Professionals tend to use the terms Data Analysis and Data Analytics interchangeably. That has led some people to believe that these two terms are the same. However, that’s far from true. Data analysis looks at data in one way and data analytics looks at data in the exact opposite way. It’s time to examine and explain these ways in more detail!
What is Data Analysis?
Professionals undertake data analysis when they convert raw numbers (data) into information that’s useful for reports, blogs, academics, and other purposes. The conversion process often consists of cleaning, interpreting, transforming data and fitting it into models useful for making business decisions.
What is Data Analytics?
Data analytics takes the analysis process one step further. When business professionals do data analytics, they examine and interpret cleaned data and draw conclusions that will form the foundation for making strategic business decisions.
How Data Analysis and Data Analytics Differ
Professionals who do data analysis examine historic data to arrive at conclusions that will inform the foundation for and affect current strategic business decisions. Professionals who do data analytics examine future data or projections to arrive at conclusions that will inform the foundation for and affect current strategic business decisions. To master Data Analytics skills from experts do check Data Analytics Classes in Pune
Put another way, data analysis looks at events that should happen in the future and examines how they affect the current business and their decisions. Data analytics does the same when looking at events that happened in the past.
Which Situations Are Data Analytics Necessary In?
Data Analytics is necessary in the following instances:
- Providing better healthcare that helps more people live longer. For example, doctors could use data to predict how babies with congenital heart disease (CHD) would fare after having undergone a vital life saving operation.
- Safeguarding the environment. Climate change is real and is affecting humanity’s survival prospects. Data analysis helps environmentalists predict the amounts of energy, water, and other resources people in a particular community will use in the future. That information helps them ramp up recycling efforts, and work on making energy and water more plentiful. Environmentalists can also take steps to make the use of natural resources much more efficient.
- Help researchers understand which ethnic groups are experiencing the greatest amount of change the fastest. Researchers can use data analysis tools to understand the data in surveys, polls, and public opinion forums to predict future ethnic and demographic behavior and trends. For example, researchers could set up more ESL programs if they see that more Latinos are moving to a particular community.
- Researchers can use data analytics to predict which drugs may have potentially dangerous side effects and therefore should not be introduced on the market. That’s often done when major pharmaceutical drugs are still in the clinical trial phase.
- Manufacturers of digital devices use data analytics when examining text. They can program apps to anticipate what the user will type in advance and make appropriate changes accordingly.
Which Situations Are Data Analysis Necessary in?
Data Analysis is invaluable for many businesses, especially manufacturing units. For example, it can help plant managers understand which machines will be the busiest based on past runtime, workload, and downtime data. They can then predict when the most productive times of the day will be. They can also plan for bottlenecks.
Data analytics also analyzes past data to identify who the most productive employees are. Managers can improve worker productivity by rewarding these workers appropriately. Also, many managers analyze past data to understand which departments are the most productive. Then, they can formulate strategies to maintain the higher productivity levels.
Managers can also analyze past consumer buying and purchases behavior to understand which products and services are the most popular. From there, the managers can examine the traits that make these brands highly sought after. They can also analyze that data to understand what to do to increase the sales of some brands that may not have high purchase rates.
Data analysis also helps managers improve a company’s key services. A good example is its customer service. Another good example is in sales.
Final Words:
Managers need to use data analytics to help them understand company processes and consumer behavior better. The information can help position their companies strategically in their industries by making products better and company processes more efficient. Researchers can use data analytics to understand where the needs for certain services are in a community. It’s possible for them to serve disadvantaged sections better once they understand that.
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