"Big data", "data processing", "analytics" or even "data mining" are buzzwords today - and rightly so, because the volume of data has never been greater and companies have never had more opportunities to use these large volumes of data profitably for their own purposes.
Nevertheless, a considerable part of the potential of corresponding data flows still remains unused. For many managers, dealing with the enormous variety of digital information that comes their way every day seems too complex and often simply not useful enough.
In fact, this is a big mistake. By using Big Data, companies can optimize their typical business processes extremely efficiently and ultimately offer (potential) customers better service and even more targeted goods or services. This results in an enormous unique selling proposition in many industries (still). Because, as already mentioned, Big Data has by no means reached every company yet.
The more communication - in the most diverse forms - takes place digitally, the larger the volumes of data that are created, transferred and ultimately stored. Today, the collective term "Big Data" stands for all processes and technologies that are relevant for the collection, analysis, utilization (data mining), structuring, further use and also the marketing of corresponding information.
Information considered in the context of Big Data are website and social media activities of (potential) customers, telephone connection data, server protocol data, machine and plant data, supply chain management data, warehouse and intralogistics data, transaction data, sensor information and others.
The Big Data Volume, Big Data Velocity, Big Data Variety, Big Data Variability and Big Data Varacity assumptions serve as the basis for applicable processes. These define that data can come from an almost incomprehensible number of sources and that it must be stored appropriately(Big Data Volume). As a rule, the data flows onto companies at an enormous speed(Big Data Velocity) and appear in a wide variety of formats(Big Data Variety), which must be handled appropriately. In addition, the flow of data is unpredictable or irregular(Big Data Variability), whereby trends in the form of peaks or dips must be correctly interpreted. In all of this, the quality of the data plays a decisive role in order to be able to assess data stocks, combine them in a beneficial way and ultimately use them in a way that is really maximally useful.
The greatest economic opportunities of Big Data lie in the fact that the analysis of correlations in information sets allows entrepreneurial decisions that are based less than ever on assumptions and more on verifiable knowledge. As a result, companies that pursue a good strategy with regard to their processing of data can operate highly economically on the market.
This, together with the great relevance of Big Data for digital technologies that tend to be immensely revenue-generating, such as Machine Learning and Deep Learning, makes its adoption in many business contexts a key premise for long-term competitiveness. In fact, Deep Learning needs Big Data, as it is the only way to crystallize hidden patterns without having to overly customize the information. The rule is: the more good quality data available, the better the results.
Where there is a lot of light, there is usually also shadow - and so Big Data also brings with it some risks that must definitely be taken into account. Data protection should be highlighted here in particular. If the collection and use of large amounts of data does not comply with applicable regulations, there is a risk of severe penalties. Such negative effects are even more drastic if the information is hacked. Furthermore, there is always the risk of serious misjudgements - not only with regard to legal requirements - when companies use big data without sufficient competencies.
Before Big Data can be used meaningfully, companies should figure out how the data flows between the increasingly numerous locations, sources, systems, providers and users involved. An extremely extensive data network must be defined and brought under control.
The first step is to draw up a procedural plan. The aim should be to obtain an overview of one's own possibilities for the collection, storage, management and use of data. Clear goals are of elementary importance here. Taking these factors into account, a common thread emerges, which clarifies the central relevancies. Without this, Big Data can never be used efficiently and ultimately effectively.
The most important Big Data sources in the respective company context must then be determined. Of course, the web offers enormous potential here. Corresponding data streams run in particular via websites, social media and search engines. Relevant information manifests itself, among other things, in click paths, dwell times, interactions on YouTube, Facebook, Instagram, etc., as well as the use of images, videos, podcasts, texts, voice inputs, and more. As these and other Big Data manifestations arrive, those must be analyzed and decisions made about what information to store or discard. It's not just "proprietary" data that can be used. Moreover, public sources, such as the US government website data.gov, the CIA World Factbook or the EU's open data portal, are sometimes very useful additional resources. Here, companies gain access to data in vast quantities. Furthermore, Big Data can come from Data Lakes or Data Clouds or from suppliers or customers.
The information defined as particularly useful must be easily accessible or organized and stored. In order to create these preconditions, companies can fall back on a multitude of computer systems, some of which are very powerful. The possibility of a differentiated classification of the quality of the data is extremely important here, since only with precisely coordinated information can maximum meaningful analyses be carried out and these finally be used in a really beneficial way.
Just a few years ago, analyzing big data was a highly difficult undertaking. In view of today's relatively easily accessible high-performance technologies, such as in-memory analytics or grid computing, however, this situation has been put into perspective. Organizations now have the option of analyzing their entire Big Data inventory or specific parts of it in a comparatively uncomplicated and cost-effective manner.
After analysis, intelligent, data-driven decisions are made. High-quality, trustworthy and competently maintained and analyzed data leads to sustainable decisions. This and all other central Big Data conditions in the respective company context must be established by those responsible at all important points and to the full extent in the company. This is the only way to ensure the most efficient processes possible with regard to the collection of Big Data and the processing of the data. At the same time, it is always important to be aware that gut feelings and instinct must not play a role in the decisions concerned and that the data are the measure of all things.
- In order to increase your own competitiveness, a digital strategy is necessary - we will gladly advise you with an open mind and support you in the decision-making process.
- In this step, we would also be happy to conduct a status analysis of your previous digital activities and jointly develop a digital guide for the optimization of your digital strategy.
- Our years of extensive experience enable us to implement adigital strategy in a targeted manner, through implementation and performance measurement to daily support.
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