How Are Big Data Collected And Managed?
How are big data used today? Today, big data has established itself as a key concept to business decision-making. It provides data analytics, helping managers make decisions about their companies. Big data not only helps reduce the operational costs but also makes businesses more competitive. As the data sets keep growing, they become a reliable source of statistical analysis.
Big data collection has emerged as a major management challenge in all organizations. The big data collection consists of diverse sources of data. The internet, social media networks, mobile devices, and automotive infrastructures are some examples. Most of the data sets are very large and complex. They require special analytical tools for best results. While traditional data analysis and modeling techniques work well for some parts of the data, the problems get deeper as the volume of data increases.
Fortunately, there are some ways to handle unstructured data collected through social media, online surveys, and connected cars. One way is to use an already proven analytical methodology called complex network analysis (CNA). CNA enables one to effectively analyze unstructured and complex data collected from different sources. By doing so, you will be able to provide timely and accurate results to decision makers.
There are several reasons why the automotive industry needs to deal with unstructured data generated these days. One of the reasons is that they have to satisfy legal and regulatory requirements such as filing reports and compliance. Another reason is that they have to provide relevant and actionable data to their customers. And yet another reason is that they have to remain relevant and useful to their selves and to their customers. So, what is the automotive industry waiting for?
It is hard to know how is big data collection works when the data gathered is in its early stages of collection. But, there are certain trends that can give you an idea on how this will work and what kind of challenges the industry will face in collecting big and real-time data. And, those are challenges shared by companies big and small who engage in this endeavor.
The first challenge faced is scalability – how to manage the huge volume of data collected. Data silos are being built to handle big data collection processes. The real-time analytics frameworks used by social networks, online survey panels, and automotive industry subsectors are complex and quite demanding of the system’s scalability. In general, an enterprise data management system (EDM) must be able to rapidly adapt to different workloads by deploying a flexible infrastructure. EDRs typically contain application servers, workstations, storage area Networks (SANs), and a Distributed Management Fabric (DMF).
Another challenge is the ability to extract qualitative and quantitative information from large volumes of unstructured or structured data. Traditional IT approaches to structured data mining seek to exploit the relationships between consumers, merchants, and suppliers. The challenge in turning unstructured data into valuable information is the ability to extract meaningful pieces of information from a large volume of unstructured data. Some of the technologies used to achieve this goal are: text mining, graphs and chart analysis, automated structured data extraction tools, and visual computing. In addition, big data tools have been developed to aid in the extraction, aggregation, and usage of structured data. These technologies include: relational databases, object-oriented database management systems, and social media systems.
Another challenge in how is big data collected and managed is the ability to analyze, collect, and present this data in a meaningful way. Traditional IT approaches to this problem focus on data warehousing, and the ability to manage and analyze this data. However, to get real-time insights, business users need to be able to access the analytic capabilities at any time. Some of the solutions available to provide this functionality, including: web analytics, enterprise social networks, product catalogs, customer relationship management (CRM), and the commercial market intelligence.