Data analytics is not a familiar word and many people still get confused in understanding it. If this is the case with you too, then you are at the right place. Data analytics is a broader term, analytics basically clears the concepts of analysis. Data analytics is important from a business perspective.
What is data analytics?
Data analytics is the process of analyzing raw data in order to discover useful information. It inspects, cleans, transforms, and models the data to draw a conclusion. Data analytics has multiple approaches, a variety of techniques, and different business domains. It plays an important role in the business world in making scientific decisions and helping businesses work efficaciously. Data analytics is a technique that allows extracting uncover patterns to take out valuable insights from raw data. it includes different techniques with different goals.
Types of Data Analytics
Descriptive analysis is the technique that precise the datasets to explain the outcomes to stakeholders. It is the numerical approach to get insights into the data. Success and failures can be tracked by developing key performance indicators. This process requires the collection of data, data analysis, and processing of data to provide necessary insight into past performance. The main goal of descriptive analytics is to find the reason behind success or failure in the past.
Once you understand the data, diagnostic analysis helps to dig deeper and find the cause. Diagnostics analytics is more basic, they take the findings from the descriptive analysis. They help in investigating how things are going, why the performance is getting better or worse. They identify anomalies in the data and the data related to anomalies are collected. It identifies the casual relationship among data elements. It analyses the data numerically and it is the optical way to analyze the data.
Predictive analysis is a technique that references future causes. They use historical data to know the trends and determine whether they will occur again or not. The main objective is to predict the future unknown outcome and then take action based on the prediction. It includes a variety of machine learning and statistical techniques. Predictive Analytics is famous among business intelligence applications. It aims at if we know the mistakes we are going to make in future, obviously we are going to avoid that. Hence, analyzing historical events predicts the future outcome in a scientific way.
Prescriptive analytics focuses on what should be done. This uses data-driven decisions by using insights from predictive analytics. By machine learning strategies, prescriptive analytics allows businesses to make informed decisions. It uses both internal and external data. It combines various business and data rules. To optimize the customer experience, prescriptive analytics is used by large-scale organizations for arranging the inventory in the supply chain.
Role of data analyst
Data analyst works with data in various ways. Data management, data mining, data presentation, and statistical analysis are the primary steps in the data analytics process. A Data analyst fixes the coding errors and solves other data-related issues by designing and managing the data systems and databases. Also, a data analyst mines the data and reorganizes in such a way that it is easily readable or writable by human or machine. Then, to interpret data tools, it uses the statistical tool. The data analyst serves as a gatekeeper for an organization’s data so stakeholders can understand data and use it to make strategic business decisions.
To sum up, analysis of data supports decision-making. It is important to know the usage of data in a beneficial way. Data analytics helps in understanding the complex problems faced by organizations. Data in itself is wholesome facts and figures. So, the concept of data analytics organizes and presents the data in useful information. The growth rate of data analytics is high and the future is going to be unpredictable.