Data Analytics
Updated: Jan 11
Data analytics is the simple process of collecting raw data and turning it into useful, reliable, information. This procedure helps people understand specific things like differences and patterns in a data set. Big companies and organizations use data analytics to provide useful information instead of pure guesswork. Data analytics is used in a variety of fields such as healthcare, engineering, and architecture. Overall, data analytics is a utilitarian process used to help understand different sets of data in all fields of work.
Data analytics follows four steps
Collect Data
Clean Data
Analyze Data
Interpret Results
Collect Data
The first step of data analytics is to collect data. The data could come from a wide variety of sites, ranging from surveys to sales records.
Clean Data
When you collect data, at first it will always be raw. At this stage, the clean data step comes in handy. In this process, you will do things like removing duplicates or fixing incorrect values to have an accurate set of data.
Analyze Data
After cleaning the data, the next step is to analyze it. During this procedure, you study the data to find patterns and trends. To do this, you can do things like calculating averages or comparing groups.
Interpret Results
The fourth and final step of data analytics is to make decisions based on the results from the data. Using the results from the data, you understand what it means and take action based on the scenario. Results from analyzed data can help you make many decisions like improving services, or planning future actions.
Types of Data Analytics:
Descriptive Analytics
Diagnostics Analytics
Prescriptive Analytics
Predictive Analytics
Descriptive Analytics
Descriptive analytics, the most simple of the four data types, is the process of summarizing the data. It answers questions like “how many?”, “how often?” and other straightforward inquiries. Some basic examples are like checking how many students passed an exam, or finding out how many people belong to a certain age group. Descriptive analytics can give us numerous results such as totals, counts, and percentages.
Diagnostic Analytics Diagnostic analytics is the procedure of looking for reasons of why certain results happen. This type of analytics answers questions like “why did sales go down?”, or “why did website traffic increase?”. Some examples of diagnostic analytics are like finding out why a student scored low on a test, or checking why a product is not selling well. Diagnostic analytics compares different data and looks for patterns or differences in a data set. Common methods used in diagnostic analytics are comparing groups or looking at trends.
Prescriptive Analytics
Prescriptive analytics help us see what actions are needed next. Some queries it gives answers to are “what should we do now?”, and “which option is the best?”. Some easy examples of prescriptive analytics are like suggesting how stock a store should order, or recommending the best treatment plan in healthcare. Prescriptive analytics uses results from past data and considers possible future outcomes. Some techniques used in prescriptive analytics are optimization models and decision rules.
Predictive Analytics
Predictive analytics help us guess what is most likely to happen in the future using past data. It answers inquiries like “what will happen next?”, and “what is likely to occur?”. Some examples of predictive analytics are predicting next month’s sales, or forecasting the number of hospital visits. Predictive analytics looks at past data, finds patterns, and then uses those patterns to make certain decisions. A common tool used for predictive analytics are statistical methods.
Real Life Scenario
Consider this case where you are working at a restaurant, and suddenly, sales start going down rapidly. You need to know what foods customers like the most in order to receive the correct stock. To find this information, we can use simple data analytics to help us.
Step 1-Collect Data
The first step is to collect some data. You can easily do this by tracking the dishes customers order every day in the diner. Using this data, we can go on to the next step of data analytics.
Step 2-Clean Data
The next step is to clean the new, perceived data. To do this, you can fix incorrect data values by checking each order, or you can correct erroneous data by modifying inaccurate entries.
Step 3-Analyze Data
After cleaning the data, we have to analyze it. To do this, we can find statistical averages and create charts to visually see the data.
Step 4-Interpret Results
The final step of this process is to take action based on the results of the data analysis. Using these newfound calculations, we can now find the right actions for the restaurant, and save it from the verge of collapsing.
