What is Data Analysis? Data Analysis Process

What is Data Analysis?

How data analysis used in business?

Data Analysis has become a vital need for businesses today. It is one of the secrets behind the decision making powers of successful companies around the world. It helps organizations to make better business decisions. Data Analysis helps the businesses- whether it is market research, product research, positioning, customer reviews, overall analysis, any issue in the system, etc. So, analyzing all these factors before making the actual decision helps the organizations to get better results.

Famous Companies that use the Data Analysis Process

Data Analysis Tools

As the data analysis craze is booming among small and big organizations, the demand for the data analysis tools for better decision-making is also increasing. Here is the list of data analysis tools:

Data Analysis Methods

Generally, data analysis is carried in two ways and techniques - Qualitative Analysis and Quantitative Analysis.

Qualitative Analysis

This data analysis technique is based on the questionnaire like "what," "why," and "how." Interestingly, all of these questions are prepared using quantitative techniques such as attitude scaling, standard outcomes, etc.

Quantitative Analysis

This data analysis technique is based on the numbers of how each factor is measured, etc. Other data analysis techniques are text analysis, statistical analysis, diagnostic analysis, predictive analysis, prescriptive analysis, etc.

Data Analysis Process: How data analysis process carried out?

Defining the Question

Before the data analysis process, it is essential to ask why you need the data analysis? Asking the data analysis questions means you are defining the "problem statement." Starting with the question - Which business problem am I trying to solve? For instance, your organization's main concerns are why we are losing customers? In such situations, you have to define the questions like "Which factors are negatively impacting the customers?" and "How can you gain customers in minimum costs?" So, once you define the questions or clear to perform the data analysis, you are good to go with the data analysis process. You have the reason you are performing the data analysis; you can go further with the step of data collection.

Tools to use for Defining the Question

Databox, Dasheroo.

Data Collection

It is another crucial step for data analysis - the collection of data and requirements. Now, you have the objective to complete the data analysis process, so, in this step, you will collect the data in the form of facts, figures, numbers, and customer reviews, etc. The data collection is categorized into three categories: first-party, second-party, and third-party.

First-party Data Collection

First-party data collection means that Data, your organization has collected directly from the customer reviews. It can be in the form of transactional tracking data or your company's Customer Relational Management(CRM) systems. It includes surveys, groups, interviews, and observations.

Second-party Data Collection

Second-party data collection means you are using the data of other companies or organization's first-party data collection. It includes websites, apps, social media, shipping, online purchase histories.

Third-party Data Collection

The Data has been collected using numerous sources by a third-party organization. Many organizations collect big data to create industry reports like Gartner. It includes third-party organizations, repositories, and government portals.

Tools to use for Data Collection

Salesforce DMP, Xplenty.

Data Cleaning

Data cleaning is managing and detecting the missings, errors, and inaccuracies in the collected data. It includes the steps for cleaning the data are: • Removal of major or minor errors, duplicates, and other inaccuracies • Removal of unwanted and inappropriate data points • Providing structures to the disorganized data sets • Filling up the significant gaps by cleaning the Data It is one of the facts that the data cleaning process takes up to 70% of the data analysis process's time.

Tools to use for Data Cleaning

OpenRefine, DataLadder.

Data Analysis

Once you have done the data cleaning process, the next step is to perform the data analysis. This can be achieved by analyzing the data through four categories like Descriptive analysis, Diagnostic analysis, Predictive analysis, Prescriptive analysis.

Descriptive Analysis

This analysis process comprises the process of identifying what has already happened. For instance, "how many customers are interested in your organization?"

Diagnostic Analysis

This analysis process includes the diagnosis of factors why it has happened. For instance. "Which factors are negatively impacting your customer experience?"

Predictive Analysis

This analysis process allows you to find our future trends based on historical data. For instance, "How many customers will you gain if you improve the customer experience?"

Prescriptive Analysis

This analysis process is performed to make recommendations for the future. For instance, "Which products or business areas to invest in?"

Result Sharing

This step is one of the essential steps after the data analysis. The Data is explored; conclusions are drawn; it's time to share the findings and results through blog writing, presentations, and making reports.

Tools to use for Result Sharing

Infogram, Google Charts, Datawrapper.

Embrace the Failures

Data Analysis Process is completely iterative. In last, embrace your failures, mistakes happen, and learn from them. So, these transformations help your business to become successful. So, this is all about the data analysis process.

Final Thoughts