SAS商业分析实战：从入门到精通

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"Practical Business Analytics Using SAS(Apress,2015)" 是一本实用的商业分析书籍，通过SAS软件来指导读者如何在实际的商业环境中有效地分析数据。作者是SAS、统计学、分析和大数据领域的专家，他们首先解释了分析、分析工具以及SAS编程的基础知识，然后演示如何在商业环境中应用SAS，并逐步介绍如何导入数据和进行数据操作。书中通过真实的业务案例提供实践练习，涵盖了SAS的基本功能及其在提升业务性能中的应用。该书的结构分为两部分。第一部分是SAS编程基础，包括对商业分析和数据分析工具的简介，SAS入门，数据处理，以及SAS的重要函数和过程。例如，第1章介绍了商业分析的基本概念，第2章引导读者入门SAS，第3章讲解如何使用SAS处理数据，第4章则深入介绍了SAS的关键功能和过程。第二部分是SAS在商业分析中的应用，从统计分析概述开始，涵盖基本描述性统计报告，数据探索、验证和数据清洗，假设检验，相关性与线性回归，多元回归分析，逻辑回归，时间序列分析和预测，以及最后的大数据分析介绍。每一章都提供了具体的实例，如第5章介绍统计分析的基础，第6章教授如何在SAS中生成基本统计报告，而第13章则涉及大数据分析的初步知识。这本书通过银行业和其他行业的实例让分析原理和方法更加生动具体，旨在帮助读者掌握使用SAS进行高级分析技巧，如第9章的线性回归，第10章的多元回归分析，第11章的逻辑回归，以及第12章的时间序列预测等，这些都是在现代商业决策中至关重要的技能。 "Practical Business Analytics Using SAS"是一本全面且实践性强的指南，适合SAS用户和商业人士提升数据分析能力，通过学习，读者将能够运用SAS解决复杂的业务问题，提升业务效率和决策质量。

CHAPTER 1 ■ INTRODUCTION TO BUSINESS ANALYTICS AND DATA ANALYSIS TOOLS

Some Practical Applications of Business Analytics

The following sections discuss a couple of examples on the practical usage of application of business analytics

in the real world. Predicting customer behavior towards some product features, or application of business

analytics in the supply chain to predict the constraints, such as raw material lead times, are very common

examples. Applications of analytics are very popular in retail and predicting trends on social media as well.

Customer Analytics

Predicting consumer behavior is the key to all marketing campaigns. Market segmentation, customer

relationship management, pricing, product design, promotion, and direct marketing campaigns can benefit

to a large extent if consumer behavior can be predicted with reasonable accuracy. Companies with direct

interaction with customers collect and analyze a lot of consumer-related data to get valuable business

insights that may be useful in positively affecting sales and marketing strategies. Retailers such as Amazon

and Walmart have a vast amount of transactional data available at their disposal, and it contains information

about every product and customer on a daily basis. These companies use business analytics techniques

effectively for marketing, pricing policies, and campaign designs, which enable them to reach the right

customers with the right products. They understand customer needs better using analytics. They can swap

better-selling products at the cost of less-efficient ones. Many companies are also tapping the power of social

media to get meaningful data, which can be used to analyze consumer behavior. The results of this analytics

can also be used to design more personalized direct marketing campaigns.

Operational Analytics

Several companies use operational analytics to improve existing operations. It is now possible to look

into business processes in real time for any given time frame, with companies having enterprise resource

planning (ERP) systems such as SAP, which give an integrated operational view of the business. Drilling

down into history to re-create the events is also possible. With proper analytics tools, this data is used to

analyze root cases, uncover trends, and prevent disasters. Operational analytics can be used to predict lead

times of shipments and other constraints in supply chains. Some software can present a graphical view of

supply chain, which can depict any possible constraints in events such as shipments and production delays.

Social Media Analytics

Millions of consumers use social media at any given time. Whenever a new mobile phone or a movie, for

instance, is launched in the market, millions of people tweet about it almost instantly, write about their

feelings on Facebook, and give their opinions in the numerous blogs on the World Wide Web. This data, if

tapped properly, can be an important source to uncover user attitudes, sentiments, opinions, and trends.

Online reputation and future revenue predictions for brands, products, and effectiveness of ad campaigns

can be determined by applying proper analytical techniques on these instant, vast, and valuable sources of

data. In fact, many players in the analytics software market such as IBM and SAS claim to have products to

achieve this.

Social media analytics is simply text mining or text analytics in some sense. Unstructured text data

is available on social media web sites, which can be challenging to analyze using traditional analytics

techniques. (Describing text analytics techniques is out of scope for this book.)

Some companies are now using consumer sentiment analysis on key social media web sites such

as Twitter and Facebook to predict revenues from new movie launches or any new products introduced

in the market.

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CHAPTER 1 ■ INTRODUCTION TO BUSINESS ANALYTICS AND DATA ANALYSIS TOOLS

Data Used in Analytics

The data used in analytics can be broadly divided into two types: qualitative and quantitative. The

qualitative, discrete, or categorical data is expressed in terms of natural languages. Color, days of a week,

street name, city name, and so on, fall under this type of data. Measurements that are explained with the

help of numbers are quantitative data, or a continuous form of data. Distance between two cities expressed

in miles, height of a person measured in inches, and so on, are forms of continuous data.

This data can come from a variety of sources that can be internal or external. Internal sources include

customer transactions, company databases and data warehouses, e-mails, product databases, and the

like. External data sources can be professional credit bureaus, federal databases, and other commercially

available databases. In some cases, such as engineering analysis, a company may like developing its own

data to solve an uncommon problem.

Selecting the data for building a business analytics problem requires a thorough understanding of the

overall business and the problem to be resolved. The past sections discussed that an analytics model uses

data combined with the statistical techniques used to analyze it. Hence, the accuracy of any model is largely

dependent upon the quality of underlying data and statistical methods used to analyze it.

Obtaining data in a usable format is the first step in any model-building process. You need to first

understand the format and content of the raw data made available for building a model. Raw data may require

extraction from its base sources such as a flat file or a data warehouse. It may be available in multiple sources

and in a variety of formats. The format of the raw data may warrant separation of desired field values, which

otherwise appear to be junk or have little meaning in its raw form. The data may require a cleansing step as

well, before an attempt is made to process it further. For example, a gender field may have only two values of

male and female. Any other value in this field may be considered as junk. However, it may vary depending

upon the application. In the same way, a negative value in an amounts field may not be acceptable.

In some cases, the size of available data may be so large that it may require sampling to reduce it to a

manageable form for analysis purposes. A sample is a subset from the available data, which for all practical

purposes represents all the characteristics of the original population. The data sourcing, extraction,

transformation, and cleansing may eat up to 70 percent of total hours made available to a business analytics

project.

Big Data vs. Conventional Business Analytics

Conventional analytical tools and techniques are inadequate to handle data that is unstructured (like text

data), that is too large in size, or that is growing rapidly like social media data. A cluster analysis on a 200MB

file with 1 million customer records is manageable, but the same cluster analysis on 1000GB of Facebook

customer profile information will take a considerable amount of time if conventional tools and techniques

are used. Facebook as well as entities like Google and Walmart generate data in petabytes every day.

Distributed computing methodologies might need to be used in order to carry out such analysis.

Introduction to Big Data

The SAS web site defines big data as follows:

Big data is a popular term used to describe the exponential growth and availability of

data, both structured and unstructured. And big data may be as important to business—

and society—as the Internet has become. Why? More data may lead to more accurate

analyses.

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CHAPTER 1 ■ INTRODUCTION TO BUSINESS ANALYTICS AND DATA ANALYSIS TOOLS

It further states, “More accurate analyses may lead to more confident decision making. And better

decisions can mean greater operational efficiencies, cost reductions, and reduced risk.”

This definition refers to big data as a popular term that is used to describe data sets of large volumes

that are beyond the limits of commonly used desktop database and analytical applications. Sometimes even

server-class databases and analytical applications fail to manage this kind of data set.

Wikipedia describes big data sizes as a constantly moving target, ranging from a few dozen terabytes

to some petabytes (as in 2012) in a single data set. The size of big data may vary from one organization to

the other, depending on the capabilities of software that are commonly used to process the data set in its

domain. For some organizations, only a few hundred gigabytes of data may require reconsideration using

their data processing and analysis systems, while some may feel quite at home with even hundreds of

terabytes of data.

CONSIDER A FEW EXAMPLES

Cern’s Large Hydron Collider experiments deal with 500 quintillion bytes of data per day, which is

200 times more than all other sources combined in the world.

In Business

eBay.com uses a 40 petabytes Hadoop cluster to support its merchandising, search, and consumer

recommendations.

Amazon.com deals with some of the world’s largest Linux databases, which measure up to 24 terabytes.

Walmart’s daily consumer transactions run into 1 million per hour. Its databases sizes are estimated to

be 2.5 petabytes.

All this, of course, is extremely large data. It is almost impossible for conventional database and

business applications to handle it.

The industry has two more definitions for big data.

Big data is a collection of data sets so large and complex that it becomes difficult to •

process using on-hand database management tools or traditional data processing

applications.

Big data is the data whose scale, diversity, and complexity requires new architecture, •

techniques, algorithms, and analytics to manage it and extract value and hidden

knowledge from it.

In simple terms, big data cannot be handled by conventional data-handling tools, and big data analysis

cannot be performed using conventional analytical tools. Big data tools that use distributed computing

techniques are needed.

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CHAPTER 1 ■ INTRODUCTION TO BUSINESS ANALYTICS AND DATA ANALYSIS TOOLS

Big Data Is Not Just About Size

Gartner defines the three v’s of big data as volume, velocity, and variety. So far, only the volume aspect of big

data has been discussed. In this context, the speed with which the data is getting created is also important.

Consider the familiar example of the Cern Hydron Collider experiments; it annually generates 150 million

petabytes of data, which is about 1.36EB (1EB = 1073741824GB) per day. Per-hour transactions for Walmart

are more than 1 million.

The third v is variety. This dimension refers to the type of formats in which the data gets generated. It can be

structured, numeric or non-numeric, text, e-mail, customer transactions, audio, and video, to name just a few.

In addition to these three v’s, some like to include veracity while defining big data. Veracity includes the

biases, noise, and deviation that is inherent in most big data sets. It is more common to the data generated from

social media web sites. The SAS web site also counts on data complexity as one of the factors for defining big data.

Gartner’s definition of the three v’s has almost become an industry standard when it comes to defining

big data.

Sources of Big Data

Some of the big data sources have already been discussed in the earlier sections. Advanced science studies

in environmental sciences, genomics, microbiology, quantum physics, and so on, are the sources of data sets

that may be classified in the category of big data. Scientists are often struck by the sheer volume of data sets

they need to analyze for their research work. They need to continuously innovate ways and means to store,

process, and analyze such data.

Daily customer transactions with retailers such as Amazon, Walmart, and eBay also generate large

volumes of data at amazing rates. This kind of data mainly falls under the category of structured data.

Unstructured text data such as product descriptions, book reviews, and so on, is also involved. Healthcare

systems also add hundreds of terabytes of data to data centers annually in the form of patient records and

case documentations. Global consumer transactions processed daily by credit card companies such as Visa,

American Express, and MasterCard may also be classified as sources of big data.

The United States and other governments also are big sources of data generation. They need the power

of some of the world’s most powerful supercomputers to meaningfully process the data in reasonable time

frames. Research projects in fields such as economics and population studies, conducted by the World Bank,

UN, and IMF, also consume large amounts of data.

More recently, social media sites such as Facebook, Twitter, and LinkedIn are presenting some great

opportunities in the field of big data analysis. These sites are now among some of the biggest data generation

sources in the world. They are mainly the sources of unstructured data. Data forms included here are text

data such as customer responses, conversations, messages, and so on. Lots of other data sources such as

audio clips, numerous videos, and images are also included. Their databases are hundreds of petabytes.

This data, although difficult to analyze, presents immense opportunities to generate useful insights

and information such as product promotion, trend and sentiment analysis, brand management, online

reputation management for political outfits and individuals, to name a few. Social media analytics is a

rapidly growing field, and several startups and established companies are devoting considerable time and

energies to this practice. Table 1-1 compares big data to conventional data.

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CHAPTER 1 ■ INTRODUCTION TO BUSINESS ANALYTICS AND DATA ANALYSIS TOOLS

Table 1-1. Big Data vs. Conventional Data

Big Data Normal or Conventional Data

Huge data sets. Data set size in control.

Unstructured data such as text, video, and audio. Normally structured data such as numbers and

categories, but it can take other forms as well.

Hard-to-perform queries and analysis. Relatively easy-to-perform queries and analysis.

Needs a new methodology for analysis. Data analysis can be achieved by using

conventional methods.

Need tools such as Hadoop, Hive, Hbase, Pig, Sqoop,

and so on.

Tools such as SQL, SAS, R, and Excel alone may

be sufficient.

Raw transactional data. The aggregated or sampled or filtered data.

Used for reporting, basic analysis, and text mining.

Advanced analytics is only in a starting stage in big data.

Used for reporting, advanced analysis, and

predictive modeling .

Big data analysis needs both programming skills (such

as Java) and analytical skills to perform analysis.

Analytical skills are sufficient for conventional

data; advanced analysis tools don’t require

expert programing skills.

Petabytes/exabytes of data.

Millions/billions of accounts.

Billions/trillions of transactions.

Megabytes/gigabytes of data.

Thousands/millions of accounts.

Millions of transactions.

Generated by big financial institutions, Facebook,

Google, Amazon, eBay, Walmart, and so on.

Generated by small enterprises and small banks.

Big Data Use Cases

Big Data is a buzzword today. Every organization wants to use it in the decision-making process. Provided

next are just a few examples to give you a feel for its application.

Big Data in Retail

This case is centered on an imaginary large retail chain The retail chain decides to use big data to its advantage

to predict trends and to prepare for future demands. The work begins several months before the holiday

shopping season begins. The need is to predict the thrust segments and determine the hot items for the season.

For analysis, the enterprise data is combined with other relevant information such as industry advertising,

web browsing patterns, and social media sentiments. Big data analytics makes use of predictive modeling and

generates a future demand pattern for various consumer items. Location-wise demand prediction is also done.

Delivery channels such as brick-and-mortar shops and online web sites are also determined, and the delivery

of various items is done as per the predicted values. Big data price optimization models are used to determine

the right price levels. This may vary from one location to the other.

Big data analytics on the customer demand data combined with current price levels, inventories,

and competition data helps the retailer to adjust the prices in real time as per demand and other market

dynamics. The expected buying pattern predictions are used to pinpoint customers who are likely to buy a

hot-pick item this season. When the customer is at the retail store, they can be contacted using text messaging

or e-mail and can be presented with a real-time promotional offer that may prompt a buying decision.

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