Data Analytics can be a science of analysing raw data to make any kind of conclusions all about the information. There are many techniques and also processes of data analytics that have been automated into mechanical processes and algorithms that work over raw data for human consumption. The data analytics is a term that encompasses many diverse types of data analysis. There are types of information that can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would do a lot in the mass of information. This is an information that can be used to optimise process to increase the overall efficiency of a business. Consider an example, manufacturing in the companies often record the runtime, downtime and work queue for various machines and also analyse the data better plan workloads as the machines operate closer to peak capacity.
Data analytics can do much better more than pointing out the bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players that active in the game. There are content management companies that use many of the same data analytics to keep the clicking, watching and also reorganising content to get another view.
The data analytics steps are
1. The first step is to identify the data requirements or data grouping methods. Data may be separated by age, demographic, income and gender there are data values may be numerical or may be divided by category.
2. The second step in data analytics can be collecting it. It is done through a variety of sources like computers, online sources, cameras, environmental sources.
3. The data must be organised after it is collected that may be analysed. This may take place like spreadsheet or other form of software that can take a statistical data.
4. The data will be cleaned up before analysis. It is scrubbed and checked to make sure that there is no duplication or error and that may be not complete. This step will help to correct any errors before it goes to the data analyst to be analysed.
There are many types of Data analytics:
1. Descriptive analytics- This describes what happened over given period of time, like have a number of views that are gone up?
2. Diagnostic analytics- It will focuses more on why something happened. It involves more diverse data inputs and little bit of hypothesising.
3. Predictive analytics- This moves to what will be likely going to happen in the near term. eg: what happened in the sales the last time we had a hot summer?
4. Prescriptive analytics- It suggests a course of action. We should add the evening shift to the brewery and also in the rent in an additional tank increase the output if the likelihood of a hot summer that will be measured as an average of these five weather models.
The different Data analytics techniques:
A Data analyst may use many several techniques like analytical methods to process and data extract information. Some of most popular methods include.
1. Regression analysis- This entails analysing the relationship between dependent variables to determine how to change in one may affect the change in another.
2. Factor analysis- This entails taking a large data set and shrinking it into smaller data set. The goal of this manoeuvre will be attempt to discover hidden trends that will be otherwise more difficult to see.
Cohort analysis will be the process of breaking a data set into groups of similar data that often into the customer demographic. This will allow data analysts and other users of data analytics to further dive into the numbers relating to the specific subset of the data.
3. Monte carlo simulations- model the probability of different outcomes happening. They are used for risk mitigation and loss prevention. These are the simulations incorporate multiple values and variables and can also have greater forecasting capabilities than other data analytics.
1. What is Data analytics?
2. What are the types of Data analytics?