A Brief Intro to Data Analytics

Analytics is the systematic statistical analysis of quantitative data or information. It is chiefly used for the interpretation, discovery, and measurement of meaningful patterns in physical data. It also involves applying statistical methods towards efficient decision-making by providing insights into the relationships between variables. The process of analytics incorporates several areas such as research, business, the environment, political science etc.

2kJ6t4a A Brief Intro to Data Analytics

Analytics can be used for different purposes. The data scientist who works on predictive analytics uses these methods to provide information on what is new in the market, to help in decisions about new products and services, or to ensure that there is a need for changes in a process. Descriptive analytics is used to derive actionable intelligence from large amounts of unstructured data to support tactical and strategic decisions. Analytics can also help managers achieve goals by discovering opportunities in the marketplace or complex processes.

Both types of analytics make use of mathematical techniques to extract patterns from the complex interactions of people, data, and systems. Aspects like machine learning and artificial intelligence play a vital role in both prescriptive analytics and predictive analytics. This makes both methods essential for businesses that are looking for ways to make their business more profitable. But whereas, in the case of prescriptive analytics the primary objective is to generate and provide insights, in case of predictive analytics the primary objective is to ensure that business activities are well coordinated so that a company achieves its objectives.

Analytics has been considered important from the earliest times. History shows that the ancients made use of such methods as hunting, gathering, monitoring animals, tracking vegetation, maintaining records of weather and winds, and keeping track of crops. Over a period of time, different techniques were developed for analyzing natural phenomena. These include astronomy (the study of celestial bodies), astrology (discovering the effects of celestial bodies on human affairs), geography (routing routes according to fixed standards), linguistics (language analysis and categorization), ontology (obtaining information about costs and assets), physics (discovering the internal working of physical systems), psychology (understanding human behavior), and sociology (making statistical studies on large-scale population) all of which became crucial for understanding organizations, their activities, and the interactions among people within an organization. By the end of the eighteenth century, all over Europe and the world a wide variety of applied mathematics were being applied in organizations; this includes finance, management, accounting, and natural sciences.

In the early twentieth century, James radar (a Scottish surgeon and meteorologist) developed a technique called radar graph printing. This enabled him to compile and analyze maps of barometric pressure, land surface temperatures, wind directions and velocity readings from various weather stations all over Great Britain. This gave rise to the field of business intelligence (or business analytics) that evolved later that decade.

Today, many advanced Analytics models and methods are available to organizations. One such way is Deep Learning. This is the use of large databases containing unsupervised machine learning techniques to automatically extract insights from massive amounts of unstructured data sets. Organizations can thus leverage the insights discovered by the Deep Learning computers. Another popular form of analytics is machine learning. Machine learning uses data mining techniques to find patterns and relationships among pieces of structured data and then applies mathematical algorithms to these patterns to come up with predictions and insights.

Data mining and machine learning are not the only available options; there are also dedicated analytic packages available for specific purposes. Some examples of such packages include strategic management, financial metrics, and customer relationship management. With the increasing availability of analytics software and the Internet, the need for data analysis and Analytics experts in organizations has never been more important. Organizations can outsource the analytical work to organizations specialized in this field. Alternatively, organizations can also develop their own in-house team of Analytics experts.

The increasing demand for descriptive analytics over rich data sets, especially on operational topics, has resulted in many software packages geared to perform this task. These packages are very useful for organizations that aim to obtain timely insights. As organizations continue to reap the benefits of using rich sources of information, they will increasingly rely on descriptive analytics in order to make better decisions and achieve more purposeful results.