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E-raamat: Business Analytics for Managers: Taking Business Intelligence Beyond Reporting

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"In this second edition of Business Analytics for Managers, the authors provided an updated look at business analytics, focusing mainly on the subject of "big data" and the increased use of analytical information in real time processes. Though the 1E touches on these two subjects, there are new terms and theories in the analytics world that need to be reflected and made explicit in a second edition. Topics covered include: - Growing open-source technologies like Hadoop and their strengths and weaknesses compared to the traditional way of storing and processing data in a data warehouse - The impact of social media. Social media has both become a new data source that can be used for multiple analytical purposes, as well as a channel to deploy analytical results in. - Data security - Cloud technologies, and - Future trends"--

"The intensified used of data based on analytical models to control digitalized operational business processes in an intelligent way is a game changer that continuously disrupts more and more markets. This book exemplifies this development and shows the latest tools and advances in this field Business Analytics for Managers offers real-world guidance for organizations looking to leverage their data into a competitive advantage. This new second edition covers the advances that have revolutionized the field since the first edition's release; big data and real-time digitalized decision making have become major components of any analytics strategy, and new technologies are allowing businesses to gain even more insight from the ever-increasing influx of data. New terms, theories, and technologies are explained and discussed in terms of practical benefit, and the emphasis on forward thinking over historical data describes how analytics can drive better business planning. Coverage includes data warehousing, big data, social media, security, cloud technologies, and future trends, with expert insight on the practical aspects of the current state of the field. Analytics helps businesses move forward. Extensive use of statistical and quantitative analysis alongside explanatory and predictive modeling facilitates fact-based decision making, and evolving technologies continue to streamline every step of the process. This book provides an essential update, and describes how today's tools make business analytics morevaluable than ever. Learn how Hadoop can upgrade your data processing and storage Discover the many uses for social media data in analysis and communication Get up to speed on the latest in cloud technologies, data security, and more Prepare for emerging technologies and the future of business analytics Most businesses are caught in a massive, non-stop stream of data. It can become one of your most valuable assets, or a never-ending flood of missed opportunity. Technology moves fast, and keeping upwith the cutting edge is crucial for wringing even more value from your data--Business Analytics for Managers brings you up to date, and shows you what analytics can do for you now"--

In this second edition of Business Analytics for Managers, the authors provided an updated look at business analytics, focusing mainly on the subject of "big data" and the increased use of analytical information in real time processes. Though the 1E touches on these two subjects, there are new terms and theories in the analytics world that need to be reflected and made explicit in a second edition. Topics covered include:

  • Growing open-source technologies like Hadoop and their strengths and weaknesses compared to the traditional way of storing and processing data in a data warehouse
  • The impact of social media. Social media has both become a new data source that can be used for multiple analytical purposes, as well as a channel to deploy analytical results in.
  • Data security
  • Cloud technologies, and
  • Future trends

Business Analytics for Managers 2E will consider all advances in business analytics since the 1E's release in 2010, namely "big data" and new nontraditional technologies like Hadoop, an open-source software framework for distributed storage and distributed processing of very large data sets.

Normal0falsefalsefalseEN-USX-NONEX-NONEMicrosoftInternetExplorer4Open source technologies like Hadoop. Gartner estimates that it will be embedded in 65% of all analytical platforms in 2 years’ time. In regard to the book, it is not about describing Hadoop as a technology, but the alternative way of storing and processing data along with its strengths and weaknesses compared to the traditional way of storing and processing data in a data warehouse.
Foreword xi
Introduction xiii
What Is the Scope of Business Analytics? Information Systems---Not Technical Solutions xvii
Purpose and Audience xix
Organization of
Chapters
xxiii
Why the Term Business Analytics? xxiv
Chapter 1 The Business Analytics Model
1(16)
Overview of the Business Analytics Model
2(4)
Strategy Creation
4(1)
Business Processes and Information Use
4(1)
Types of Reporting and Analytical Processes
5(1)
Data Warehouse
5(1)
Data Sources: IT Operations and Development
5(1)
Deployment of the Business Analytics Model
6(7)
Case Study: How to Make an Information Strategy for a Radio Station
6(7)
Summary
13(4)
Chapter 2 Business Analytics at the Strategic Level
17(30)
Link between Strategy and the Deployment of Business Analytics
19(1)
Strategy and Business Analytics: Four Scenarios
20(12)
Scenario 1 No Formal Link between Strategy and Business Analytics
22(2)
Scenario 2 Business Analytics Supports Strategy at a Functional Level
24(4)
Scenario 3 Dialogue between the Strategy and the Business Analytics Functions
28(2)
Scenario 4 Information as a Strategic Resource
30(2)
Which Information Do We Prioritize?
32(12)
The Product and Innovation Perspective
34(4)
Customer Relations Perspective
38(4)
The Operational Excellence Perspective
42(2)
Summary
44(3)
Chapter 3 Development and Deployment of information at the Functional Level
47(56)
Case Study: A Trip to the Summerhouse
50(9)
Specification of Requirements
51(1)
Technical Support
52(1)
Off We Go to the Summerhouse
53(1)
Lead and Lag Information
54(3)
More about Lead and Lag Information
57(2)
Establishing Business Processes with the Rockart Model
59(2)
Example: Establishing New Business Processes with the Rockart Model
61(11)
Level 1 Identifying the Objectives
62(1)
Level 2 Identifying an Operational Strategy
62(2)
Level 3 Identifying the Critical Success Factors
64(2)
Level 4 Identifying Lead and Lag Information
66(6)
Optimizing Existing Business Processes
72(1)
Example: Deploying Performance Management to Optimize Existing Processes
73(5)
Concept of Performance Management
74(4)
Which Process Should We Start With?
78(21)
Customer Relationship Management Activities
80(4)
Campaign Management
84(1)
Product Development
85(1)
Web Log Analyses
86(3)
Pricing
89(2)
Human Resource Development
91(2)
Corporate Performance Management
93(1)
Finance
94(1)
Inventory Management
95(1)
Supply Chain Management
95(2)
Lean
97(2)
A Catalogue of Ideas with Key Performance Indicators for the Company's Different Functions
99(2)
Summary
101(2)
Chapter 4 Business Analytics at the Analytical Level
103(46)
Data, Information, and Knowledge
106(1)
Analyst's Role in the Business Analytics Model
107(2)
Three Requirements the Analyst Must Meet
109(4)
Business Competencies
110(1)
Tool Kit Must Be in Order (Method Competencies)
111(1)
Technical Understanding (Data Competencies)
112(1)
Required Competencies for the Analyst
113(16)
Analytical Methods (Information Domains)
113(1)
How to Select the Analytical Method
114(2)
The Three Imperatives
116(6)
Descriptive Statistical Methods, Lists, and Reports
122(7)
Hypothesis-Driven Methods
129(4)
Tests with Several Input Variables
130(3)
Data Mining with Target Variables
133(7)
Data Mining Algorithms
139(1)
Explorative Methods
140(3)
Data Reduction
141(1)
Cluster Analysis
141(1)
Cross-Sell Models
142(1)
Up-Sell Models
143(1)
Business Requirements
143(4)
Definition of the Overall Problem
144(1)
Definition of Delivery
144(1)
Definition of Content
145(2)
Summary
147(2)
Chapter 5 Business Analytics at the Data Warehouse Level
149(36)
Why a Data Warehouse?
151(3)
Architecture and Processes in a Data Warehouse
154(21)
Selection of Certain Columns To Be Loaded
156(2)
Staging Area and Operational Data Stores
158(1)
Causes and Effects of Poor Data Quality
159(3)
The Data Warehouse: Functions, Components, and Examples
162(8)
Alternative Ways of Storing Data
170(1)
Business Analytics Portal: Functions and Examples
171(4)
Tips and Techniques in Data Warehousing
175(8)
Master Data Management
175(1)
Service-Oriented Architecture
176(1)
How Should Data Be Accessed?
177(1)
Access to Business Analytics Portals
178(2)
Access to Data Mart Areas
180(1)
Access to Data Warehouse Areas
181(1)
Access to Source Systems
182(1)
Summary
183(2)
Chapter 6 The Company's Collection of Source Data
185(14)
What Are Source Systems, and What Can They Be Used For?
187(5)
Which Information Is Best to Use for Which Task?
192(2)
When There Is More Than One Way to Get the Job Done
194(3)
When the Quality of Source Data Fails
197(1)
Summary
198(1)
Chapter 7 Structuring of a Business Analytics Competency Center
199(22)
What Is a Business Analytics Competency Center?
201(1)
Why Set Up a Business Analytics Competency Center?
202(1)
Tasks and Competencies
203(5)
Establishing an Information Wheel
203(2)
Creating Synergies between Information Wheels
205(2)
Educating Users
207(1)
Prioritizing New Business Analytics Initiatives
208(1)
Competencies
208(5)
Centralized or Decentralized Organization
208(2)
Strategy and Performance
210(3)
When the Analysts Report to the IT Department
213(2)
When Should a Business Analytics Competency Center Be Established?
215(2)
Applying the Analytical Factory Approach
217(2)
Summary
219(2)
Chapter 8 Assessment and Prioritization of Business Analytics Projects
221(26)
Is It a Strategic Project or Not?
222(8)
Uncovering the Value Creation of the Project
224(6)
When Projects Run Over Several Years
230(2)
When the Uncertainty Is Too Big
232(3)
The Descriptive Part of the Cost/Benefit Analysis for the Business Case
233(2)
The Cost/Benefit Analysis Used for the Business Case
235(1)
Projects as Part of the Bigger Picture
235(8)
Case Study on How to Make an Information Strategy Roadmap
240(3)
Summary
243(4)
Chapter 9 Business Analytics in the Future
247(8)
About the Authors 255(2)
Index 257
GERT H. N. LAURSEN is a business consultant who builds analytical organizations around the world. He also builds disruptive business strategies for global market leaders and humanitarian organizations. He has an MBA in digital strategy, a masters degree in marketing, and was named a global thought leader by IBM and SAS Institute.

JESPER THORLUND is a business intelligence consultant and frequent speaker on business intelligence, business analytics, and microeconomics throughout Europe.