Big Data Analytics: Extracting the Most Out of the Data That You Collect
Analytics is a general term that refers to the systematic mathematical analysis of quantitative information or statistics. It’s used for the detection, interpretation, and explanation of meaningful patterns in numerical data. It also involves applying statistical data patterns to effective strategic decision making. Analytics fall under two general categories: financial and business intelligence analytics. Financial analytics is more directly useful for making trading decisions and implementing business strategies. Business intelligence analytics on the other hand, is aimed at improving firms’ internal efficiency and knowledge.
The field of analytics has gained widespread popularity over the last couple of years due to its ability to provide quantitative insights and quantitative proof for previously hidden patterns. This proof is provided by models and algorithms. The main goal of an analytics strategy is to find, model, and exploit relationships between variables. This way, managers can gain relevant insight into how their firm can improve performance. Also, by providing quantitative proof, analytics strategies open up avenues for a company to explore strategic options.
Data science is another subfield of analytics that deals with large-scale problems such as optimization and machine learning. Data science deals with algorithms and artificial intelligence, specifically with the applications in databases and optimization. Machine learning deals with the generation of synthetic, reusable, repeatable programs from large amounts of unprocessed data, which can then be used in a variety of tasks (such as speech recognition and computer vision). These tasks can then be trained using artificial intelligence to make them run faster and better.
Another subfield of analytics is Natural Language Processing (NLP), which refers to the use of natural languages to facilitate business. This includes content mining, which uses databases, keyword frequency analysis, and other methods to extract useful information from large collections of texts. NLP typically involves the use of a more sophisticated machine learning algorithm, rather than something as simple as sentiment recognition or text mining. This form of analytics requires a lot more sophisticated computational tools and training, and has thus been traditionally been more expensive than basic machine learning. However, thanks to improvements in the availability of NLP and big data analytics platforms, it is now possible for data scientists to train artificial intelligence agents without requiring high-end computing resources.
Natural Language Processing analytics deals with questions about the human condition. Can we predict the future? How are the most successful people in your business interacting with one another? What is it about our cultural norms that prevent us from communicating better with each other? Can we anticipate what the world will look like in the future, based on current trends? Our ability to anticipate these questions, and the answers that we come up with, gives insight into how human interaction and culture may evolve in the future.
Cultural psychology is an area of research that goes beyond just studying individual’s norms and behaviors. It looks deeper into the extent to which people can be affected by social norms, and the consequences of those norms on individual behaviors. These insights can help organizations make more informed decisions about how to adapt to changes in the marketplace, increase productivity, reduce employee turnover, reduce customer dissatisfaction, and get ahead in the race for market share. The goal of this style of analytics is to provide insights that can be used by organizations to enhance their internal functioning. It goes beyond traditional data analysis techniques such as trend analysis or time series analysis by taking into account the complex relationships among people within a group, and the interdependence of those relationships.
Machine Learning is an advanced form of artificial intelligence software applications that has rapidly increased in popularity over the past few years. Software developers have been able to take full advantage of advances in this field to create highly intelligent systems that can operate on extremely large databases, and which can provide quick and accurate insights into the real-world data that they are exposed to. Deep learning uses an approach to data analytics that differs from traditional techniques in that it relies on mathematical algorithms to achieve its goals. Although it is still early days for machine learning applications to impact businesses in the way that traditional data analytics has, the potential for transforming how business is done is huge.
It is no surprise that companies are increasingly choosing to leverage the power of big data analytics in their own organizations. The data that makes up the information that these applications build up is staggering. If businesses can’t analyze the information that they need, then they risk missing opportunities for improved performance. However, even if big data analytics fails to give organizations insight into the true state of their business, they can still utilize the insights that they do gather to improve their internal processes. By collecting and using the data analytics that are available to them, they can gain a deeper understanding of their clients’ needs, detect patterns that could change the course of their future, and adapt to changes more effectively.