、音频、视频、社交媒体交互和地图数据中。

以下是非结构化数据的示例及其在零售环境中的使用示例：

您进入一家大型商店并随身携带手机。这允许跟踪您在商店周围的移动。商店可能知道也可能不知道您是谁（取决于它是否知道您的手机号码）。商店可以记录您访问了哪些部门，以及您在每个部门停留的时间。天花板上的安全摄像头将您的图像与手机相匹配，因此现在它们知道您的长相，并且能够在以后的访问中认出您。您经过某个特定产品附近，之前的记录显示您之前看过该产品，因此可以发送一条短信提醒您，或者宣传10%的降价。也许这家商店有一个营销活动，声明它永远不会被低估，因此，当您通过附近的产品时，您可能会进行价格比较，而商店必须检查其他商店网站上的价格，并向您发送新价格。如果您购买了该产品，那么商店可能会为相关产品和消耗品提供进一步的营销机会，并且还必须记录此数据。您使用亲和力信用卡（一种与慈善机构或航空公司等其他组织有关联的卡）付款，因此现在商店对您的兴趣有了一些了解。也许您购买了几种产品，商店会想知道这些商品是否通常一起购买。您使用亲和力信用卡（一种与慈善机构或航空公司等其他组织有关联的卡）付款，因此现在商店对您的兴趣有了一些了解。也许您购买了几种产品，商店会想知道这些商品是否通常一起购买。您使用亲和力信用卡（一种与慈善机构或航空公司等其他组织有关联的卡）付款，因此现在商店对您的兴趣有了一些了解。也许您购买了几种产品，商店会想知道这些商品是否通常一起购买。

因此，仅仅在商店里走一圈就会产生大量数据，每个人的数据大小和性质都大不相同。

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