Data analytics: What is it? A Basic Dissection

Data analytics: What is it? A Basic Dissection

These days, data is present everywhere. Companies desire to make decisions based on facts. Additionally, almost every industry wants to make use of the new information gathering capabilities at our disposal in order to increase productivity, profitability, and quality of results.

However, without data analytics, that amount of raw data and information is essentially meaningless. It would be like asking someone to take a dose of cough syrup out of an ocean.

You can navigate a sea of information, spot trends, extract key insights, and—most importantly—learn through data analysis. Have you heard of data analytics before? If you are already familiar with data analytics, are you interested in finding out more about how to work as one? 

Now, let us define data analytics.

The process of gathering, arranging, and figuring out how to use massive volumes of data is known as data analytics. Saying it is easy. However, data analytics is not so much a profession as it is an industry of occupations due to its immense versatility and range of applications.

It is really difficult to even gain a cursory understanding of data analytics because there is so much going on in the sector. But if you do get started, you will discover how fascinating the field can be.

Senior data analyst at BI Worldwide Nathan Keysser chuckles, saying, "Data is lit AF." "The capacity to transform vast volumes of meaningless data into something meaningful, intriguing, and helpful—it truly satisfies that problem-solving itch."

According to Keysser, this is a particularly fantastic field for puzzle enthusiasts. If so, continue reading. Watch how it functions.

What is the appearance of data analysis?

When you start from scratch, data analytics is a fairly simple procedure to learn. If you would like to get more specific, you can divide each of these processes into further phases. But this is what you need to know if you want to have a general understanding of data analysis.

Step 1: Gathering data

Data collection is the first step in the data analysis process because it is necessary for data analysis to occur. Businesses, data scientists, and analysts must figure out how to find pertinent sources of information.

This can resemble your preferred shoe retailer sending out an automatic survey and gathering the responses following each transaction. Alternatively, it might be web cookies that log who visits a website, what they click on, how long they stay, and other details. The more automated branch of data science known as data mining focuses on gathering large volumes of unprocessed data in preparation for insightful analysis.

Law firms may examine past data on case studies, hospitals may use data from their electronic health records, and environmental organizations may gather information about hundreds of places' soil, wind, and sun conditions. Essentially, before you can utilize information, you must first find a means to gather it.

Step 2: Processing data

Once you have a large amount of data, you must arrange it into a format that you can manipulate or filter through with ease. This could include scanning paper documents, transferring data from multiple files into a single spreadsheet, or a variety of other techniques.

This portion of the raw data analysis process typically takes a long time since it involves a lot of human labor and careful consideration of what information may be valuable. If not, you will have to repeat the entire procedure.

It used to be a common question to wonder if investing in pricey data analytics solutions was worthwhile. However, as more people become aware of data's value, this is frequently a higher priority issue. Technology that can help with data management and automate this laborious process may soon pay for itself for many businesses.

Step 3: Analyzing the data

We have finally arrived at the game's name! Once the data is safely stored, easily accessible, and arranged, you can begin the analysis process. The needs of the moment, your business, and even your preferred working style will all have a significant impact on how you evaluate.

Keysser states, "In general, I know what I want to find." "However, I usually do not know how the data is organized, so I have to solve quite a few puzzles to determine exactly how I want to pull the data." Keysser continues, "Many people are unaware of how creative the work can be in data analytics because the field sounds so technical."

There are numerous ways that data scientists and analysts can approach analysis. One option would be to look for information on a certain query. For example: Will Illinois consumers spend $15 on this kind of shirt?

Alternatively, you may focus on a specific area and get as much information as you can about it. For example: What information do we have about customers who have bought shirts from this website?

Here, the possibilities in data analytics are only limited by your creativity. However, there are specific categories of data analytics that can inspire you.

Analyses that are descriptive

Analyses of this kind might be rather straightforward. Providing answers to the what, when, and how questions in a given data collection is the main goal of descriptive analytics. Descriptive analytics, for instance, can involve doing a straightforward statistical study to determine the median value within a big collection of data.

Alternatively, and maybe more precisely, a telehealth service could monitor the timing of strep throat consultations scheduled by their clients with little children. They could identify the months of the year with the highest number of consultations over the previous ten years and pinpoint the peak periods for pediatric strep throat consultations with the use of descriptive analysis.

When performing data analysis, descriptive analytics is an excellent place to start since it may help you spot commonalities you might not have seen at first and identify mistakes in your data, such as typos.

analytical diagnostics

A little farther, diagnostic analytics explores the "why" questions we might ask of the data. These can be more social/emotional (such as why are employees leaving our organization) or technical (such as why does our website constantly crashing).

For instance, a business may be perplexed as to why staff turnover is increasing. After gathering data from exit interviews, they could apply diagnostic analytics to identify the issues that workers bring up most frequently and figure out how to assign a relative importance to each one.

Analytics that predict

As the name suggests, predictive analytics makes predictions. Predictive analytics can assist you in determining when tornadoes may recur in the future if, for example, you examine data on every tornado to have ever struck the United States.

Predictive analysis is a great way to get insightful data because it is very profitable for almost everyone to have some idea of future trends.

analytics that prescribe

The main idea behind prescriptive analytics is to use data to determine the best course of action. It uses predictive analytics to generate a suggested course of action. When a large number of predictive variables are present, this can be highly beneficial. In the tornado scenario, towns might make a prescriptive decision about when to activate their tornado warning sirens based on a mix of data on when tornadoes are most likely to occur and what weather conditions are likely to develop first.

In order to handle all the raw data, this kind of data analytics frequently uses automation, machine learning, or algorithms. Consider how a credit card corporation might keep track of transactions; it is far too much information for data analysts to sort through. As a result, the business may develop a prescriptive analytics system to monitor expenditure trends, identify irregularities, and suggest a fraud or theft alert.

Then, in order to minimize potential loss in the event that the card is stolen, a consumer who purchases anything extremely unusual will immediately get a fraud alert and have their card placed on hold.

Step 4: Interpreting the data

Making sense of the data is the ultimate goal of all of this, as evidenced by the various forms of data analytics.

Following data analysis, those revelations are now at hand. It still requires some effort to translate and explain, though. Here's where data modeling and visualization are useful! Even with the best data analysis in the world, your efforts will be in vain if your research's conclusions are not understood by the general public.

Making the data analytics process (as well as the findings and suggestions) understandable to non-experts is one of the most crucial data analytics strategies.

How are data analytics used by organizations?

I am sure you already see the picture. An organization could utilize data analytics to answer queries regarding.

• What they are doing (i.e., who our clients or customers are, how much we create, and where we have the most success)

• Why something is taking place? (why does this work, Why does not work?)

• What could possibly occur in the future (when are we most likely to be busy, and how frequently is something likely to happen)?

• What ought to occur next? (When ___ occurs, how should we respond and what is the best course of action?)

The potential in all of that is difficult to exaggerate.

It extends well beyond increasing revenue as well. Healthcare, education, government, economics, politics, and all the other "this is too vast to wrap my head around" aspects of society can all benefit from data analytics.

According to Keysser, "knowledge is power." "Although it may sound corny, the more truth we can uncover in the data, the better decisions we can make for the future."

Those who figure it out

One kind of professional in the data analytics sector is a data analyst. If you enjoy finding solutions, you may be interested in finding out more about what it is like to be a data analyst in real life. While all of this high-level data is useful for providing a broad picture, the details become far more fascinating when you focus on the specific work at hand.