What is Data Protection and Data Integrity

As a matter of principle, when it comes to data protection, a differentiation

must be made between personal data and data involving companies. As soon

as inferences can be made to a specific individual and single data levels are

being worked at, a moment has to be taken to consider: What is being processed? Is there already a business relationship? Which permissions or legal

consent elements are at hand? Customer data may not be collected without

permission and may also not be resold. Anybody who acts carelessly here can

quickly render themselves liable to prosecution.

In principle, the following applies however: Almost anything is possible

with the customer’s consent. This is the reason why Facebook can act with

the data to such an extent, because consent has been given, even if only few

users have probably fully read and understood the Terms of Use. Likewise, a

relatively far-reaching data processing in the scope of an ongoing customer relationship under the motto “for our own purposes” is possible and permit-

ted. This could cover the likes of market research, acquisition activities and

advertising.

In correlation with digitalisation, we frequently hear the keyword data

integrity: It is in fact existential for businessmen as nobody can or wants

to divulge more data on the Internet than absolutely necessary Data integrity means nothing other than knowing exactly what is happening to one’s

own data and to only share as much data as is actually necessary. This also

includes critically reviewing the use of one’s data and online services, portals and databases availed of—third-party providers, above all how they handle the entrusted company data. Data integrity thus means for businessmen

who is allowed to find, use and disclose data and when and where.

The following chapters are initially dedicated to the use of algorithms in

all four steps of the marketing process. Afterwards, practical examples as well

as proposals for the right handling of algorithmic marketing will be given.

The anticipated effects of algorithmic marketing on the economy as a whole

will then be briefly presented.

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Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

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