Congratulations on your 20th birthday, Google! The Internet search engine has run a remarkable course in the last two decades. As the most visited website in the world and with a worldwide market share of over 90 percent, Google processes more than 3.5 billion search queries per day. Looking at the previous year, this represents an increase of nine percent in search queries. Established over the years as a synonym for Internet search, the search engine is now also available in 173 languages. The subsidiary YouTube records more than 400 hours of newly uploaded video material per minute. With other services such as Google Ads, Gmail, Google Maps, Google Chrome or the Android operating system, Google can also evaluate a significant amount of data for advertising purposes, for example. This has earned the company its reputation as a "data collection machine".
Data, data and more data. Google gives its storage pool a petabyte of additional capacity every day; that is 1,000 terabytes. But is the search engine giant an individual case? Which other industries also have to deal with a flood of data? And how do companies cope with it? In the following paragraphs, I will present some examples of industries that should definitely deal with the topic of data analysis.
Whether through the use of drones, driverless transport systems or smart glasses - digitization is increasingly finding its way into logistics. The amount of data generated here is increasing accordingly. Data is even expected to grow by a factor of 1,000 per decade. At this size, you can say Big Data. From different data sources, generated by users, sensors and processes, a real added value from the flood of data can only be created if it is specifically evaluated and analyzed. Data-based logistics management can support more accurate decisions in real time and increase the efficiency of processes. Optimization potentials can be quickly identified by a targeted visualization of all relevant key figures.
Production is closely linked to logistics and is therefore equally overflowed with data. The development of companies into Smart Factories is further driving data growth. Numerous processes are now intelligently controlled here. In addition, the data can be of great advantage for production planning with the help of Artificial Intelligence methods, such as Machine Learning. Planning replenishment times or work processes is one area that could be improved using these methods. For example, an operation of a machine could always be calculated as 400 minutes in the planning phase, although in reality it only takes about 100 minutes. The large time buffers lead to high inactivity times of the machines. Such misjudgments in the planning phase can be enormously improved by the "learning" of the machines.
In addition, applications such as Predictive Analytics can advance the predictive maintenance of machines in production and thus, prevent delays in production or machine failures.
Another industry that has to deal with Big Data securely and on a daily basis is the financial sector. According to the forecast, cashless payment transactions will make up a total of 725.9 billion transactions by 2020. By comparison, in 2015, there were 433,1 billion cashless transactions.
The large amount of data makes it essential for financial service providers and banks to have a continuous overview of all transaction flows and data. This is the only way they can respond to the needs of their customers, while protecting them from the risk of fraud. Targeted data analysis supports rapid, forward-looking responses to identify fraud cases and keeps an eye on customer portfolios and transaction flows.
Data volumes are not the same as information volumes. It’s not just data giant Google that is aware of this. Other industries also need to transform growing amounts of data into meaningful information. This is the only way it can be used to support decision-making. The intelligent use of Big Data has become an important competitive advantage for companies in the most diverse areas such as logistics, production and finance.
New trends and further developments improve data analysis for companies. Applications such as Predictive Analytics or Machine Learning present solutions for data analysis with new challenges, but also offer enormous potential for further optimizing your own business processes.
In which area do you struggle with the analysis of large amounts of data? Do you already use intelligent data analytics tools?