In IT terminology, big data is defined as a set of data sets that are so complex and large that they cannot be easily captured, stored, searched, shared, analyzed, or visualized using available tools. In the global market, such “big data” usually appears in the process of trying to identify business trends from available data sets. Other areas where big data continues to emerge include various research areas, including the human genome and the environment. The limitations created by big data have greatly affected business informatics, financial markets, and Internet search results. The processing of “big data” requires specialized software that must be able to coordinate parallel processing on thousands of servers simultaneously.
The importance of such a huge data set is especially important for companies in uncertain times. In this case, quickly processing market data to support decision-making may be the difference between survival and death. I recently came across an article about big data and its impact on the Irish industry. Author Jason Ward is a country manager for EMC Ireland, and his views on the company ’s use of big data do n’t just apply to Ireland. The author believes that one of the reasons why Ireland relies on big data is the deepening of the eurozone crisis. However, the impact of the European double-dip recession will affect markets around the world. In this case, companies around the world will naturally focus on using big data to gain a competitive advantage.
Because the processing of these data sets is beyond the scope of most separately operated companies, it is expected that there will be a collaborative framework with companies that operate different parts of the new data analysis process and share the results obtained from processing such data. In a world where smartphone sales exceed personal computers, big data analytics is expected to be the next big company. The US, Europe and various Asian companies have invested heavily in this area. Current data sources for Big Data include but are not limited to retailer buyer information, information posted by individuals on social networking sites, and historical corporate data on production and sales. Since big data is generated through the interaction of multiple factors, advances in analytical techniques for large data sets are expected to lead to the introduction of technologies that can use available computing resources to handle more and more variables.
Recently announced commercial use of big data
Recent examples include targeted marketing of baby products by US retailer Target. Target uses these emerging technologies to identify customers who need baby care products in the near future based on how they are purchased. The data source is the information that Target collected from its customers during its previous visit to its store. Each buyer is assigned an ID number in Target ’s database and tracks their purchases. Target processes and uses this information to predict customer buying patterns and design targeted marketing campaigns.
Other sources of these data sets for use by Business intelligence solutions Include information available on public forums; social networking sites, such as Facebook, LinkedIn, Twitter, and the digital shadows we leave behind when we visit websites. This data is analyzed by Amazon and correlated with other people ’s product analysis, searches, and reviews to generate more accurate product recommendations for visitors to their online shopping sites. In addition to business uses, the ability to collect, classify, and analyze such large amounts of data by helping to identify and analyze drug interactions, personal medical care, and various social and economic factors that affect drug treatment outcomes is also critical to the healthcare industry. treatment. Big data analysis refers to the new world of data science, and Cisco estimates that it will consist of approximately 10 billion Internet-enabled devices.
Road to market growth
Although industry analysts and experts believe that big data analysis is the next revolution in the field of data analysis, how to expand this trend is still a topic of debate. Current recommendations to promote development in this area include:
• Offering specialized courses to impart necessary skills
• Incorporate these analytical techniques as papers in leading applied science courses
• Government-led partnership with industry initiatives to raise public awareness
• Increase research and development funding for improving current big data plans
These are just some of the suggestions that can help this emerging analytical market to develop into the future of all data analysis across multiple industries.