Big Data Analytics is currently considered the IT trend par excellence in the IT industry and is discussed controversially.
The almost inflationary use of big data goes hand in hand with the risk of degenerating into a fashion or plastic word without clear outlines.
In addition, the IT industry is a burned child when it comes to fashion topics: Green IT, SOA, EAI, dot-com bubble, … It is therefore not surprising that the word hype is used again and again in the discussion about big data.
So far, there has been no generally applicable, clear definition of big data. In general, Big Data can be defined as any dataset that exceeds the limits and possibilities of conventional IT.
Big data is about everything that no longer works with traditional technology due to the size of the data, e.g., capturing, storing, searching, distributing, analyzing, and visualizing large amounts of data.
Standard databases and tools are increasingly having problems dealing with the increasing flood of data: Relative databases fail because of their volume,
ETL processes are too slow and have difficulties with the diverse data formats, traditional BI is therefore too slow and can no longer effectively process masses of unstructured data.
Big data analytics describes the systematic evaluation/analysis of large amounts of data with the help of newly developed software. In contrast to conventional software solutions, significant data software includes special functions
Significant data software can form the basis for extensive data analysis. A software program can execute the applications listed under Big Data Analytics.
The company’s situation: increased requirements, growing challenges
As already mentioned, the phenomenon of growing data volumes and the multiplication of data sources is not entirely new. The unique thing about big data seems to come from the corporate environment.
It is the increased requirements on the part of companies that give big data a new dimension. BI software has gained growing strategic importance in companies in recent years.
Accordingly, the number of users has continued to rise, as have the expectations of the topicality and short-term availability of the data and the query performance of the system with a simultaneous need for more complex analysis.
The increased requirements reflect the increased challenges in the business world. Given the increasingly fierce global economic competition, the following applies more than ever: time is money.
Companies that react the fastest to current market developments and align their internal process landscape to market requirements create a decisive competitive advantage.
In addition to the critical factor of time, companies need to quickly understand the increasingly complex structures and their interrelationships.
Effective countermeasures can only be initiated if you know exactly where and what is going wrong in your own company.
In addition, an awareness of the strategic value of data has now established itself in large parts of the corporate world. This awareness is reflected in the fact that medium-sized companies now use BI software almost as standard.
This emerges from the current study by the consultancy and market analyst company Soft Select on the subject of business intelligence.
Those who successfully analyze the enormous data material concerning initially hidden patterns and relationships are often one step ahead of their competitors.
To consider the time and complexity aspects of day-to-day business, high-performance processing of the vast mountains of data is required.
This new constellation has, of course, not gone unnoticed by the software manufacturers. Analogous to the classic BI architecture, new methods, and technologies for capturing, storing, processing, analyzing, and displaying large, poly-structured amounts of data have long been available on the market.
However, the software offered is just as diverse as the problems raised by big data. It is often complicated for companies to see through this confusing market.
In data integration, the main problem lies in the speed and manageability of the poly-structured data.
Software providers are currently combining significant data functions with established data integration tools such as Informatica, Pentaho, or Pervasive.
There are also specialists for integrating poly structured data sources such as Hadoop, Chukwa, Flume, or Sqoop.
Unique file systems such as HDFS from Hadoop and so-called NoSQL (not-only SQL) databases are ideal for storing and high-performance big data processing.
It is essential here that these techniques are harmonized with the classic analytical databases, which continue to take on important functions.
This is the only way to maintain the data’s consistency and carry out typical relational operations without problems.
The MapReduce approach developed by Google is central to the rapid processing of big data. This is based on the following mechanism: A task is broken down into the possible minor parts, then distributed to as many computers as possible for parallel processing, and then merged again.
A high level of parallel processing of poly-structured data is thus possible. Another tool that enables big data to be processed in a matter of seconds is in-memory computing, such as the SAP HANA offered by SAP. The main memory of a computer is used as a data storage device.
In contrast to data stored on a hard drive, this enables a much higher access speed to the data. Some solutions rely on analytical databases.
These are primarily column-oriented databases that break with the familiar concept of traditional row-oriented databases. They filter out unneeded areas and thus enable flexible and, above all, quick access.
With all these technologies, vast amounts of data can be processed at such a speed that one can quite correctly speak of real-time analysis.
Analyzing structured poly data is primarily the modeling based on detailed data that can be observed. Especially the open-source provider R, but also other data mining tools from EMC, SAS, or SPSS have established themselves on the market.
In addition, some devices can cover completely new areas of application, such as text mining or location intelligence, due to their ability to process large amounts of data.
Big data analytics is often used in the business intelligence environment. The aim is to use the knowledge gained from data analysis to optimize company processes and achieve advantages over competitors.
For this purpose, Big Data Analytics examines large amounts of different data available to the company for helpful information, hidden patterns, or other correlations.
Conventional programs for business intelligence are not capable of such comprehensive analyzes of vast amounts of information.
The analyzes obtained and visualized by Big Data Analytics provide results for the optimization of various business processes. They can also be used to support complicated decision-making processes.
In fast-moving markets like today, competitive advantages are essential to building a good business position. This is where the data analysis comes into play.
By analyzing large amounts of data, trends and patterns in the market can be identified, and thus competitive advantages can be generated.
However, the realization of potential savings and the creation of new business areas are sometimes based on these data evaluations.
In particular, the granting of loans can be improved with the help of big data. In this way, the creditworthiness of many customers can be evaluated within a concise time with the help of corresponding data analysis.
The results of such big data scoring far exceed the classic creditworthiness decisions in terms of their objectivity and efficiency.
Marketing is a classic area of application for extensive data analysis. In marketing, however, it is less about the data itself than about the knowledge drawn from big data.
The right decisions can be made on this basis, and the most profitable measures can be implemented.
The data evaluation provides fundamental knowledge about the customers, who they are, what they want, where they shop and get information, and how they want to be contacted.
With the significant data analysis results, marketing experts learn how customer loyalty can be influenced and how lost customers can be won back. And this knowledge, in turn, enables a targeted, effective use of the marketing budget.
Our last application example for big data in companies deals with fraud detection – known as risk prophylaxis.
Time and again, customers use fraudulent scams to steal a product or service. With the help of extensive data analysis, possible irregularities can be identified at an early stage, which can then be checked more closely afterward.
Unwanted or incorrect transactions can also be placed in this way with minimal effort.
In addition to business intelligence, there are several other areas of application for big data analytics. The analysis of large amounts of data can be used, for example, in the fight against crime, in the insurance sector for risk assessment and adjustment of insurance premiums, or the healthcare sector. Further possible application examples are:
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