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5 Steps of Data Analysis

  • Mallika Rangaiah
  • May 04, 2021
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A critical point of concern when it comes to research is not just the dearth of data but also scenarios where they might just be too much data at their behest, which becomes the case for many government agencies and businesses. The overwhelmingly high level of information generally leads to lack of clarity and confusion. 


With a massive level of data available for them to arrange, data analysts generally need to focus on determining if the data is helpful to them, drawing precise conclusions through that data and finally using that data to shape their decision making process. 


It's fascinating how the right data analysis process and tools can serve as the powerful weapon that makes an ocean of cluttered information become a piece of cake to sort and comprehend. 


A range of data visualization tools come to use in the data analysis process as per varying levels of experience. These include Infogram, DataBox, Data wrapper, Google Charts, Chartblocks and Tableau.



Steps of Data Analysis


Below are 5 data analysis steps which can be implemented in the data analysis process by the data analyst. 


Step 1 - Determining the objective


The initial step is ofcourse to determine our objective, which can also be termed as a “problem statement”

This step is all about determining a hypothesis and calculating how it can be tested.  Certain questions emerge in mind here, such as determining the business issue that the person is attempting to resolve. This question, the one the whole analysis would be based upon is extremely crucial. If the senior management of the business raises the question regarding the decline of customers. 


For example, if the issue of losing customers is raised, the focus of a data analyst is to comprehend the root of the issue by getting an idea regarding the business and its goals so that the issue can be defined in a proper manner.  


For instance, let’s assume we work at a fictional firm termed Prestos Knowledge and Learning that produces custom training softwares for its customers. Although the firm excels when it comes to gaining fresh clients, yet it fails to secure constant business with them, raising the question of not just why it is facing loss of customers but also about the aspects which adversely affect the customer experience and how we can enhance consumer retention while curtailing the expenses. 


Upon the issue getting defined, it is essential to conclude which data sources can aid in resolving it. For example, you may note that the platform has a smooth sales process but a weak customer experience owing to which customers fail to return to avail its services. So the question of which data sources can play a role in responding to this issue gets focused on here. 


While this step is all about making use of lateral thinking, soft skills and business knowledge, yet that doesn’t mean it doesn’t require tools. To keep track of our key performance indicators (KPIs) and business metrics, tools and softwares need to be put to use. For instance, KPI dashboards like DataBox or open source softwares such as Dashbuilder can be useful for generating easy dashboards, towards the start and end of data analysis processes. 



Step two: Gathering the data


Once the objective has been set up, the analyst needs to work on gathering and arranging the suitable data. This makes defining the required data a prerequisite. This can be either qualitative or quantitative data. Each of the data is primarily arranged into three categories, namely first party, second party and third party data.


1. First-party data


First-party data is basically the data which the user, or their company has directly gathered from its customers. This can either be the data gathered via the customer relationship management system of the company or it can be transactional tracking data. 


Wherever the data is generated it is generally organized and structured. Remaining first data sources can include the subscription data, social data, data gathered from interviews, focus groups, surveys regarding consumer satisfaction etc. This data is useful for predicting future patterns and gaining audience insights.


2. Second-party data


This data is primarily the first-party data gathered from other companies. This might be available directly from the company or through a private marketplace. It can include data from similar sources as first party data like website activity, customer surveys, social media activity etc.  


This data can be used for reaching new audiences and predicting behaviors. It offers the advantage of being generally structured and dependable.


3. Third-party data


This is the data that has been gathered and separated via multiple sources through a third party organisation. This is often largely unstructured and is collected by many companies for generating industry reports and for conducting marketing analytics and research. Examples of this data include, email address, postal address, phone numbers, social media handles, purchase history and website browsing activities of the customers. 


Other examples of this form of data include Open data repositories and government portals. 


Once the analyst has determined the data he needs and how to gather it,  many useful tools are put to work. Speaking of tools, data management platforms (DMP), is one of the first softwares that comes to mind. This is a software that enables the user to detect and accumulate data through a number of sources, prior to shaping and separating it. Examples of this software include Xplenty or Salesforce DMP.  


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These are the 5 data analysis steps which can be implemented in the data analysis process. Step 1 - Determining the objective Step 2 - Gathering the data Step 3 - Cleaning the data Step 4 - Interpreting the data Step 5 - Sharing the results

Data Analysis Steps

Step three: Cleaning the data


Once the data has been collected, we prepare to execute the analysis which involves cleaning and scrubbing the data and ensuring that its quality remains unmarred. The primary duties involved in cleaning the data include :


  • Getting rid of errors, replicas, and deviation issues that are encountered while the data is aggregated from multiple sources. 

  • Getting rid of nonessential data points, and picking out nonrelevant observations that are not related to the proposed analysis.

  • Giving the data structure by managing any layout problems, or typos and helping in mapping and maneuvering the data in a simple manner. 

  • Replenishing the breach by identifying and filling the gaps while cleaning.


It is important to ensure that the proper data points are analyzed so that the results are not influenced by the wrong points.


Exploratory analysis


Along with cleaning the data, this step also involves executing an exploratory analysis. This aids in detecting any initial trends and to reshape the analyst’s hypotheses. For instance, if we take the example of Prestos Knowledge and Learning, an exploratory analysis of the platform can offer a correlation between the amount that Prestos’s clients pay and how swiftly they divert on to other suppliers to determine the quality of its customer experience. This can lead to Prestos Knowledge and Learning reshaping its hypotheses and focusing on other factors. 


Uncluttering datasets using the conventional approaches can be quite a hassle, but that’s not the case with tools designed for this purpose coming to the rescue. For instance Open-source tools, such as OpenRefine, Trifacta Wrangler and Drake are useful tools that help in maintaining clean and consistent data. Yet when it comes to rigorous scrubbing of the data, R Packages and Python libraries come to the fore. 



Step four: Interpreting the data


Once the data has been cleaned, we focus on analyzing this cleaned data. The approach we take up for analyzing this data relies on our aim. Be it time series analysis, regression analysis or univariate and bivariate analysis, there’s plenty of data analysis types at our behest. Applying them is the real task. This would largely depend on what we hope to achieve by this analysis. The different types of data analysis can be put under four categories. 


1. Descriptive analysis


This form of analysis determines what has already taken place. This is normally carried out prior to the analyst exploring deeper into the issue. For instance, if we take the example of Prestos Knowledge and Learning again, the platform might utilize descriptive analytics to detect the number of users accessing their product during a certain period. They might use it for measuring sales figures in the past couple of years. Even if concrete decisions may not be undertaken through these insights, compiling and expressing the data, will aid them in concluding how to advance. 


2. Diagnostic analysis


This form of analytics is focused on comprehending why a certain issue has taken place, rather as the name suggests, it is the diagnosis of the issue. If we bring up the example of Prestos Knowledge and Learning again, the primary focus of the platform was on determining which factors adversely affect its customer experience. This issue can be resolved through a diagnostic analysis. 


For example, the analytics can aid the platform in making correlation between the main issue and what aspects could be triggering it. These aspects could range from the delivery speed to the project expenses. 


3. Predictive analysis


This form of analysis enables the analyst to detect future trends and forecast future growth on the basis of historical data. It has recently evolved over the years with the evolution of technology. For instance, the insurance industry providers generally make use of past records to forecast which of their clients have the probability of encountering accidents. Through these records they raise the insurance premium of those clients.  


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4. Prescriptive analysis


This form of analysis allows its users to make future recommendations. Being the final step in the analytics process, it includes all analysed aspects previously mentioned.  It suggests many courses of action and highlights their possible consequences. 


CenterLight Healthcare adopts prescriptive analytics to cut down the uncertain element in case of patient appointing and care. This form of analysis aids the organization in discovering the most suitable times for scheduling check-up appointments and treatments to avoid afflicting their patients, and also ensuring the health and security of the patient. 



Step five: Sharing the results


Once the analyst has concluded their analyses and derived their insights, the last step in the data analysis process is for sharing insights with the people concerned. Being more complicated than merely the disclosure of work results it is also concerned with deciphering the results and exhibiting them in an easy manner. 


It is crucial to ensure that the insights have clarity and are explicit. Owing to this, data analysts generally adopt reports, dashboards, and interactive visualizations for supplementing their discoveries. 


How the results are deciphered and exhibited has a significant impact on the course of a business. On the basis of what the analyst discloses, the decision is made regarding restructuring, launching of risky products and if a division is to be shut down. 


This makes it essential to supply all the collected evidence and to make sure that everything is covered in a proper, compact manner on the basis of evidence and facts. At the same time, it has to be ensured that all breach in the data or ambiguous data is highlighted.





These are the 5 primary steps involved in data analysis. With a massive range of data being produced by businesses each day, many sections of it still remain untouched. This data is put to use through data analysis which aids businesses in deriving relevant insights and plays a powerful role in determining their decisions. 

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