The analysis of large amounts of data makes it possible to gain insights. These results can serve as a basis for decisions, for example, regarding the strategicdirection of the company.
Companies, for instance, want to learn more about the preferences of their customers in order to adapt their product range, advertising, and so on, to them.
DeepLearning also uses Big Data: this is a special method of informationprocessing and a sub-area of machinelearning. A machine is “fed” with large amounts of data, which is analysed and used to train the machine. The machine is able to link new information with each other and on this basis can make forecasts and make its own decisions. However, the result is only as good as the data, the machine has “learned” from
One example is a machine translation system that “learns” to correctly translate technical terms in a company by entering data (existing translations).
In addition, authorities and secretservices use large amounts of data to detect discrepancies and anomalies that could indicate criminal or terrorist activities. In science, large amounts of data are used to investigate complexnaturalphenomena such as climate change or the occurrence of earthquakes and epidemics.
However, the large amounts of data are not always handled responsibly. Some companies or institutions do not adhere to data protection regulations, which means that information is released to the public. This can be trivial, but in some cases it can also be dangerous and lead to fraud and blackmail.
Example
In 2015, the Ashley Madison fling portal, where people in search of an extramarital adventure can create a profile, became the victim of a hacker attack. As a result, information about the people registered on the portal became available on the Internet. Information on celebrity flings and personal information such as credit card numbers became public. In addition, those affected were asked by e-mail to pay a ransom so that their life partner would not find out about the profile on the fling portal.
Remember
Large amounts of data can be used for the following purposes, among others:
strategic orientation of companies
Deep Learning
fighting against crime and terrorism
scientific investigation of natural phenomena (e.g. earthquakes and climate change)
unlawful evaluations which may lead to blackmail or fraud
The decisive factor regarding Big Data is not so much the data itself as what happens to it.
Companies in particular benefit from analysing and evaluating Big Data. Both consciously and unconsciously, they generate and store vast amounts of data today. In the following, you will learn in detail what possibilities the correct analysis of large amounts of data offers companies.
Decision-making
By analysing the large amounts of data generated in the company, companies can identify patterns and filter out information. This enables companies to make better business decisions that increase the success of the company. By evaluating machine data, for example, it is possible to calculate at what intervals a machine breaks down. The company can use this knowledge to service the machine before it fails. Big Data is also used in the finance and insurance industry to better calculate risks.
Example
Ms Schmidt is 47 years old and would like to conclude a private health insurance. When visiting her insurance broker, she is surprised about the high costs and enquires. It turns out that her provider analyses large amounts of data in order to better calculate the individual insurance costs. The company finds out, for example, what particular health risks women of this age bear who, like Ms. Schmidt, are smokers, have no children and never do sports.
Increase in efficiency
Competitiveness is very important for companies. In order to keep up with the competition, companies need to design strategies to save costs without compromising performance. Analysing and connecting large amounts of data helps to do this.
Example
Have you ever heard that UPS drivers almost always turn right?
That’s because UPS has discovered, through big-data analysis, that this can save about $10 million a year. You’re probably wondering how that’s possible: the merging of various data sets, such as accident statistics, fuel consumption data, etc., has shown that UPS vehicles are much less likely to be involved in accidents if they don’t turn left. This can save a lot of money, even if the routes become more complicated as a result.
Prediction in research and development
By making existing or potential customers or clients aware of their preference for certain products, research can identify and predict trends, design appropriate marketing strategies and develop tailor-made products. With the appropriate analytical methods, it is also possible, for example, to predict the rupture safety of a product while it is still being developed.
Example
An operator of an online web shop installs cookies and online tracking and tracks the movements of its visitors. He can determine where visitors come from, which products they click on, how often they visit the site, etc. With the help of this data, the operator can adapt the contents of the site and the products offered to the preferences of the visitors and thus increase his turnover.
Personalised customer service
By storing customers’ decisions, companies are able to provide them with personalised customer service. For example, if a user watches a particular movie or series on Netflix, the system will save it and the next time the user logs in, recommendations will be based on the movies or series the user has already watched. But this personalised offer does not always meet approval:
Example
When Mr. Maier realises that his old mountain boots are no longer usable, he searches on Google for “mountain boots new for men”. He is overwhelmed by the many different offers and Mr. Maier also discovers that many products cannot be delivered to his home country, Austria. Mr. Maier decides to get personal advice in a specialist shop and also buys a pair of mountain boots. Nevertheless, he sees more and more advertising for mountain boots on the Internet in the coming days and weeks, as his search query has been saved and analysed on Google. Mr Maier is irritated and feels observed. He decides not to place any more search queries on Google in the future.
Let’s recap once again:
Remember
Companies have numerous opportunities to use Big Data to be more successful. These include:
Decision making:
Big Data analysis enables companies to make better business decisions and better assess risks.
Increased efficiency:
Analysing and linking data (such as weather and congestion data with fuel prices) helps companies to make processes more efficient.
Forecasting in the field of research and development
With the help of Big Data, predictions can be made regarding trends, characteristics of a product, etc.
Personalised customer service
By storing the decisions made by customers, companies can offer them personalised customer service on their next visit.
The analysis of large amounts of data makes it possible to gain insights. These results can serve as a basis for decisions, for example, regarding the strategic direction of the company.
Companies, for instance, want to learn more about the preferences of their customers in order to adapt their product range, advertising, and so on, to them.
Deep Learning also uses Big Data: this is a special method of information processing and a sub-area of machine learning. A machine is “fed” with large amounts of data, which is analysed and used to train the machine. The machine is able to link new information with each other and on this basis can make forecasts and make its own decisions. However, the result is only as good as the data, the machine has “learned” from
One example is a machine translation system that “learns” to correctly translate technical terms in a company by entering data (existing translations).
In addition, authorities and secret services use large amounts of data to detect discrepancies and anomalies that could indicate criminal or terrorist activities. In science, large amounts of data are used to investigate complex natural phenomena such as climate change or the occurrence of earthquakes and epidemics.
However, the large amounts of data are not always handled responsibly. Some companies or institutions do not adhere to data protection regulations, which means that information is released to the public. This can be trivial, but in some cases it can also be dangerous and lead to fraud and blackmail.
Example
In 2015, the Ashley Madison fling portal, where people in search of an extramarital adventure can create a profile, became the victim of a hacker attack. As a result, information about the people registered on the portal became available on the Internet. Information on celebrity flings and personal information such as credit card numbers became public. In addition, those affected were asked by e-mail to pay a ransom so that their life partner would not find out about the profile on the fling portal.
Remember
Large amounts of data can be used for the following purposes, among others:
The decisive factor regarding Big Data is not so much the data itself as what happens to it.
Companies in particular benefit from analysing and evaluating Big Data. Both consciously and unconsciously, they generate and store vast amounts of data today. In the following, you will learn in detail what possibilities the correct analysis of large amounts of data offers companies.
Decision-making
By analysing the large amounts of data generated in the company, companies can identify patterns and filter out information. This enables companies to make better business decisions that increase the success of the company. By evaluating machine data, for example, it is possible to calculate at what intervals a machine breaks down. The company can use this knowledge to service the machine before it fails. Big Data is also used in the finance and insurance industry to better calculate risks.
Example
Ms Schmidt is 47 years old and would like to conclude a private health insurance. When visiting her insurance broker, she is surprised about the high costs and enquires. It turns out that her provider analyses large amounts of data in order to better calculate the individual insurance costs. The company finds out, for example, what particular health risks women of this age bear who, like Ms. Schmidt, are smokers, have no children and never do sports.
Increase in efficiency
Competitiveness is very important for companies. In order to keep up with the competition, companies need to design strategies to save costs without compromising performance. Analysing and connecting large amounts of data helps to do this.
Example
Have you ever heard that UPS drivers almost always turn right?
That’s because UPS has discovered, through big-data analysis, that this can save about $10 million a year. You’re probably wondering how that’s possible: the merging of various data sets, such as accident statistics, fuel consumption data, etc., has shown that UPS vehicles are much less likely to be involved in accidents if they don’t turn left. This can save a lot of money, even if the routes become more complicated as a result.
Prediction in research and development
By making existing or potential customers or clients aware of their preference for certain products, research can identify and predict trends, design appropriate marketing strategies and develop tailor-made products. With the appropriate analytical methods, it is also possible, for example, to predict the rupture safety of a product while it is still being developed.
Example
An operator of an online web shop installs cookies and online tracking and tracks the movements of its visitors. He can determine where visitors come from, which products they click on, how often they visit the site, etc. With the help of this data, the operator can adapt the contents of the site and the products offered to the preferences of the visitors and thus increase his turnover.
Personalised customer service
By storing customers’ decisions, companies are able to provide them with personalised customer service. For example, if a user watches a particular movie or series on Netflix, the system will save it and the next time the user logs in, recommendations will be based on the movies or series the user has already watched. But this personalised offer does not always meet approval:
Example
When Mr. Maier realises that his old mountain boots are no longer usable, he searches on Google for “mountain boots new for men”. He is overwhelmed by the many different offers and Mr. Maier also discovers that many products cannot be delivered to his home country, Austria. Mr. Maier decides to get personal advice in a specialist shop and also buys a pair of mountain boots. Nevertheless, he sees more and more advertising for mountain boots on the Internet in the coming days and weeks, as his search query has been saved and analysed on Google. Mr Maier is irritated and feels observed. He decides not to place any more search queries on Google in the future.
Let’s recap once again:
Remember
Companies have numerous opportunities to use Big Data to be more successful. These include:
Big Data analysis enables companies to make better business decisions and better assess risks.
Analysing and linking data (such as weather and congestion data with fuel prices) helps companies to make processes more efficient.
With the help of Big Data, predictions can be made regarding trends, characteristics of a product, etc.
By storing the decisions made by customers, companies can offer them personalised customer service on their next visit.
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