Data analytics is a science that analyzes raw data to determine the information gathered. For any business, data analytics is crucial to understand the company’s performance to become more efficient, make strategically guided decisions, and maximize its earnings. Today, data analytics has become more automated into algorithms and mechanical processes that operate the raw data for human utilization.
Understanding Data Analytics
The term data analytics encompasses a variety of different types of data analysis. Any information can be exposed to numerous data analytic techniques to gather insight and then use that to make improvements. These techniques will show various trends and metrics that could otherwise get lost in the giant mess of data. It will then help to move on to decision intelligence to go further with the information collected.
Process of Data Analytics
The steps of data analytics involve several steps to gather information. The first is determining the requirements or how the data is collected. For example, the data must most likely be separated by age, income, gender, demographic, numerical, or categorical. Secondly, the data must be collected through various means, such as computers, cameras, online sources, environmental sources, and personnel.
After the data is collected, it must be organized to analyze it properly. This may be done through a spreadsheet or specific software the business uses. Once the data has been cleaned up and checked to ensure no errors, duplications, or incompletions, it can be analyzed.
Types of Data Analytics
Once the data is ready to be analyzed, there are different ways this can be done in four different ways. The first is known as descriptive analytics. This type defines what has happened over time, such as the number of views or sales strength throughout the month. The second type of data analytics is diagnostic analytics, which focuses more on the reason behind what happened. It involves more diverse data inputs and some hypotheses, such as asking if the weather affected sales or if the latest marketing strategy impacted sales.
Predictive analytics is the third type used to indicate what will likely happen in the next term based on the data collected. The fourth and final data analytic type is prescriptive analytics, a call to action based on collected data. For example, if the summer season has proven to bring in more business, then it would be a good idea, based on the data, to extend their hours during the warm seasons.
Data analytics builds various quality control systems found in the financial world. Many other sectors have embraced the use of data analytics, such as the hospitality and travel industry as well as the retail sector. Healthcare also mixes high volumes of structured and unstructured data to use data analytics and then make rapid decisions.
Various Methods of Data Analytics
Along with the four types, five data analytics methods are used to process data and pull information. One approach is called regression analysis which involves analyzing the relationship between dependent variables to figure out how a shift in one way affects the change in another. The second method is factor analysis which takes a large amount of data and decreases it into a smaller data set. Ultimately, the goal is to figure out hidden trends that may be more difficult to see.
The third method is cohort analysis. With this method, the data is broken down into groups of similar data, most likely into specific customer demographics. The fourth method, called Monte Carlo simulations, shows various models of the probability of multiple outcomes. Many times businesses will use this method for risk mitigation and loss prevention. The final method is known as time series analysis which follows data over time to solidify a connection between the value of a data point and the circumstance of the data point.
Data analytics is essential because it helps a business optimize its performance overall and, in the end, will reduce costs by determining a more efficient way of doing business.