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Getting to Know Data Analysis and The Best Tips for Doing It

In a world that is now all digital, it is undeniable that data analysis plays an important role in the success of a company. From finding the root of the problem to risk management needs, data analysis is an important aspect that cannot be dated. Because the trend of the data world is growing, it feels like you need to know the sundries of data analytics. Come on, see more in this article which I have summarized for you.

What Is Data Analysis?

Data analytics, or data analytics, is basically the process of collecting, compiling, and pulling useful information from an abundant data set. This analysis process can reveal trends and metrics that could be missing in that data set.

This information can then be used to optimize processes to improve the efficiency of the business or the system as a whole. For example, let’s just say that in a class there are 30 students. Each student will be given a box of 12 crayons by the homeroom teacher, where they have to choose 2 favorite colors. When this rule is done for all students, analysts can record what colors each student chooses.

Analysts could find interesting patterns, such as the possibility that half the class chose red, or students who took green always chose yellow as well. Departing from this information, analysts can provide the fact that the homeroom teacher can save costs by buying 8 boxes, with 12 pieces of red crayons, instead of having to buy 30 boxes of crayons.

In essence, data analytics is the process of taking and processing data, where analysts can obtain useful information and can be used as a reference for facts for problem-solving purposes such as savings or increasing profits.
Types of Data Analysis
data analysis

Actually, there is no specification for this type of data analysis. The methods used for data analytics tend to refer to the types of analysis in general. Each type has a different function and the results can be applied to a variety of corporate purposes. The following is an explanation of the types of analysis in data analytics that you need to know.

1. Descriptive analytics

The first type of analysis in data analytics is descriptive analytics.

This type of analysis helps companies to understand how their business is performing by providing context to help stakeholders interpret information. This information can be explained in the form of data visualizations such as graphs, diagrams, reports, and dashboards.

For example, in health agencies, say that the number of people treated in emergency rooms soared high in such a short period of time. Descriptive analytics can give you an idea that this is indeed happening with all the supporting statistics, such as the date of the event, the volume and details of the patient, and others.

2. Diagnostic analytics

The second is diagnostic analytics. This type of analysis helps the company in understanding why something could have happened in the past.

Why is this analysis necessary? The reason is, diagnostic analysis is often referred to as root cause analysis. This type of analysis encourages analysts to carry out processes such as data discovery, data mining, drill down, and drill through.

For example, in the field of health, diagnostic analysis will explore data and make correlations. This type of analysis can help the hospital to determine all the symptoms that the patient has, such as high fever, dry cough, and fatigue, leading to one type of disease in common. With diagnostic analysis, hospitals now have an explanation for the surge in patient volume that occurred in the ER.

3. Predictive analytics

Predictive analytics takes historical data and incorporates it into machine learning models that will later consider key trends and patterns. The model is then applied to the latest data to predict what will happen in the future. Returning to the health sector example, predictive analysis can predict a surge in patients admitted to the ER in the next few weeks. This information is filtered based on patterns in the data regarding the type of disease that is spreading rapidly.

4. Prescriptive analytics

This type of analysis in data analytics provides recommendations for actions that companies can take after seeing ongoing trends and patterns. Prescriptive analytics suggests a variety of efforts and outlines the potential implications for each problem.

Another example in the health sector is that this analysis could suggest that hospitals increase the number of staff to deal with the ever-increasing number of patients.

Things to Look For When Analyzing Data

In fact, there are many things that need to be considered before you can start doing data analysis. However, in my opinion, the most important aspects to pay attention to are the following:

  • the bigger picture
  • scope and limitations
  • problem structure
  • problem segmentation

After these aspects, then you can continue the process with hypotheses, analysis, comparisons, recommendations, conclusions, and so on.

Beginner Mistakes when Analyzing Data

So, why do I think the four aspects above are so important? Because, starting from personal experience, there are still many errors found related to the four aspects that I mentioned. Sometimes, an analyst fails to see the bigger picture of the problem at hand

This is why it’s important to start each analysis by finding the correct context. By looking at the bigger picture, analysts can find solutions that are more suitable and more usable over a longer period of time. In addition, many analysts are still affected by personal bias.

Issues like this can affect the mindset of analysts, they will determine recommendations without the slightest consideration of the data due to biases that have occurred in the past. This can result in them changing the data or drawing conclusions in the direction they want.

Finally, analysts often perceive that correlation is similar to the law of cause and effect. When analysts recommend actions based on correlation, usually, their recommended results are less than effective.

Therefore, be careful when advocating something. Use hypothesis tests and A/B/n testing to ascertain the cause of a problem.

Why Is Data Analysis Important?

As I have explained, in an already all-digital world, data analysis has an important role in the success of a company. Companies must find ways to stay up to date with the times. With that said, it means that the company needs to do a lot of things at the same time.

The reason is that the company cannot put all its resources on all things. If the company wants to do a lot of things, it needs abundant human resources. If the company does not have abundant human resources, it will cost a lot of money.

Then, if a company does not have abundant human resources and a lot of costs, they must be smarter at work. Well, data analysis is the solution to the problem.

The process of analyzing demands that the company to work smarter in figuring out what is important at a certain period of time. In addition, data analytics also helps companies to find out what has the most impact on their business.
A Message for Aspiring Data Analysts

Before closing the article, there are several messages that I want to convey for those of you who want to be involved in the world of data analytics. My first message to friends was to have a high curiosity and always follow up on all that curiosity.

Curiosity is indeed an important thing, but following up is the real thing more important. Second, be a resourceful person. A good analyst knows where he should look for answers, and is careful in using existing resources. Third, don’t focus too much on your hard skills, but focus on developing your soft skills.

A good analysis consists of the results of observation and good delivery. How you present a hypothesis determines the results of the analysis you do. The last one is don’t forget to have fun. If you don’t enjoy your work, little by little you will hate it, and the results will not be maximized. Therefore, enjoy every process and mistake you make. Guaranteed, you will become a more reliable analyst.

That’s roughly what the data analysis explains that I can tell you.

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