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ACCA PM知识点：处理和分析大数据
文章来源：ACCA官网
发布时间：2021-08-17 16:06
阅读：1311次
Processing and analysing big data
The processing of big data is generally known as big data analytics and includes:
Data mining:analysing data to identify patterns and establish relationships such as associations(where several events are connected),sequences(where one event leads to another)and correlations.
Predictive analytics:a type of data mining which aims to predict future events.For example,the chance of someone being persuaded to upgrade a flight.
Text analytics:scanning text such as emails and word processing documents to extract useful information.It could simply be looking for key-words that indicate an interest in a product or place.
Voice analytics:as above but with audio.
Statistical analytics:used to identify trends,correlations and changes in behaviour.
Google provides website owners with Google Analytics that will track many features of website traffic.For example,the website OpenTuition.com provides free ACCA study resources.Google analytics reports statistics such as the following:
GEOGRAPHICAL DISTRIBUTION OF USERS
TYPE OF BROWSER USED
AGE OF USER
The final table is instructive.OpenTuition.com does not ask for users’ages,so this data has been pieced together from other information available to Google.It has been able to do this for only about 58%of users.
These analytical findings can lead to:
Better marketing
Better customer service and relationship management
Increased customer loyalty
Increased competitive strength
Increased operational efficiency
The discovery of new sources of revenue.
The Big Data(DIKW)pyramid
The DIKW pyramid,also known as the knowledge pyramid became well known in 1989 from the work of Askoff.With the emergence of big data,the pyramid has also become known as the big data pyramid.The work of Jennifer Rowley in 2007 explained the relationships between data,information,knowledge and wisdom.
Rowley explained the pyramid:'Typically information is defined in terms of data,knowledge in terms of information,and wisdom in terms of knowledge.'
Data:a range of data can be collected from various sources–this is raw data and not particularly useful in this form.
Information:The raw data can be analysed to look for trends or patterns,for example it may appear that there is a link between the purchase of a particular product and a particular group of customers.This is information.
Knowledge:The information can be analysed further to establish how the identified links are connected.Knowing the details of exactly what types of customers buy a particular product or favour particular product features is knowledge.
Wisdom:The knowledge gathered can be used to make informed business decisions.
Example of how the pyramid could be used:
A soft drink manufacturer makes a range of fruity soft drinks in four different flavours(orange,apple,lime and pear).It has traditionally used plastic bottles but has recently run a trial whereby two flavours were also made available in glass bottles.It is making its plan for next year’s production and is considering if it should expand the use of glass bottles.
Data:The company has collected a range of data from previous purchases,customer questionnaires,social media posts etc.
Information:The raw data was analysed to look for trends or patterns.The company finds that there appears to be a link between the types of bottles purchased by different age groups.
Knowledge:Further analysis has shown that younger customers prefer the glass bottles while customers from the older age range prefer plastic bottles.Previous analysis also showed that lime flavour is almost exclusively only purchased by older customers and pear is almost exclusively only purchased by younger customers.
Wisdom:How can this knowledge be used?The company should only produce lime flavour in plastic bottles and only produce pear flavour in glass bottles.Here,the company is using the insights gained in order to make a decision and therefore this is classed as wisdom.
Dangers/risks of big data
Despite the examples of the use of big data in commerce,particularly for marketing and customer relationship management,there are some potential dangers and drawbacks.
Cost:It is expensive to establish the hardware and analytical software needed,though these costs are continually falling.
Regulation:Some countries and cultures worry about the amount of information that is being collected and have passed laws governing its collection,storage and use.Breaking a law can have serious reputational and punitive consequences.
Loss and theft of data:Apart from the consequences arising from regulatory breaches as mentioned above,companies might find themselves open to civil legal action if data were stolen and individuals suffered as a consequence.
Incorrect data:If the data held is incorrect or out of date incorrect conclusions are likely.Even if the data is correct,some correlations might be spurious leading to false positive results.
Updated article extracted from articles by Ken Garrett,a freelance lecturer and writer,and Nick Ryan,a lead tutor for performance management subjects
翻译参考
处理和分析大数据
大数据的处理通常称为大数据分析，包括：
数据挖掘：分析数据以识别模式并建立关系，例如关联（多个事件连接在一起）、序列（一个事件导致另一个事件）和相关性。
预测分析：一种旨在预测未来事件的数据挖掘。例如，有人被说服升级航班的机会。
文本分析：扫描电子邮件和文字处理文档等文本以提取有用信息。它可能只是寻找表明对产品或地点感兴趣的关键字。
语音分析：同上，但带有音频。【点击免费下载>>>更多ACCA学习相关资料】
统计分析：用于识别行为的趋势、相关性和变化。
谷歌为网站所有者提供谷歌分析，可以跟踪网站流量的许多功能。例如，网站OpenTuition.com提供免费的ACCA学习资源。谷歌分析报告统计如下：
用户地域分布
使用的浏览器类型
用户年龄
决赛桌很有启发性。OpenTuition.com不会询问用户的年龄，因此这些数据是从Google可用的其他信息中拼凑而成的。它只能为大约58%的用户做到这一点。
这些分析结果可导致：
更好的营销
更好的客户服务和关系管理
提高客户忠诚度
竞争实力增强
提高运营效率
发现新的收入来源。
大数据(DIKW)金字塔
DIKW金字塔，也称为知识金字塔，于1989年因Askoff的工作而广为人知。随着大数据的出现，金字塔也被称为大数据金字塔。Jennifer Rowley在2007年的工作解释了数据、信息、知识和智慧之间的关系。
罗利解释了金字塔：“通常，信息是根据数据来定义的，知识是根据信息来定义的，而智慧是根据知识来定义的。”
数据：可以从各种来源收集一系列数据——这是原始数据，在这种形式中不是特别有用。
信息：可以分析原始数据以寻找趋势或模式，例如，特定产品的购买与特定客户群之间可能存在联系。这是信息。
知识：可以进一步分析信息以确定已识别的链接是如何连接的。确切了解哪些类型的客户购买特定产品或喜欢特定产品功能的详细信息是知识。
智慧：收集到的知识可用于做出明智的业务决策。
如何使用金字塔的示例：
一家软饮料制造商生产一系列具有四种不同口味（橙子、苹果、酸橙和梨）的果味软饮料。它传统上使用塑料瓶，但最近进行了一项试验，在玻璃瓶中也提供了两种口味。它正在制定明年的生产计划，并正在考虑是否应该扩大玻璃瓶的使用。
数据：公司从之前的购买、客户问卷、社交媒体帖子等中收集了一系列数据。
信息：分析原始数据以寻找趋势或模式。该公司发现，不同年龄组购买的瓶子类型之间似乎存在联系。
知识：进一步的分析表明，年轻的客户更喜欢玻璃瓶，而年龄较大的客户更喜欢塑料瓶。之前的分析还表明，酸橙味几乎只由年长的客户购买，而梨味几乎只由年轻的客户购买。
智慧：如何运用这些知识？该公司应只生产塑料瓶中的酸橙味，只生产玻璃瓶中的梨味。在这里，公司正在使用获得的见解来做出决定，因此这被归类为智慧。
大数据的危害/风险
尽管有在商业中使用大数据的例子，特别是在营销和客户关系管理中，但仍有一些潜在的危险和缺点。
成本：建立所需的硬件和分析软件是昂贵的，尽管这些成本在不断下降。
法规：一些国家和文化担心正在收集的信息量，并通过了管理其收集、存储和使用的法律。违反法律可能会导致严重的声誉和惩罚性后果。
数据丢失和被盗：除了上述违反监管规定的后果外，如果数据被盗并且个人因此遭受损失，公司可能会面临民事法律诉讼。
不正确的数据：如果持有的数据不正确或过时，则可能得出错误的结论。即使数据是正确的，一些相关性也可能是虚假的，导致假阳性结果。
以上翻译仅供参考，请以ACCA官网内容为准！
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