Monetizing Data (eBook)

How to Uplift Your Business

Andrea Ahlemeyer-Stubbe, Shirley Coleman (Autoren)

eBook Download: EPUB

2018
John Wiley & Sons (Verlag)
978-1-119-12515-0 (ISBN)

Infos

Practical guide for deriving insight and commercial gain from data 

Monetising Data offers a practical guide for anyone working with commercial data but lacking deep knowledge of statistics or data mining. The authors - noted experts in the field - show how to generate extra benefit from data already collected and how to use it to solve business problems.  In accessible terms, the book details ways to extract data to enhance business practices and offers information on important topics such as data handling and management, statistical methods, graphics and business issues. The text presents a wide range of illustrative case studies and examples to demonstrate how to adapt the ideas towards monetisation, no matter the size or type of organisation.

The authors explain on a general level how data is cleaned and matched between data sets and how we learn from data analytics to address vital business issues. The book clearly shows how to analyse and organise data to identify people and follow and interact with them through the customer lifecycle. Monetising Data is an important resource:

• Focuses on different business scenarios and opportunities to turn data into value
• Gives an overview on how to store, manage and maintain data
• Presents mechanisms for using knowledge from data analytics to improve the business and increase profits
• Includes practical suggestions for identifying business issues from the data

Written for everyone engaged in improving the performance of a company, including managers and students, Monetising Data is an essential guide for understanding and using data to enrich business practice.

Andrea Ahlemeyer-Stubbe is Director of Strategical Analytics at the servicepro Agentur für Dialogmarketing und Verkaufsförderung GmbH, Munich, Germany.

Shirley Coleman is Technical Director of ISRU at the School of Mathematics and Statistics, Newcastle University, UK.

Textauszug

List of Figures

Figure 1.1

Where does big data come from?

Figure 1.2

Big data empowers business

Figure 1.3

Roadmap to success

Figure 1.4

Wish list for generating money out of data

Figure 1.5

Monetising data

Figure 2.1

Deming’s ‘Plan, Do, Check, Act’ quality improvement cycle

Figure 2.2

Six Sigma quality improvement cycle

Figure 2.3

Example of data maturity model

Figure 2.4

Data improvement cycle

Figure 3.1

Big data definition

Figure 3.2

Internet of things timeline

Figure 3.3

Example data structure

Figure 3.4

NoSQL management systems

Figure 3.5

Big data structure and latency

Figure 4.1

Supervised learning

Figure 4.2

Unsupervised learning

Figure 4.3

The CRISP‐DM process

Figure 4.4

The SEMMA process

Figure 4.5

General representation of the data mining process

Figure 4.6

Time periods for data mining process

Figure 4.7

Stratified sampling

Figure 4.8

Lift chart for model comparison

Figure 4.9

Lift chart at small scale

Figure 4.10

An example of model control

Figure 5.1

Raw data from a customer transaction

Figure 5.2

Bar chart of relative frequencies

Figure 5.3

Example of cumulative view

Figure 5.4

Example of a Pareto chart

Figure 5.5

Example of a pie chart

Figure 5.6

Scatterplot of company age and auditing behaviour with LOWESS line

Figure 5.7

Scatterplot of design options

Figure 5.8

Ternary diagram showing proportions

Figure 5.9

Radar plot of fitness panel data

Figure 5.10

Example of a word cloud

Figure 5.11

Example of a mind map

Figure 5.12

Location heat map

Figure 5.13

Density map for minivans

Figure 5.14

SPC chart of shipping journeys

Figure 5.15

Decision tree analysis for older workers

Figure 5.16

Gains chart

Figure 5.17

Lift chart

Figure 5.18

ROC curve development during predictive modelling

Figure 6.1

Example of logistic regression

Figure 6.2

Corrected logistic regression

Figure 6.3

Decision tree

Figure 6.4

Artificial neural network

Figure 6.5

Bayesian network analysis of survey data

Figure 6.6

Bayesian network used to explore what‐if scenarios

Figure 6.7

Plot of non‐linear separation on a hyperplane

Figure 6.8

Dendrogram from hierarchical cluster analysis

Figure 6.9

Parallel plot from K‐means cluster analysis

Figure 6.10

Kohonen network with two‐dimensional arrangement of the output neurons

Figure 6.11

SOM output

Figure 6.12

T‐SNE output

Figure 6.13

Correspondence analysis output

Figure 6.14

Association rules

Figure 6.15

Association analysis of products

Figure 6.16

Comparison of customer base and population

Figure 6.17

Relationship between energy usage and deprivation

Figure 6.18

Map showing prices

Figure 7.1

Strategic opportunities

Figure 7.2

How data can boost top‐ and bottom‐line results

Figure 7.3

Typical data request

Figure 7.4

Observed data and usage

Figure 7.5

Maslow’s hierarchy of needs

Figure 7.6

Data sources to empower consumer business

Figure 7.7

Ready information on market opportunities

Figure 7.8

Word cloud from keyword occurrences

Figure 7.9

Using different data sources for analytics

Figure 7.10

Daily sleep patterns

Figure 7.11

Predictive analytics in insurance

Figure 8.1

Pathways to monetising data

Figure 8.2

Segmentation features of walk‐in customers

Figure 8.3

Business opportunities

Figure 9.1

Paths to monetisation

Figure 9.2

Pareto diagram of customer compliments

Figure 9.3

Graphical dashboard

Figure 9.4

Decrypting the DNA of the best existing customers

Figure 9.5

Aspects of digital maturity

Figure 9.6

Closed loop of B2B customer profiling – continuous learning

Figure 9.7

Automated B2B lead generation system

Figure 9.8

New methods, new insights, smart business

Figure 9.9

Misleading scatterplots

Figure 9.10

Scatterplot with multiple features

Figure 9.11

Histogram of suspicious‐quality recordings

Figure 10.1

The evolution of data analytics

Figure 10.2

Cumulative distribution of risk scores

Figure 10.3

Data sources in the shipping industry

Figure 10.4

Optimum speed recommendation

Figure 10.5

Pruned decision tree

Figure 10.6

Detail from decision tree

Figure 10.7

Customised communication

Figure 10.8

Individualised communication

Figure 10.9

Complexity of data mining steps

Figure 10.10

Data in the customer journey

Figure 10.11

Intelligent profiles and segments in B2C

Figure 10.12

Personalised journey

Figure 10.13

The reach of social media

Figure 10.14

The power of social media

Figure 10.15

Using peer group behaviour

Figure 10.16

National statistics oil prices

Figure 10.17

Example of reports portal

Figure 10.18

Making a business out of boring statistics

Figure 10.19

Right place, right time

Figure...

Produkt-Details

• Erscheint lt. Verlag: 1.2.2018
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Naturwissenschaften
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Schlagworte: Andrea Ahlemeyer-Stubbe • Big Data • Business data processing • changes in attitude to data • Computer Science • Data Analysis • Data handling • data handling and quality • data improvement and data transformation • Data Management • data management and customer lifetime • data management and focus options • data management and product lifetime • data matching • Data Mining • data mining and statistical methods • Data Mining Statistics • Data Ownership • Data Science • data storage • Data Types • Datenanalyse • how to monetise data • Informatik • Introduction to monetising data • legacy data sources • Monetising data • Open Data • planning ahead • predictive analytics • Reshaping data to give useful summaries • resources for monetising data • Shirley Coleman • sources of data • Statistics • Statistik • tailored data cleaning and compatibility issues • The data improvement cycle • types of available data • Wirtschaftsinformatik
• ISBN-10: 1-119-12515-4 / 1119125154
• ISBN-13: 978-1-119-12515-0 / 9781119125150