What are the requirements for big data
Big data is the merging of data from different sources in order to obtain the most comprehensive data possible. Visual analyzes can help, among other things, to interpret this amount of data more easily. The total amount of data is used, for example, for even more efficient targeting or customer relationship management. The overriding goal when collecting big data is to increase conversions or sales. The data stocks, which are referred to as big data, require corresponding storage capacities that can no longer be measured in gigabytes. However, there is no fixed limit from which to speak of big data.
With the rapid development of storage media, the possibilities of analyzing even larger amounts of data and making profit have increased. In addition, the amount of data is increasing just as rapidly worldwide. It is assumed that the data volume will double worldwide in two-year steps. The reason for this is in turn to be seen in the increasing digitization of the world. Because while data stocks were previously maintained or recorded manually, machines and fast computers now do this. From shopping in the supermarket to booking a trip to ordering in a restaurant or managing health insurance data: all steps are recorded, managed and organized by computers.
Big data is consequently a consequence of a general human trend to produce ever larger volumes of data. Today, big data is used in both science and business.
Technical requirements 
Processing large amounts of data requires many individual steps. With conventional technology, however, big data can no longer be processed efficiently. Because that requires the software used,
- that it can process many data sets at once
- that it can import large amounts of data quickly
- that it makes databases available quickly
- that it can process several database queries at the same time
Such requirements are met not only by paid programs such as NeuroBayes, but also by software such as Hadoop.
Possible uses of big data 
Big data, especially large companies, promise decisive advantages over competitors who have less data at their disposal. At the same time, costs can be saved in many ways if entire operational processes can be controlled solely on the basis of automatically read out data. In science, too, completely new evaluation approaches can be tested on a statistical basis, which are only possible with the help of big data.
The following possible applications are conceivable and are used in practice:
- an automated and fast market research that can react immediately to changes
- Detecting abuse in financial transactions
- Comprehensive web analyzes to increase and optimize online marketing measures
- comprehensive medical diagnostics
- Control of energy consumption, e.g. in an intelligent power grid
- Expanded possibilities in e-commerce through flexible up-selling or cross-selling
- Raster search or profiling for secret services or police
Critical handling of large databases 
Big data is seen as an important component in online marketing. Large brands in particular can work with huge amounts of data that provide even more marketing potential. However, similar to targeting, big data is often criticized for the fact that high-precision user profiles can be created with large amounts of data. As a result, Big Data offers a major intrusion into the privacy of users. Companies that work with big data should inform their customers or visitors to their websites in their data protection regulations that user data will be processed further.
Companies like Google or other search engine providers that finance themselves with advertising have been working with big data for years, which they feed from user data and other available sources. The regular debates about data protection make the problem area “big data” clear, as it gives individual companies too much data sovereignty. But also with other users of big data there is an increased risk that data will be misused and that this misuse will harm citizens in the long term.
Another point of criticism of big data is that the analysis methods are based only on algorithms due to the immense amount of data and therefore have a very technical orientation. However, the IT industry is still at the beginning when it comes to handling huge amounts of data and even more precise evaluation methods can be expected.
Benefits for SEO 
When looking at the possibilities offered by web analytics tools like Google Analytics, SEOs also benefit from big data. Because through benchmark comparisons, keyword tools, etc., search engine optimizers receive partial results from an enormous amount of data in a clearly arranged manner in order to use them for the optimization of their projects. Large companies, on the other hand, that merge their data stocks can also provide important aspects for SEO strategies and targeting.
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