Due to the strong digitalization of the last decades, companies and systems are confronted with the analysis of ever-increasing amounts of data. Classic BI structures are overwhelmed with the analysis of large data volumes, making it difficult for companies to access and evaluate internal and external data.
In classic BI solutions, data warehouses based on relational databases act as data storage. Since relational databases require a certain structure, it is necessary to prepare unstructured data in advance and to generate metadata, which requires immense computing and storage capacities and results in low query speeds. In view of the high volume of data in Big Data, relational databases are increasingly reaching the limits of economic feasibility in BI applications – with sometimes very mediocre performance.
An unbroken trend in the BI industry is therefore the development of new software that enables and optimizes the analysis of Big Data.
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Big Data Analytics in practice
Powerful Big Data software can process different data sets simultaneously, enables the import of large amounts of data and offers the possibility to analyze different types of information. This is a major advantage, especially when analyzing unstructured data (for example, from social networks).
Data utilization in the cloud represents a more recent trend. Cloud solutions offer potential for more flexible performance, reduction of service costs and faster renewal of system environments. However, there are still objections from the corporate side regarding data protection and data security.
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Big Data software: structure and products
Depending on the requirements, there are various software offerings on the market:
The focus is primarily on evaluating qualitative data and transforming it into quantitatively usable information for creating graphics or tabular reports. Unstructured or polysaturated data account for a significant share of the general data volume. However, it is becoming apparent that many companies do not yet have the necessary expertise to work with unstructured Big Data information in a meaningful way.
There are different types of information that can be captured with appropriate Big Data software. These include texts as well as voice recordings, which can be processed by analyzing linguistic and semantic speech patterns. The use of images such as logos can also be tracked. Most of these technologies have emerged from the THESEUS research project.
Important for the import of large amounts of data from different data sources is a powerful connection to basic connectors. These ensure access to the Internet as well as to files or databases. Depending on the source, the data must also be converted for further processing. Efficient connectivity therefore also results from support by standard formats, which must be considered when selecting software solutions. An open-source framework for importing, distributing, and accessing Big Data is SMILA. Google’s Hadoop system is also available open source. These systems are specifically designed to process unstructured data to take full advantage of the new range of information that Big Data offers. Legacy BI architectures severely limit analysis by pre-selecting and structuring data. Hadoop, on the other hand, is not locked into any structure. The structures can even change without the need to adapt the data store or interrupt loading processes.
Fast processing of Big Data is realized by Map Reduce approaches. This is an algorithm that first divides the data sensibly between different computers or file systems (see e.g. HDFS) and then reassembles them sensibly at the end. Furthermore, there are in-memory computing offerings such as SAP HANA. In this method, the RAM of computers is used for data storage.
For example, HDFS (Hadoop Distributed File System) is available as a file system for storing data and further processing with MapReduce. HDFS was developed specifically for the implementation of MapReduce. As an alternative, the file system GFS is available.
Different databases are available for the use of Big Data. First, there are SQL systems for relational data. These are suitable for processing the traditional BI information, i.e. for processing structured data. Furthermore, there are NoSQL databases. These are geared towards the new unstructured data, i.e. the evaluation of texts, audio files or videos. However, it is important to note that different databases must be used depending on the type of data. Examples of document databases are MongoDB or Couch DB. However, there are also special solutions for graph databases or key value stores.
For the area of Big Data Analytics, different data mining products are on the market. Popular tools are for example SPSS or SAS. However, open-source products such as “R” have also established themselves on the market.
Application examples for Big Data Analytics: Big Data can bring companies decisive advantages
The commercial use of Big Data is extremely versatile and can be applied in various industries:
Sales, Marketing and CRM
The main task of Big Data analytics in marketing includes cost reduction. Big Data enables companies to tailor their product and service offerings more precisely to their customers. For example, revenue increases can be measured through marketing efforts.
The analysis of sales transactions can also bring companies revenue increases. For Big Data evaluation, all known information from customers, which includes transactions, location data, demographics, or preferences, is brought together and evaluated. On this basis, patterns of purchasing decisions can be derived and new cross-selling potential can be tapped.
Big Data also facilitates competitive intelligence. Big Data software can extract all relevant information from the press, competitor sites and any social networks and converting it into statistically analyzable data. For this purpose, texts are read from computer screens, for example, or semantic subtleties in texts are registered. SEO information is also included in the analysis. Reports on relevant markets and competitors can be created from the collected data, thus forming a basis for more effective corporate and marketing strategies.
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Furthermore, forecasts about customer satisfaction or potential churn can be created. For online devices in particular, information about possible network or quality problems can be obtained and counteracted in good time with targeted discount campaigns or benefit packages.
The management of customer contact by mail can also be improved by analyzing response rates and adjusting distribution lists.
In this way, big data supports customer relationship management at crucial points and promotes customer loyalty and acquisition.
Big Data analytics in product development
In the development departments of companies, Big Data can help with the collection and evaluation of customer reviews. In addition, there is information from opinion forums or social media platforms. Companies can gather valuable information here about product weaknesses or development suggestions. The market is also interesting for identifying new trends and market gaps as well as developing new products. With this information advantage, companies can secure competitive advantages for the launch phase of a product and thus increase sales opportunities. In addition, conclusions can be drawn about general brand perception.
Big Data for production and support
Big Data can contribute to optimization in production. All processes can be accurately recorded via sensors and then compiled in large databases. For example, in many highly engineered processes in production chains, such as oil production, sensors are installed on all critical machinery, allowing technical monitoring to immediately detect malfunctions in the mechanisms. Thus, the collection of Big Data ensures preventive maintenance and can prevent production delays or failures.
Especially when a product consists of various components from different manufacturers (e.g. in the automotive industry), the overarching quality assurance can be decisively improved by Big Data technologies.
Big Data for Production and Support
Big Data can contribute to optimization in production. All processes can be precisely recorded via sensors and then compiled in large databases. For example, in many highly engineered processes in production chains, such as oil production, sensors are installed on all critical machinery, allowing technical monitoring to immediately detect malfunctions in the mechanisms. Thus, the collection of Big Data ensures preventive maintenance and can prevent production delays or failures.
Especially when a product consists of various components from different manufacturers (e.g., in the automotive industry), overarching quality assurance can be decisively improved by Big Data technologies.
Supply chain management
When analyzing supply chains, different information from production sites, the intermediate storage facilities, and the transport routes flow together and form a confusing collection of data. At this point, Big Data software helps to bring order into the chaos and optimize access to data from external locations.
Particularly in the case of transport vehicles, there is increasing networking with companies. For example, the vehicle can provide information ranging from consumption to wear and tear to position data, and in return is informed about alternative routes or changes in loading. This creates an advantage for companies by reducing empty runs, shortening travel times or planning vehicle maintenance.
This technology has also been applied to aircraft. Aircraft engines, for example, can send data to the control center and indicate malfunctions at an early stage. This gives the transportation industry an enormous safety advantage.
Big data management for finance and insurance
The world of finance also benefits from Big Data. Particularly in the simulation of predictive models to facilitate the decision-making process, the use of Big Data is very popular. Big Data is also particularly interesting for banks in risk calculation. In the investment business, it can be used to react more quickly to falling prices or influential market developments. The analysis of factors influencing the granting of loans can also be significantly optimized using Big Data.
In the insurance sector, data on both machines and people can be used to adapt insurance services on the one hand and to highlight potential risks for insurance companies on the other. For example, data on the health status of patients can be collected.
Of course, especially when collecting data on individuals, attention must be paid to data protection regulations.
In summary, it can be said that the use of Big Data opens a wide range of benefits that are not yet fully exploited by many companies.
Problems with the use of Big Data
Many companies still complain about the quality of the newly acquired data. Big Data software continues to exhibit problems in the technical implementation of managing and analyzing data. Especially when linking unstructured external data and internal company data, problems often arise. Data protection issues also pose a challenge for the use of Big Data. It is not for nothing that there is now talk of “transparent customers” when the newly developed data is analyzed. The pace of innovation should be increased when adapting systems to the needs of companies.
More detailed information on the topic of Big Data Analytics and the latest tools for implementation in companies can be provide by our team. Contact Enkronos today.