Large amounts of data (big data) are accumulated on the Internet or in companies every day, but how can they be processed efficiently and profitably? And why is big data so important? You will learn that and more in this article.
The term “Big Data” comes from the English-speaking world and describes the amount of data accumulated, for example, on the Internet or in companies – concerning companies. It is about unstructured and disjointed amounts of data produced daily.
More and more technologies and programs process these mountains of data and allow companies to benefit from advanced and data-based analyses.
What is Big Data?
Big data essentially describes two aspects: on the one hand, the term describes faster-growing amounts of data ever. On the other hand, it is also about new and compelling IT solutions and systems to work profitably. Especially data from z. B social networks make up a large part of this amount of data.
Big data is often used as a collective term for modern digital technology. In digitization, big data is increasingly influencing the collection, management, utilization, and, above all, digital data analysis.
Why is Big Data So Important?
Big data starts where conventional approaches to information processing reach their limits. Big data is, of course, also about the amount of data a company collects, but much more about what is ultimately done with it.
One of the primary goals is to discover reproducible business patterns. The correct use of data enables costs to be reduced, time to be saved, new products to be developed, offers to be optimized, and well-founded business decisions to be made. Users can do the following tasks more efficiently, among others:
- recognize fraudulent behavior in good time,
- Recalculate risk portfolios within a few minutes,
- foresee possible future events and results,
- Determine causes of problems, defects, and malfunctions almost in real-time,
- create vouchers based on customer buying habits.
As already described, the real added value of big data lies in the evaluation – also known as data mining. This term summarizes the entire process of preprocessing, searching, and evaluating data – i.e., the systematic use of methods for analysis with the aim of pattern recognition.
From a technical point of view, algorithms are used in data mining that helps to establish connections between the amounts of data and thus to discover cross-connections and trends. It is essential here: Data mining is only about the evaluation and not about the generation of new data.
Competitive Advantage With The Help of Big Data
Due to the enormous amounts of data that arise within a company, those responsible have entirely new insights into customers’ interests and behavior. Companies use analysis methods to filter and process this information. These methods provide insightful information about the customer – hidden patterns, unknown correlations. In this way, companies generate competitive advantages over their competitors.
Therefore, advanced data management is the prerequisite for process optimization since providing information in real-time is the basis for entirely new business processes. In addition, the growing technical possibilities offer companies the opportunity to use big data projects to simplify the system infrastructure and reduce the number of databases.
Software Tools For Analysis
There are several different software tools for analysis. One example of this is predictive analytics from SAP. The software uses big data for predictive analysis. Users get a forecast of future events and likely results based on the analysis of historical data. The software thus creates added value for companies, as decisions in every conceivable area can be made more well-founded based on forecast models.
This method is used for. B. in maintenance. To make predictions, large amounts of data are collected, stored, and analyzed. Storage and analysis technologies from big data, such as data lakes and analytical databases, are used.
The basis for predictive maintenance are systems that are equipped with sensors and networked, embedded systems.
Big Data Use Cases
As described in the previous example, there are already areas in which big data is used. We have put together a selection of other fields of application here:
- Retail: In retail, using big data is key to building customer relationships. Retailers need to know what is most likely to appeal to their customers, which products which target group is buying, and what effective customer acquisition strategies are. Big data analyzes forms a reasonable basis for implementing this.
- Health care: In the health care system, accuracy, but also speed, are the top priorities – and in many cases also sufficient transparency to meet the strict legal requirements. With the help of efficient, extensive data management, healthcare providers can gain the knowledge they need to improve patient care.
- Banking: Because of the large amounts of information, banks have to find new, innovative ways of handling large amounts of data. Minimizing risk and fraud in compliance with legal regulations is as important as a positive customer experience and satisfied customers.
- Education: With the help of big data analytics, educational institutions can improve systems and curricula. This means that better systems for evaluating and supporting teachers and managers can also be implemented.
- Sales and Marketing: Big Data makes it easier for the sales and marketing departments to tailor product offerings to customer segments or individual customers and thus minimize losses in the customer base. The observation of the market and competition can therefore be expanded significantly with big data analyzes.
Big data will develop into a critical business success factor in the future. Therefore, it is already crucial for companies to take the time to collect, store, and process big data. Do you have any questions about big data or possible big data tools? Then we can help you. The mind square consultants are experts in the field – contact us, and we will be happy to advise you.