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2014 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Predictive Analytics, Data Mining and Big Data

Myths, Misconceptions and Methods

verfasst von: Steven Finlay

Verlag: Palgrave Macmillan UK

Buchreihe : Business in the Digital Economy

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Retailers, banks, governments, social networking sites, credit reference agencies and télécoms companies, amongst others, hold vast amounts of information about us. They know where we live, what we spend our money on, who our friends and family are, our likes and dislikes, our lifestyles a nd our opinions. Every year the amount of electronic information about us grows as we increasingly use internet services, social media and smart devices to move more and more of our lives into the online environment.
Steven Finlay
Chapter 2. Using Predictive Models
Abstract
You may find it surprising, but there are quite a few management books about predictive analytics, data mining and Big Data that never explain what a predictive model looks like or how you might actually go about using one. Why I don’t know but I assume that some people think managers should leave that sort of thing to the experts. Alternatively some may think that you can’t understand these things without getting into the mathematics behind them, but that’s not my view.
Steven Finlay
Chapter 3. Analytics, Organization and Culture
Abstract
As someone from the UK, the sort of things that come to mind when someone mentions culture are Shakespeare, Buckingham Palace, Mark E. Smith, the King James Bible, Mary Poppins, hobbits, Charles Dickens, Withnail and I, Henry Moore and the League of Gentlemen. These are all quintessential British things that form part of Britain’s rich and diverse cultural heritage, but a society’s culture is far more than just the music, art and literature it creates. In its broadest anthropological sense, culture is about the full spectrum of behaviors and activities that individuals within a society undertake. The arts are a part of this, but culture covers the way people do things, how the pecking order is determined, what is acceptable behavior, what is not acceptable and what is taboo.
Steven Finlay
Chapter 4. The Value of Data
Abstract
Data is a key ingredient in the predictive analytics process. You can’t do predictive analytics without it. In general, more data leads to more accurate models, but not all data is useful. In fact, most data is completely useless and it is very much a case of diminishing returns as more data becomes available. Doubling the amount of data you hold about your customers will not double the predictive accuracy of your models. Likewise, using twice as many customer records to construct a model won’t double the accuracy either. There is also a cost/benefit case to consider, in terms of both the number of data items you consider for inclusion in a model and the number of individual records that are used to construct models.
Steven Finlay
Chapter 5. Ethics and Legislation
Abstract
The way in which governments, corporations and other organizations gather, store and use data about people has been the subject of much debate in recent years. As organizations hold more data and increasingly rely on automated decision making to decide how to deal with people so have concerns grown about the impact this has on us as individuals and as a society.
Steven Finlay
Chapter 6. Types of Predictive Models
Abstract
Predictive models come in all shapes and sizes. There are dozens, if not hundreds, of different methods that can be used to create a model, and more are being developed all the time. However, there are relatively few types of predictive models. The most common ones are:
Steven Finlay
Chapter 7. The Predictive Analytics Process
Abstract
If you are going to place predictive analytics at the heart of an automated decision-making process, then you need to approach it in a controlled and systematic way. It’s very tempting to just let your highly paid data scientists run wild, trusting that their analytical skills will deliver something useful. However, building a decision-making infrastructure based on predictive analytics is just like any other sort of project, whether it be building a new office building, setting up an IT data center, refitting a factory or restructuring an organization. Only a fool would hire a team of builders and let them lose on a greenfield site without an architect having drawn up the plans first, or think that a II their IT problems are solved just because they have bought a truck load of cutting edge hardware.
Steven Finlay
Chapter 8. How to Build a Predictive Model
Abstract
Once you’ve decided what your objectives and timescales a re, who needs to be involved, and how you are going to implement and use the model, the data scientist can get on with the task of building the model. Figure 8.1 (which expands upon Step 4 in Figure 7.1) shows the steps required.
Steven Finlay
Chapter 9. Text Mining and Social Network Analysis
Abstract
Text mining and social network analysis have both come to prominence in conjunction with increasing interest in Big Data. Both deal in large quantities of data, much of it unstructured, and a lot of the potential added value of Big Data comes from applying these two data analysis methods. We shall begin by discussing some of the ways in which text mining can be applied to predictive analytics, before moving on to discuss social network analysis.
Steven Finlay
Chapter 10. Hardware, Software and All that Jazz
Abstract
All large consumer-facing organizations maintain a number of different systems that hold data about people. A standard setup will include:
•Operational systems. These hold information that is directly relevant to managing the relationship that an organization has with its customers. Examples of operational systems include: seat reservation systems, power dialers,1 insurance quotation systems and customer contact systems used for direct marketing. Most operational systems provide only limited analytical and reporting capability; usually just enough to allow the operational status of the system to be assessed : for example, how many cases were processed that day total value of transactions in the system or number of calls waiting to be answered.
Steven Finlay
Backmatter
Metadaten
Titel
Predictive Analytics, Data Mining and Big Data
verfasst von
Steven Finlay
Copyright-Jahr
2014
Verlag
Palgrave Macmillan UK
Electronic ISBN
978-1-137-37928-3
Print ISBN
978-1-349-47868-2
DOI
https://doi.org/10.1057/9781137379283

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