| 000 -LEADER |
|---|
| fixed length control field |
13277cam a2200565Mi 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
u12260 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
SA-PMU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20210418123007.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
141027t20162016enk e 001 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
YDXCP |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
YDXCP |
| Modifying agency |
OCLCO |
| -- |
CHVBK |
| -- |
OCLCO |
| -- |
OCLCQ |
| -- |
IGA |
| -- |
OCLCO |
| -- |
U3G |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119027034 |
| Qualifying information |
(paperback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1119027039 |
| Qualifying information |
(paperback) |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)946545337 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
HD30.28 |
| Item number |
.M37 2016 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658.4012 |
| Edition number |
23 |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) |
|---|
| OCLC library identifier |
CHNEW |
| System control number |
000869911 |
| 029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC) |
|---|
| OCLC library identifier |
CHVBK |
| System control number |
366490079 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Marr, Bernard, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Big data for small business for dummies / |
| Statement of responsibility, etc. |
by Bernard Marr. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Chichester, West Sussex, United Kingdom : |
| Name of producer, publisher, distributor, manufacturer |
Wiley, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2016. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2016 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 242 pages ; |
| Dimensions |
24 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Media type code |
n |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Carrier type code |
nc |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
--For dummies |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Capitalise on big data to add value to your small business Written by bestselling author and big data expert Bernard Marr, Big Data For Small Business For Dummies helps you understand what big data actually is and how you can analyse and use it to improve your business. Free of confusing jargon and complemented with lots of step-by-step guidance and helpful advice, it quickly and painlessly helps you get the most from using big data in a small business. Business data has been around for a long time. Unfortunately, it was trapped away in overcrowded filing cabinets and on archaic floppy disks. Now, thanks to technology and new tools that display complex databases in a much simpler manner, small businesses can benefit from the big data that's been hiding right under their noses. With the help of this friendly guide, you'll discover how to get your hands on big data to develop new offerings, products and services; understand technological change; create an infrastructure; develop strategies; and make smarter business decisions. * Shows you how to use big data to make sense of user activity on social networks and customer transactions * Demonstrates how to capture, store, search, share, analyse and visualise analytics * Helps you turn your data into actionable insights * Explains how to use big data to your advantage in order to transform your small business If you're a small business owner or employee, Big Data For Small Business For Dummies helps you harness the hottest commodity on the market today in order to take your company to new heights. |
| 505 00 - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Note continued: |
| Miscellaneous information |
ch. 16 |
| Title |
Ten Key Big Data Collection Tools -- |
| -- |
Smartphone GPS Sensor -- |
| -- |
Smartphone Accelerometer Sensor -- |
| -- |
Telematics System -- |
| -- |
Wi-Fi Signals -- |
| -- |
LinkedIn -- |
| -- |
Facebook -- |
| -- |
Twitter -- |
| -- |
Machine Sensors -- |
| -- |
Transaction Data -- |
| -- |
Finance Data. |
| 505 00 - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Machine generated contents note: |
| Title |
About This Book -- |
| -- |
Foolish Assumptions -- |
| -- |
Icons Used in This Book -- |
| -- |
Beyond the Book -- |
| -- |
Where to Go from Here -- |
| Miscellaneous information |
ch. 1 |
| Title |
Introducing Big Data for Small Businesses -- |
| -- |
Why Big Data Matters to Every Business, Big and Small -- |
| -- |
Entering a world filled with data -- |
| -- |
Understanding the infinite ways to use big data -- |
| -- |
Using big data in small businesses -- |
| -- |
Understanding Big Data in More Detail -- |
| -- |
Breaking big data down into four Vs -- |
| -- |
Why big data is so big right now -- |
| -- |
Turning data into big data -- |
| -- |
Data, Data Everywhere -- |
| -- |
Discovering structured, unstructured, internal and external data -- |
| -- |
Getting acquainted with new types of data -- |
| -- |
Making Key Big Data Decisions -- |
| -- |
Understanding the value of insights -- |
| -- |
Building big data skills and competencies -- |
| -- |
Getting the infrastructure in place -- |
| -- |
Making Big Data Work for You -- |
| -- |
Starting with a plan -- |
| -- |
Using big data to change your decision making -- |
| -- |
Transforming your business operations -- |
| Miscellaneous information |
ch. 2 |
| Title |
Digging into the Essence of Big Data -- |
| -- |
Breaking Big Data into Four Vs -- |
| -- |
Growing volumes of data -- |
| -- |
Increasing velocity of data -- |
| -- |
Exploding variety of data -- |
| -- |
Coping with the veracity of data -- |
| -- |
Introducing a fifth V -- Value -- |
| -- |
Understanding Why Big Data is Such Big News -- |
| -- |
Promising predictability -- |
| -- |
Making more fact-based decisions -- |
| -- |
Challenging causality -- |
| -- |
Why Now? A Cloud Full of Data -- |
| -- |
Harnessing more storage than ever before -- |
| -- |
Fueling big data with faster networks -- |
| -- |
Taking advantage of new and better analytical capability -- |
| -- |
What Next for Big Data? -- |
| -- |
Bracing for the big data backlash -- |
| -- |
Encouraging transparency and ethics -- |
| -- |
Making sure you add value -- |
| Miscellaneous information |
ch. 3 |
| Title |
Identifying Big Data Uses in Small Businesses -- |
| -- |
Understanding Your Customers and Markets -- |
| -- |
Getting a 360-degree view of your customers -- |
| -- |
Understanding (and predicting) trends in your industry -- |
| -- |
Reviewing the competition -- |
| -- |
Improving Your Operations -- |
| -- |
Gaining internal efficiencies -- |
| -- |
Challenging your business model -- |
| -- |
Optimising your supply chain -- |
| -- |
Tackling Your Key Business Enablers -- |
| -- |
Recruiting and managing talent -- |
| -- |
Dealing with IT and security -- |
| -- |
Transforming research and development -- |
| -- |
Predicting Performance -- |
| -- |
Unlocking connections in data sets -- |
| -- |
Unleashing weather data -- |
| -- |
Using big data as your test bed -- |
| Miscellaneous information |
ch. 4 |
| Title |
Unpacking the Many Types of Data -- |
| -- |
Deploying Order: Structured Data -- |
| -- |
Digging into the pros and cons of structured data -- |
| -- |
Examples of structured data -- |
| -- |
Coping With Messy Data: Unstructured or Semi-Structured Data -- |
| -- |
Understanding the pros and cons of unstructured or semi-structured data -- |
| -- |
Examples of unstructured or semi-structured data -- |
| -- |
Discovering the Data You Already Have (Internal Data) -- |
| -- |
Weighing up the pros and cons of internal data -- |
| -- |
Examples of internal data -- |
| -- |
Accessing the Data That Is Out There (External Data) -- |
| -- |
Delving into the pros and cons of external data -- |
| -- |
Examples of external data -- |
| -- |
What Type of Data is Best for Me? -- |
| Miscellaneous information |
ch. 5 |
| Title |
Discovering New Forms of Data -- |
| -- |
Tracking Activity Data -- |
| -- |
Pros and cons of activity data -- |
| -- |
Using activity data -- |
| -- |
Getting your hands on activity data -- |
| -- |
Eavesdropping on Conversations -- |
| -- |
Pros and cons of conversation data -- |
| -- |
Using conversation data -- |
| -- |
Getting your hands on conversation data -- |
| -- |
Picturing Images and Photos -- |
| -- |
Pros and cons of photo and video image data -- |
| -- |
Using photo and video image data -- |
| -- |
Getting your hands on photo and video image data -- |
| -- |
Sensing Your Way to New Data -- |
| -- |
Pros and cons of sensor data -- |
| -- |
Using sensor data -- |
| -- |
Getting your hands on sensor data -- |
| -- |
Discovering the Internet of Things -- |
| Miscellaneous information |
ch. 6 |
| Title |
Understanding the Technology Changes that Underpin Big Data -- |
| -- |
The Perfect Storm: Developments that Make Big Data Possible -- |
| -- |
Introducing the cloud -- |
| -- |
Transforming data storage -- |
| -- |
Diving into data lakes -- |
| -- |
Revolutionising analytic technology -- |
| -- |
Breaking Down the Analytic Possibilities -- |
| -- |
Text analytics -- |
| -- |
Speech analytics -- |
| -- |
Image analytics -- |
| -- |
Video analytics -- |
| -- |
Data mining -- |
| -- |
Combined analytics -- |
| Miscellaneous information |
ch. 7 |
| Title |
Focusing on the Value of Insights -- |
| -- |
Moving from Data to Insights to Knowledge -- |
| -- |
Turning data into insights -- |
| -- |
Translating insights into actionable knowledge -- |
| -- |
Feeding humans and machines -- |
| -- |
Getting Insights to the People Who Need Them -- |
| -- |
Grabbing attention -- |
| -- |
Making the insights easy to access and digest -- |
| -- |
Getting the Insights to the Machines that Need Them -- |
| -- |
Understanding machine learning -- |
| -- |
Connecting data with machines -- |
| -- |
Connecting data with processes -- |
| Miscellaneous information |
ch. 8 |
| Title |
Developing and Accessing Big Data Competencies -- |
| -- |
Big Data and the Skills Shortage Challenge -- |
| -- |
Six Key Big Data Skills Any Business Needs -- |
| -- |
Analysing data -- |
| -- |
Being creative -- |
| -- |
Applying mathematics and statistics -- |
| -- |
Understanding computer science -- |
| -- |
Grasping the business side of things -- |
| -- |
Communicating insights -- |
| -- |
Understanding Two Very Different Types of Data Scientist -- |
| -- |
Building Big Data Skills In-House -- |
| -- |
Developing the people you already have -- |
| -- |
Recruiting new talent -- |
| -- |
Thinking outside the box -- |
| -- |
Sourcing External Skills -- |
| -- |
Tapping into service providers -- |
| -- |
Partnering to succeed -- |
| -- |
Crowdsourcing talent -- |
| Miscellaneous information |
ch. 9 |
| Title |
Building a Big Data Infrastructure -- |
| -- |
Making Big Data Infrastructure Decisions -- |
| -- |
Understanding the key infrastructure elements -- |
| -- |
Evaluating your existing infrastructure -- |
| -- |
Big Data on a Budget: Introducing Big Data as a Service -- |
| -- |
Introducing the Four Layers of Big Data -- |
| -- |
Data source layer -- |
| -- |
Data storage layer -- |
| -- |
Data processing/analysis layer -- |
| -- |
Data output layer -- |
| -- |
Sourcing Your Data -- |
| -- |
Collecting your own -- |
| -- |
Accessing external sources -- |
| -- |
Storing Big Data -- |
| -- |
Understanding Hadoop and MapReduce -- |
| -- |
Understanding Spark -- |
| -- |
Other considerations: Data ownership and security -- |
| -- |
Turning Data into Insights -- |
| -- |
Processing and analysing data -- |
| -- |
Understanding Python -- |
| -- |
Popular data analytics platforms -- |
| -- |
Presenting the Insights -- |
| -- |
Getting to grips with the main data output options -- |
| -- |
Looking at the visualisation tools available -- |
| Miscellaneous information |
ch. 10 |
| Title |
Creating a Big Data Strategy -- |
| -- |
Deciding How to Use Big Data -- |
| -- |
Using big data to improve your business decisions -- |
| -- |
Using big data to transform your business operations -- |
| -- |
Small Can be Beautiful: How Not to Collect Everything -- |
| -- |
The Key Steps in Creating Your Big Data Strategy -- |
| -- |
The six components of your big data strategy -- |
| -- |
Making a solid big data business case -- |
| Miscellaneous information |
ch. 11 |
| Title |
Applying Data in Your Business: Decision Making -- |
| -- |
Starting with Strategy -- |
| -- |
Introducing the SMART strategy board -- |
| -- |
Completing the SMART strategy board -- |
| -- |
Honing in on the Business Area -- |
| -- |
Identifying Your Unanswered Questions -- |
| -- |
Understanding the power of questions -- |
| -- |
The do's and don'ts of SMART questions -- |
| -- |
Finding the Data to Answer Your Questions -- |
| -- |
Forgetting what you have or what's out there -- |
| -- |
Thinking big and small -- |
| -- |
Identifying What You Already Have or Have Access To -- |
| -- |
Does it exist in some form somewhere? -- |
| -- |
Internal doesn't always mean cheap -- |
| -- |
If you need external data -- |
| -- |
Working Out if the Costs and Effort Are Justified -- |
| -- |
Collecting the Data -- |
| -- |
Deciding who will collect it -- |
| -- |
Deciding when it will be collected -- |
| -- |
Deciding how it will be collected -- |
| -- |
Analysing the Data -- |
| -- |
In-house analysis versus external analysis -- |
| -- |
Combining data to improve and validate insights -- |
| -- |
Presenting and Distributing the Insights -- |
| -- |
Communicating and visualising insights -- |
| -- |
Keeping in mind the target audience -- |
| -- |
Dashboards and infographics -- |
| -- |
Incorporating the Learning into the Business -- |
| -- |
Making the right decisions -- |
| -- |
Finding questions for the future -- |
| Miscellaneous information |
ch. 12 |
| Title |
Applying Data in Your Business: Operations -- |
| -- |
Understanding the Role of Data -- |
| -- |
Using data to improve operational processes -- |
| -- |
Reshaping your business model -- |
| -- |
Sourcing the Required Data -- |
| -- |
Finding external data -- |
| -- |
Using internal data -- |
| -- |
Weighing up Costs and Benefits -- |
| -- |
Making the business case -- |
| -- |
Sourcing alternative data sets -- |
| -- |
Securing Ownership -- |
| -- |
Big data as a business asset -- |
| -- |
Ensuring access rights and ownership -- |
| -- |
Managing the Data -- |
| -- |
Finding the right data storage for you -- |
| -- |
Ensuring data security -- |
| -- |
Avoiding data breaches -- |
| -- |
Establishing Infrastructure and Technology -- |
| -- |
Assessing infrastructure needs -- |
| -- |
Creating the infrastructure -- |
| -- |
Testing and Piloting Operations -- |
| -- |
Transforming Your Operations -- |
| -- |
Running it -- |
| -- |
Looking into the future -- |
| Miscellaneous information |
ch. |
| 505 00 - FORMATTED CONTENTS NOTE |
|---|
| Title |
13 |
| -- |
Creating a Big Data Culture in Your Business -- |
| -- |
Moving to Fact-Based Decision Making -- |
| -- |
Facilitating company-wide buy-in -- |
| -- |
Emphasising the positive impact data can have -- |
| -- |
Allowing Data to Influence Strategy -- |
| -- |
Managing talent -- |
| -- |
Boosting employee satisfaction -- |
| -- |
Increasing operational efficiency -- |
| -- |
Optimising business processes -- |
| -- |
Identifying New or Additional Business Models -- |
| Miscellaneous information |
ch. 14 |
| Title |
Ten Biggest Big Data Mistakes to Avoid -- |
| -- |
Collecting Data on Everything -- |
| -- |
Collecting Only the Fashionable Data -- |
| -- |
Going Straight to External Unstructured Big Data -- |
| -- |
Getting Overwhelmed by the Volume of Existing Data -- |
| -- |
Ignoring Small Data -- |
| -- |
Throwing Money at the Problem -- |
| -- |
Not Matching Big Data to Your Strategic Questions -- |
| -- |
Not Involving the Right People in Your Big Data Strategy -- |
| -- |
Collecting the Data and Not Analysing It -- |
| -- |
Analysing the Data but Not Reporting the Results in a User-friendly Way -- |
| Miscellaneous information |
ch. 15 |
| Title |
Ten Free Big Data Sources -- |
| -- |
Data.gov (US Government Data) -- |
| -- |
US Census Bureau -- |
| -- |
European Union Open Data Portal -- |
| -- |
Data.gov.uk -- |
| -- |
HealthData.gov -- |
| -- |
Google Trends -- |
| -- |
Facebook Graph -- |
| -- |
Weather Data Sources -- |
| -- |
Federal Reserve Economic Data -- |
| -- |
Google Maps |
| 596 ## - |
|---|
| -- |
1 2 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Business planning |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Organizational effectiveness |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Decision making |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Information technology |
| General subdivision |
Management. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Management |
| General subdivision |
Statistical methods. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Decision making |
| General subdivision |
Statistical methods. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Massendaten |
| Source of heading or term |
gnd |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Klein- und Mittelbetrieb |
| Source of heading or term |
gnd |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Entscheidungsfindung |
| Source of heading or term |
gnd |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
BUSINESS & ECONOMICS / Entrepreneurship. |
| Source of heading or term |
bisacsh |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
--For dummies. |
| 938 ## - |
|---|
| -- |
YBP Library Services |
| -- |
YANK |
| -- |
12105545 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
| 994 ## - |
|---|
| -- |
Z0 |
| -- |
SUPMU |
| 948 ## - LOCAL PROCESSING INFORMATION (OCLC); SERIES PART DESIGNATOR (RLIN) |
|---|
| h (OCLC) |
NO HOLDINGS IN SUPMU - 18 OTHER HOLDINGS |