Have you ever wondered how FedEx and DHL can deliver on time and correctly? Maybe you did! It is due to the data analytics process.

The companies employ the information to discover the optimal delivery times, shipping routes, and transportation costs. Moreover, the corporation employing GPS and collecting GPS data provides data analytics.

An analysis of data


Data analysis involves gathering, cleansing, modeling, and manipulating data to make business decisions. As a result of data analysis, suitable decisions can be made.


Let me give you an example:


During the 2012 London Olympics, data analytics was used in transportation. The Olympics required over 18 million visits. So the train operators and TFL utilized data from a similar event to estimate the number of attendees.


Using process data analytics, TFL was able to efficiently transport a large number of guests to the event.


So, what’s the point?


Yes! Sure! Business data or information is vital in decision making. They must use machine learning to examine the data and make conclusions. Analysts utilize data analysis tools and techniques to filter data to extract important data and then make clear decisions based on that data.


This is where data analytics is used to make decisions. Since key decisions are dependent on data analysis, the appropriate decision-making process is directly proportional. The following elements will help you comprehend the Data Analytics Process.


Steps of Data Analytics


Follow these procedures to make good decisions and analyze data.


1 Decide on questions


First, you should determine the correct questions before diving into data analysis. Determine the question you seek an answer to. This strategy will aid in data analysis as you decide the study’s scope.


For example, you work for XYZ Learning, a fictional corporation. Users of the company’s training software. It provides outstanding security, but it is losing potential clients. “Which parameters affect the customer experience?” “How can we increase client retention without increasing costs? “


So, choosing the proper question is the first and most crucial stage in data analytics. Thus, data analysts must choose the question and approach carefully to obtain accurate, reliable, and relevant data.


Tools for defining your goals or questions


Tracking business data and KPIs helps define the best questions (Key Performance Indicators). Monthly reports help track business-related issues.


Dasheroo and Databox are premium KPI dashboards; Dashbuilder, Grafana and Freeboard are free. Simple dashboards are suitable for both paid and free dashboards.


  1. Prioritize your measurements


Second, define your measurement priorities. With this, you must decide on data analysis criteria. This stage is further divided into —


Identify the metric


You already know your question, so now you just need to decide what data you need to answer it. Only you may set the measuring criteria and technique.


How much have sales reduced in a year, for example? Now you need sales data like how many sales have been made, consumer age group, etc.


How to measure


The next step in data analytics is deciding how to measure your data. This comes before data analysis. You must first decide on the time frame for analysis (monthly, quarterly, etc.). What is the study’s parameter?


  1. Data gathering


The third step in the data analytics process is data acquisition. You’ve already decided on the question and parameters of analysis, so gathering data should be simple.


So, in this stage, you need to collect and organize data. You can use data analytics tools like excel to collect and organize data.


You can write down what data you need and where you can get it before gathering it. You can also ask the team to collect data if you don’t have it or if it’s not in the database.


Remember this:


The data you collect must be divided into three categories:


First-party: Data obtained directly from clients. It may be in the form of transactional tracking data from its CRM (Customer Relationship Management) system. Focus groups, direct observations, consumer satisfaction surveys, or interviews can help.


Second-party: Data taken from another organization. Taken from the firm or other private market. This can be from an app, website, or social media activity.


It contains unstructured data from the advising company and researchers.


Get a good team because the database is critical to the decision-making process. So train your employees to capture just relevant data using various technologies.


You may also need data from observations, surveys, or interviews. So your team must also work on that. Of course, this must be done prior so that when analysis begins, you have all necessary data and information. This is a serious concern in data analytics.


Collecting data tools


Try Data Management Platforms that can find and aggregate data from multiple sources. Popular enterprise DMPs include SAS, Salesforce DMP, Xplenty, and open-source platforms like D: Swarm and Pimcore.


  1. De-stuff the


This is a crucial step in cleaning raw data and extracting quality details. There are only a few data purification tasks:


Get rid of duplicates and errors.





A excellent data analyst spends 70-80% of their time cleaning data. This helps them gather important data.


Tools for data cleansing


OpenRefine is an open-source data cleaning program. You can also utilize Data Ladder, which is featured in the best data matching tools.


  1. Data Mining


The next step in data analytics is to analyze data within the parameters of your question. Now data analysts must study and analyze data in depth. This is a deep examination. You must analyze all collected data. Only then can you achieve success.


Starting with the acquired data, you can plot it, create a pivot table in Excel, or develop a correlation. A pivot table allows you to filter and arrange data to your liking.


Remember this:


Data analysis falls into one of the following categories.


Descriptive analysis: It identifies past events. For example, a training center studies client course completion rates and values.


Diagnostic data analysis seeks to determine why certain events or trends occurred. A doctor utilizes the patient’s symptoms to diagnose the condition.


Predictive analysis allows users to identify future patterns based on historical data. Predictive analysis is used to forecast business growth.


Prescriptive analysis: It allows you to develop superior future recommendations and strategies. Google’s self-driving cars use prescriptive analysis.


  1. Completion and Results


The final stage is to interpret and disseminate the results. It is usually suggested when analyzing results that they never confirm the hypothesis. It’s impossible, thus the only option is to reject the idea or try to disprove the useful insight. The data analyst method is entirely dependent on assumptions and future trend projections. Interpret the results with the questions in mind.


Closing tools (data visualization tools)


Tableau, Infogram, Google Charts, and Datawrapper are data visualization tools. Use Python libraries like Seaborn, Plotly, and Matplotlib to visualize data.




Data analysis is the collecting, organization, modeling, and interpretation of data for decision making. You can’t miss any part of the data analyst process, not even a substep. This approach will help you develop better and more accurate future forecasts. Our professionals will help you with statistical data analysis.


Questions & Answers


What are data analysis tools?


Various data analysis tools are used to analyze collected or raw data.


Tableau Open.








R coding.








Why do data analysis?


Data analytics employs logical and analytical reasoning to extract essential details. The main goal of data analysis is to find meaning in data so that you can make good judgments.


Top 3 data analyst talents


A data analyst must have the following skills:


Oracle, SQL, and Python are examples.


Mathematical prowess.


Resolving issues.


Ability to plan and meet deadlines.


Modeling, analyzing, and interpreting data


a methodical and logical way


Detail-oriented accuracy