Into the digital future with Big Data
Data-driven business models are nothing new. However, they are becoming more and more important to the success of a company. Outside of advertising and market research, Big Data and data analytics are now also more interesting for Industry 4.0 and production. Those with the ability to collect and analyze data will be able to develop an edge in digitization processes in the future. But what sort of data are we talking about, and how can they be used profitably?
Big Data in communications
Let’s take a step back. Are you familiar with this? You’re interested in products and topics on the Internet. Suddenly, you’re offered content, advertising, and products related to exactly those topics on other websites. Anybody who hasn’t been living on the moon for the last ten years should know that companies collect data on user behavior on the Internet. Algorithms and cookies follow you on your “journey” through the Internet, note your preferences, and analyze the content of the sites you visited online. The result is personalized advertising and content individually tailored to you. This results in a digital profile allocated to xy, which corresponds to a certain behavioral pattern and fits into a target group. Classic pigeonholing. Previously, that was particularly interesting for purposes of market research and for the Marketing and Sales departments. So-called retargeting, which provides advertising and content precisely tailored to individual target groups, is now part of the standard repertoire for communications.
The next step: Data-driven business models in industry
However, the use of Big Data is proceeding unevenly. While communications experts even employ data analysis of users for election campaigns – Barack Obama was probably the first person in political communication to control his messaging via Big Data – industry is only just discovering this method. According to a study on digital customer relations by the digital association bitkom, while 96 percent of companies collect data on their customers, only half (53 percent) evaluate the data and use them specifically for the optimization of their company processes. The association surveyed 1,000 companies with over twenty employees. The digital association developed a readiness model in-house which measures data analytics and optimization based on six dimensions: strategy, culture and personnel, organization, processes, technology, and data. “Customers in the digital world are still strangers to many companies.” This is the conclusion which bitkom drew from their study. This shines a light on how far German companies actually are in their digitization processes. There is still a whole lot of untapped potential!
Increasing quality with industry analytics
Data-driven analyses and business intelligence are themselves subject to constant technical change. The methods become more sophisticated and the strategic uses more specific. Ultimately, it is not a matter of just collecting the data, but evaluating them to find patterns and being able to react to challenges within the company. Thus, statements about future developments in industry can be made based on data. The whole thing has its own name: industry analytics. For example, production managers are able to gain insights into how manufacturing can be optimized, if and what errors are to be expected, or even if complete machine failure threatens. In the initial industry analytics project in a company, participants first define the data needed to analyze a process. To this purpose, expert users determine the goals of the process analysis. Then the parameters which could have an effect on the result must be determined. In the next process step, these are read from the facilities and transmitted to a database. The data must be available in a central repository before reports can be evaluated and patterns analyzed using statistical methods. Patterns in the mountains of data can be identified with data mining algorithms, thus gaining new insights and connections.
The digitization and networking of data brings genuine added value and competitive advantages to companies. Based on concrete analyses, production can be optimally controlled and machine bottlenecks or failures can be better predicted.