Big data is used in business, social media, medicine, transportation organizations, etc. Almost all strategic decisions of the highest level of business are made on the basis of Big Data and Science technologies. They provide users with insight into trends and events that would otherwise be difficult or impossible to detect. Companies in many industries are placing increasing emphasis on collecting, storing, and analyzing this data, and how to use it to generate new opportunities and advancement. In this area, new job roles are constantly emerging, but in order, to big data services he needs to have certain abilities and master the appropriate techniques.
Analytical skills and abilities are some of the most important for working with big data. Without them, you cannot become an expert in this field. Analytical tools are needed to solve problems in business or other areas, and to deeply understand their capabilities, you must have both mathematical knowledge and methods of quantitative data analysis, such as descriptive statistics and inference statistics. This helps Big Data engineers curate a large amount of unstructured data, summarize it, and find patterns for predictive analysis.
Machine learning and artificial intelligence
Expanding digital skills gaps mean that organizations around the world are in an endless race for big data professionals with machine learning and artificial intelligence skills. The list goes on and on:
- neural networks;
- reinforcement learning;
- adversarial learning;
- decision trees;
- logistic regression;
- supervised machine learning.
Big Data methods are used in various industries to make decisions and optimize activities. Many companies use insights from massive datasets that they get with special tools. To better understand the objectives and goals of the analysis, the analyst needs to develop relevant skills and become familiar with the subject area. Before starting to develop a model and drawing conclusions, a data analyst must understand all aspects and business objectives of the organization. The expert should have an understanding of the business flow processes, as well as have knowledge in the field of statistics, presentation, and communication skills.
Technologies and trends in working with Big Data
Initially, the set of approaches and technologies included tools for massively parallel processing of indefinitely structured data, such as NoSQL DBMS, MapReduce algorithms and Hadoop project tools. In the future, other solutions that provide similar capabilities for processing ultra-large data arrays, as well as some hardware, began to be referred to as big data technologies.
Big data in industry
According to McKinsey’s Global Institute, Big data: The next frontier for innovation, competition, and productivity, data has become as important a factor in production as labor and productive assets. By leveraging big data, companies can gain tangible competitive advantages.