data analytics process

Complete Guide on How Data Analytics Process Works?

Have you ever imagined how FedEx and DHL can deliver things on time and at the correct addresses? You might have thought that! Well, it is just because of the data analytics process. 

The companies use the analysis process and find the best delivery time, shipping routes, and the most cost-efficient transportations. Moreover, the company using GPS and collecting information from GPS provide the benefit of data analytics.

What is data analysis?

Data analysis includes the process of collecting, cleaning, modeling, and transforming data to identify useful data for business decision-making. Data analysis aims to extract meaningful and useful details from the raw data and make the relevant decisions depend on the data analysis.

Let’s take a real-life example of it: 

In the London Olympics (a few years ago), the data analytics technique was employed in transportation. For the Olympics, almost 18 million visits had to be made. That is why the train operators and TFL used the data collected from a similar event to assume the number of people traveling to the city for the event. 
With the help of the process data analytics, the TFL was able to transport the numbers of passengers to the event on a particular period of time and kept the transportation smooth.

Is data analysis really useful?

Yes! Why not! Business data or information is very important to make decisions. They have to analyze that data with machine learning and then make decisions thereon.  As they have a lot of data and information, analysts use data analysis tools and processes to filter such data to extract important data and then make a clear decision with respect to such data.  

This is where the process data analytics is used to make decisions and to draw conclusions. Since crucial decision-making is based on data analysis, the right decision-making is directly proportionate to the right data analysis process. The below points will help you to understand the Data Analytics Process step-wise easily. 

Data Analytics Process – Step by Step

Table of Contents

Follow the following steps to make the right decisions and for proper data analysis.

1 Determine your questions

Instead of going directly to data analysis, first, you should decide the right questions. Decide the question of which answer you are looking for. This method will help you be specific in data analysis as you define the study’s scope by deciding the questions. 

For example, you are working with a fictional company known as XYZ Learning. The company has customized training software for its users. It offers excellent security, but still, it is losing its customers.So, now the questions must be like, “Why are we losing our potential clients?” Better: “Which parameters are impacting the clients’ experience?” Excellent: “How can we improve client retention rate without maximizing the costs?

So, this is the first and most important step of the data analytics process, as unless you decide on the right question, you won’t get the right result. Thus, data analysts have to decide the question and method accordingly so that they can get accurate, reliable, and relevant data for analysis. 

Useful tools for determining your objective or questions

To define the most useful questions, you need to track the business metrics and KPI (Key Performance Indicators). Monthly reports enable tracking the relevant issues related to the business. 
There are some paid KPI dashboards, like Dasheroo and Databox, and free: Dashbuilder, Grafana, and Freeboard. Both paid and free dashboards are great for producing simple dashboards.

2. Decide your measurement priorities

Secondly, you have to set clear measurement priorities. With this, you will have to decide the criteria for data analysis. This step is further bifurcated into the following – 

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Determine what to measure 

Since you have already decided on your question, you just need to decide what data is required to answer such a question. You should be well versed with the question then; only you can set the measuring criteria and method. 

For example, your question is how much your sales have decreased in a year. Now you will require sales data such as how many sales have been made to date, the age group of customers, etc. 

Determine How to measure 

Now the next step in the Data analytics process is to decide how you will measure your data. This step is done before the data analysis. For this, you have to decide what is the time frame for analysis, such as monthly or quarterly, etc. What is the parameter of the study?

3. Collection of data 

The third step in the data analytics process is the collection of data as per the above decisions. You have already decided on the question and parameters of analysis, so it would be easy for you to decide what data is required for collection. 

So in this step, you need to do two things to collect the relevant data and organize the data. For the collection and organization of data, you have several data analytics tools such as excel, etc., for the same.

Before collecting data, you can note down what information you need and where you can get the data from. And whether you have such data or not, and if it is not available in the database, you can ask the team to collect such data.

The key point to remember:
The data you will going to collect must be categories into three different categories:
First-party: It is the data that has been directly collected from the clients. It can be in a transactional tracking data form that might have taken from its CRM (Customer Relationship Management) system. The sources of this can be focus groups, direct observations, customer satisfaction surveys, or interviews.
Second-party: It is the first-party data that has been taken from the other organization. It can be taken from the company or any other private marketplace. The variety of sources of this can be any app, website, or social media activities.
Third-party: It contains a vast amount of unstructured data taken from the advisory firm and researchers. 

Get an efficient team for this step because the whole decision-making process is dependent on the database. So you have to train your team in a way that they collect relevant data only and can use different tools for the same.

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It might also be possible that you need data obtained through observations, surveys, or interviews. So you need your team to work on that too. And of course, this thing needs to be done beforehand so that when you start analysis, you have all the required data and information. This is one of the major concerns of the data analytics process. 

Useful tools for collecting the data

Try Data Management Platforms that enable you to identify and aggregate information from various sources. Some of the popular enterprise DMPs are SAS, Salesforce DMP; integrated platform, Xplenty, and open-source platforms, such as D: Swarm and Pimcore.

4. Clean the data

This is one of the essential steps used to clean the raw data and get the quality details from it. There are few data cleansing tasks, and these are:

  • Eliminate all errors, outliers, and duplicates.
  • Remove all the irrelevant and inappropriate data points.
  • Bring the structure to the collection and insights the data.
  • Always try your best to fill the major gaps in your content.

It has been seen that a great data analyst spends quality time around 70-80% of the time to clean the data. This helps them to get meaningful and useful data.

Useful tools for cleaning the data

OpenRefine is an open-source data analysis tool that helps the user to clean the data. Moreover, you can use Data Ladder that is included in the highest-rates data matching tools. 

5. Data Analysis

The next step in the data analytics process is to analyze the data in your decided question’s parameters. Now data Analysts have to do detailed study and analysis of data. Such analysis is deep in nature. You have to analyze each and every factor of the collected data. Then only you can get the right results.

You will start this step by manipulating the collected data in different ways, such as plotting the data or making a pivot table in excel or through establishing a correlation in the data. With the help of a pivot table, you can filter and arrange the data as per your needs. 

The key point to remember:
The types of data analysis can be fit into one of the below-mentioned categories.
Descriptive analysis: It is used to identify the things that have already happened. For example, a learning institute analyses the course completion rate and values for its clients.
Diagnostic analysis: This type of data analysis focuses to understand why particular things or trends have happened. An example of Diagnostic analysis is: a doctor uses the symptoms of the patient to diagnose the disease.
Predictive analysis: Predictive analysis enables the user to identify the future trends that depend on historical data. Business forecast future growth is an example of Predictive analysis.
Prescriptive analysis: It enables you to make an excellent recommendation and technique for the upcoming future. Google’s self-driving cars are a great example of prescriptive analysis.

6. Conclude and share the Results

The last step in the data analyst process is to interpret and share the results. It is always suggested while interpreting results that never prove the hypothesis that it is absolutely true. It is not possible, so the only thing you can do is reject the hypothesis or try to prove the valuable insight or insights false. The data analyst process is completely based on hypotheses, assumptions, and predictions of future trends. Keep the questions in mind while interpreting the results.  

Useful tools to conclude (data visualization tools)

The data visualization tools can include Tableau, Infogram, Google Charts, and Datawrapper. If you know Python libraries, then use Seaborn, Plotly, and Matplotlib as data visualization tools.

Conclusion

Data analysis is a business process of collection, organization, modeling, and interpretation of data in order to analyze the data for decision making. Each step of the data analyst process is an important step in itself, so you can’t skip even a substep. This process will help you to make future predictions, and thus you can make better and more accurate decisions. Get the best statistical data analysis assignment from our experts.

Frequently Asked Questions

What are the tools of data analysis?

There are various data analysis tools that are used for performing analysis for the collected or raw data.
Tableau Public.
Apache Spark.
RapidMiner.
QlikView.
R Programming.
SAS.
Excel. 
KNIME.

What is the purpose of data analysis?

The data analytics process uses logical and analytical reasoning to get relevant details from the data. Data analysis’s foremost objective is to discover significance in data to derive the knowledge used to make useful decisions.

What are top 3 skills for data analyst?

Some of the major skills that a data analyst must have:

Programming languages, like Oracle, SQL, and Python.
A high level of mathematical ability.
Problem-solving skills.
The ability to meet deadlines and can plan work.
The ability to model, analyze, and interpret data.
A systematic and logical approach.
Accuracy and concentration to detail.

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