Understanding What is Data Analytics?

A Definitive Guide for Beginners

Are you struggling to learn what is data analytics? In this guide, we discuss some of the most important concepts that all beginners should know about data analytics. These concepts include subjects such as the four different types of data analytics, the difference between qualitative and quantitative data, and even the data analysis process. But before getting to that, we first need to learn what is data analytics? Find out more below.


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What is Data Analytics?

One of the most important aspects in understanding what data analytics are, is to find the meaning behind the concept. Data analytics deals with the use of different processors and modern technology in order to find valuable information from the datasets. Data analytics deals with taking a closer look at the raw data sets to uncover recurring patterns. These patterns are then used to gather useful information


Data analytics techniques are seen as different methods that are used to describe the data using illustrations such as tables, graphs, and charts. These techniques make use of historical data alongside different systems and machine learning algorithms.


What Can Data Analytics Be Used For?

Data analytics can be used to gather information in all types of industries. In order to successfully use data analytics in a business, it requires the use of historical data. The data is usually generated from customers, other businesses, or any other persons who frequently visit the company. The data that is collected from a general customer is known as first-party data. The data that is generated from other organizations is called second-party data. And lastly, third party data is seen as data that is bought from a source outside of the company.

The information that is extracted from the historical data is then used as per the business’s requirements. The information extracted can assist a business to better understand their clients and even build on their advertising campaigns.

One of the greatest advantages of using data analysis in a business would have to be the fact that it can help the company to get a better understanding of their client base. Not only can this information be used to understand the client, but it can also provide useful information that can assist in the development of new products that will boost the company’s overall performance.


Who Makes Use of Data Analytics?

The information gathered from the research process is then used in a business. This research is usually carried out by a data scientist or analyst. A data scientist is a person who deals with the task of gathering and analysing large quantities of data sets. These experts use their skills along with modern technology and computer science to find trends and manage data.

A data analyst is a person who gathers and receives data, then organizes it to reach a meaningful conclusion. All companies in different types of industries can benefit from using a data analysis. The type of information that a data analyst can provide an organization with is one of the most valuable tools that the company can use to understand the needs of the consumer.

Some of the tasks that a data analyst deals with include the likes of developing systems for collecting data and then filing their findings in a detailed report. A data analyst can also be a vital part of the data analysis process. The role of the data analysis would be to include all of the information based on the data into the system.


The Four Types of Data Analytics

When it comes to learning analytics, it does not only consist of collecting data, but it also deals with finding some sort of meaning within the data to improve future learning experiences. To achieve this, data analytics uses four different methods.

We discuss these four method groups in detail below:


Descriptive analytics

We use descriptive analytics to examine data to find answers to the question, “what has happened?” This type of examination is usually performed manually and includes the use of visualizations such as pie charts, tables, and line graphs.


This type of method is commonly used to search through and summarise data with the intention of finding any type of pattern or meaning from the data.


There are two types of techniques used when working with the descriptive data analytics method, this includes:


Data Aggregation

The first step requires us to use the data aggregation technique to find, gather, and sort the data into groups or sets that are more understandable and manageable. Once this has been achieved, we can move on to using the data mining technique.’

Data Mining

The data mining technique deals with taking out time to search through the data in order to identify any patterns or meaning from it. Once a pattern or meaning has been identified it will be examined with more detail within the learning environment.


Descriptive analytics is more commonly used to interpret historical data in a way that will draw conclusions that are both useful and meaningful. This type of method is used across various industries. A typical example of using descriptive analytics in the field of business would include using descriptive analytics to provide an opportunity to give more detailed insight as to what has happened in a business and suggest actions to that in order to gain a positive outcome.


Diagnostic analytics

Diagnostic data analytics is used to help people find the root cause of a problem. This method is more advanced since it is used to find answers to the question, “why did it happen?” While using the diagnostics analytics method, it requires people to dig deeper into the data to each for facts that will help to unveil the core reason behind certain results.


Some of the most popular techniques used during diagnostic analytics include:


Data Discovery

During the discovery process, an analysis would try to find all of the possible sources that will help them to the reaming of their results. When using this technique, the analyst would try to better understand the data sets that were already discovered by taking the time to evaluate the ability of the data preparation.


Data discovery can be further viewed as two separate concepts which consist of manual data discovery and smart data discovery. Manual data discovery involves physically mapping out the data in order to help understand and comprehend the data sets. Smart data discovery, on the other hand, deals with the use of technology and other automated processers to represent the data more insightfully.


Drill Down

The drilling down technique is one of the best ways in which analysis can use data to focus on a certain aspect of the data.


By using the diagnostics analytics method, an analysis will be able to get the most value out of their data sets. It is important to keep in mind to look at multiple data sets in order to single out a pattern and find a correlation.


Predictive analytics

When using the predictive data analytics method, an analysis tries to find out what action would possibly happen next, based on the historical data. Predictive analytics is used by an analysis when they would like to branch off and make predictions about any unknown attributes of the data.


 The following techniques are used during the predictive analytics method:


Machine Learning

The machine learning technique is used to create algorithms that assist with predictions to recognize changing patterns. Machine learning makes it possible to take the most appropriate action.


When using the machine learning technique, systems are enhanced through data experience. This system can be adapted each time a new set of data is added, without having being reprogrammed. Machine learning is data-driven. This means that a large amount of data sets needs to be created before a particular algorithm can be used.


An example of using machine learning in a business would be to compare the level of employee satisfaction against their salaries as opposed to predicting the correct salary amount for each employee. Predictions would result in high amounts whereas using the machine learning technique will a more accurate actionable approach.


Predictive Modelling

The predictive modelling technique is a process that is used to create, test, and prove the validity of a predicted outcome. The predictive modelling technique uses different processers such as machine learning and artificial intelligence to complete the task at hand.

This technique is used by an analysis when there is a need to devise, test, and validate a prediction using data.


Various types of models can be used to find out in which set does the data belong. These models help the analyst to find out new information about the data and their prediction. Each of these models can be reused and are usually created by using historical data and a trained algorithm.

These techniques are used to make certain predictions that are likely to occur. The patterns that are identified in the historical data can be used to predict any potential risks and opportunities in the future.


The predictive analytics method can be used in a business to interpret data for the company’s own personal benefit.


Prescriptive analytics

Prescriptive analytics requires an analysis who has previous knowledge of the prescriptive methods to use both historical data from the descriptive and predictive methods as well as past information to find out what should be the next possible step that a business should take in order to solve a problem.


The prescriptive analytics method uses raw data such as information about a specific scenario or past business performance in order to make better decisions.

This method makes use of artificial intelligence and the machine learning technique in order to understand the data.


Prescriptive analytics can help to meet goals in a business or limit any potential risks in a company too. The decisions made using this method is based on facts rather than predictions or instinct.


How Does Data Analytics Help Business?

Data is something that becomes assessable now more than it has ever been before. By having access to this type of information can help businesses make changes that will improve the company.


Data analytics can be used to help businesses in certain industries learn more about the customers who buy from the business. Data analytics can also be used to find certain holes in the market on which the company can capitalize on with their products and marketing campaigns.

In order to successfully apply data analytics into a company, the business needs to first come up with a plan. By doing this, it will provide data scientists and analysts with the correct type of information o develop plans that will assist in the growth of a business.


The Difference Between Qualitative and Quantitative Data

Data analytics deals with both quantitative and qualitative data. These two types of data are very different but they comprise of all of the data that will ever be analysed. Below we discuss the main differences between qualitative and quantitative data.


Quantitative data refers to the value of data in terms of numbers. Each of the data sets used in quantitative data has a certain numerical quantity that is associated with it. This type of data can be used for mathematical calculations as well as a statistical analysis. Some of the questions that quantitative data is used to answer include “How many? Or “How much?”

Some of the most common types of qualitative data that is usually dealt with includes, but are not limited to:

  • Sensory calculation
  • Projection of data
  • Measurement of physical objects


Qualitative data is the type of data that can be organized into groups and recorded down. Qualitative data is gathered by using different types of methods of observation such as one-on-one interviews. The data gathered is commonly identified as categorical data and can be arranged according to a certain criterion.


Qualitative data is very important if that data analysis intends on finding certain characteristics within large datasets.


Some of the most commonly known methods of gathering qualitative data include, but are not limited to:

  • One-on-one interviews
  • Focus groups
  • Observations
  • Case studies


One of the main approaches to a qualitative data analysis includes the deductive approach. This approach deals with examining and interpreting qualitative data based on the idea that the analyst has in terms of the response that he/she will receive from the sample population.


When it comes to distinguishing between qualitative and quantitative data, it is quite simple to understand the difference between the two. All you have to keep in mind is that qualitative data does not include numbers, whereas quantitative data is all about numbers! This will ensure that you can easily identify between the two types of data.


Once you have covered the basics of qualitative and quantitative data, you will be able to understand the difference between structured and unstructured data.

Structured vs Unstructured Data

Data is one of the most important aspects of all business decisions. The way in which data is gathered will play a huge role in determining the type of method that will be used to extract information from the data.


The amount of data that is gathered can be grouped into two very distinct categories namely, structured and unstructured data.


Structured data is also seen a quantitative data. This type of data is usually fixed and organized into a table format for easy visualization. Structured data usually consists of numbers and texts that are either human or machine gathered within an RDBMS structure.

Some of the most common examples of structured data include:

  • Names and dates
  • Home and e-mail addresses
  • Identification numbers

Unstructured data is seen as everything else. It can either be text, no-text, human or even machine gathered. This type of data cannot be collected and processed using standard methods. Even though unstructured data does have an internal structure, the data can only be stored in its original form.


In comparison to structured data, unstructured data makes up to eighty percent of all the data captured.

Some of the most common examples of unstructured data include:

  • Social media
  • Audio files
  • Surveillance imagery


Understanding the Data Analysis Process

The data analysis process comprises of five simple steps that help to simplify decisions and improve data analysis skills.


Step one: Defining the need for an analysis

One of the first steps that need to be taken during the data analysis process deals with finding out why are you required to analyse your business. This could range from a thousand different possibilities, some of which can include questions such as,” how to reduce production cost without having to cut down on quality?” Not only is it important to find the real purpose behind conducting the data analysis process, but it is also important to find possible sources in which data can be collected.


This initial step is one of the most time-consuming tasks during the data analysis process, but if done correctly it can result in preparing you for the following four steps.


Step two: The process of collecting data

After you have identified what is the main reason behind conducting the data analysis process, the next logical step would be to start gathering your data that will be used in the analysis. This step is really important because it will determine how thorough your analysis is based on your data sources.


As a business, one of their main sources for data collection would be internal sources rather than external sources. The Internal source is commonly seen as structured data that is usually gathered from CMR software and marketing automated tools. These sources provide the analyst with information such as potential gaps in the business sales.


Once the internal sources have been used to gather enough data, the data analysis would start looking at external sources. This is where you would typically find both structured and unstructured types of data.


Step three: Clearing out and sorting your data

Once all of your data has been collected, it is then time to sort through all of the data and eliminate all of the unnecessary data that has been collected.

Not all of the data gathered is necessarily good data. This means that the data analysis would have to get rid of any unwanted data to provide the most accurate results. This such as duplicate data could potentially skew the results of the data analysis process.


Step four: Setting time aside to analyse the data

When it comes to analysing data, there are so many different options that a data scientist could choose to use. These include the use of data techniques or software that can create visualizations that can be understood by others.


Step five: Explain your findings


The last and final step that needs to be completed in the data analysis process deals with interpreting your results. This is how the business will gain potential value from the other steps that have been completed during the data analysis process.


This step should be completed to prove your reasoning behind conducting the data analysis process in the first place. Both the data analysis and the business should consider teaming up at this stage in order to interpret the results.


As a business, it is important to incorporate the data analysis process within your company to ensure the possibility of finding out useful information that could help the growth of the company.


As a beginner in the world of data analytic, it can become a bit confusing trying to wrap your head around all of the new concepts and terms used in this field. Some of the key concepts which we now know is that data can be quantitative or qualitative, structure, and even unstructured. All businesses can also use the five-step data analytics process in order to enhance the data analytics skills in a business. We hope that this guide helps you learn what is data analytics and how it can be successfully incorporated into a business.