Data analytics is a process of examining, preserving, modeling, and transforming raw data with the intention of finding useful information. Data analytics (DA) has been described as a branch of computer science that studies how people collect, use, store, transmit, retrieve, store, summarize, and analyze data. It is increasingly recognized that businesses can greatly benefit from using data analytics, because doing so can help them make better informed decisions and improve their bottom line. In particular, business owners are able to use data analytics to: understand customer preferences; discover customer buying trends; establish competitive positions; and determine whether or not to invest in certain technologies or offerings. As more business organizations and industries to implement data analytics into their business processes, they realize the importance of what is data analytics.
However, what is data analytics is often difficult for many people to define and understand. The difficulty arises because many people readily relate it to statistics and data, but fail to recognize that it is in fact an entirely different concept that statistics. Statistics on what is data analytics are essentially the results that you see when looking at a given set of numbers. The problem arises when people attempt to apply such methods of analysis to raw data. For example, if you were to ask someone about the amount of money that he makes monthly and weekly, and then plot that against the number of cars that he drives each week, you would likely get some form of data, although it would be highly un-transformed and misleading.
On the other hand, when you begin to utilize what is data analytics tools, you no longer are attempting to fit your data to a predetermined number. Instead, you are attempting to fit your data to your personal preferences in terms of what is relevant, what is interesting, what is valuable, etc. By utilizing the data analytics tools, you are no longer subjecting your findings to the usual laws of mathematics that are used in conventional scientific research. Rather, your conclusions will be derived from your own preferences, your own opinions, and your own life experiences.
This is why big data has been embraced by so many industries, including pharmaceuticals. By accessing large datasets with high levels of diversity, they are able to generate actionable insights by analyzing the relationships among variables and their individual consequences. Also, by having the ability to generate large datasets relatively easily, it allows them to leverage on the research of others. However, these conclusions are not simply extrapolations based on previously studied trends; rather, they are made after considering the unknown unknowns.
The concepts of what is data analytics and what is data science have been around for quite some time, but they really began to get traction only in the last decade. The driving force behind the concepts is basically the same; what is needed is enough data to make a distinction between what is not known and what is known. In other words, to draw inferences from the empirical relationships among observed facts. Thus, taking a machine learning approach allows for the developers to exploit a lot of information by leveraging existing datasets.
Data is obviously a non-linear stream, but there are two approaches to how to extract useful insight from it. The first is to pull all of the data together and analyze it linearly. This is what is known as linear Data Analytics or logistic regression. The other is to unstructured data, which is essentially a different way of describing what is known. In this case, what is important to analyze is the relationships among observations over time and space, rather than the singular data points themselves.
These concepts can be applied to almost any domain, including marketing, customer service, web development, healthcare, education, and so on. What is Data Analytics? What is Data Science? and How are they related?
The field of data analytics is actually much broader that these three areas mentioned above. Other categories under it include supervised machine learning (self-learning through supervised and self-policing) and big data visualization. The final category, unstructured data analytics, also falls under the larger umbrella of strategic intelligence. If you’re interested in learning more about these topics, I highly recommend becoming a student of strategic planning, particularly when it comes to educating yourself on the broad areas of intelligence. As with most things, the best training will come from taking action, so make sure you’re doing your research!