Your Modern Business Guide To Data Analysis Methods And Techniques
In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.
Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery, improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.
With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.
To help you understand the potential of analysis, the meaning, and how you can use it to enhance your business practices, we will answer a host of important analytical questions. Not only will we explore data analysis methods and techniques, but we’ll also look at different types of data analysis while demonstrating how to perform analysis in the real world with a 15-step blueprint for success.
What Is Data Analysis?
Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis.
All these various methods for data analysis are largely based on two core areas: quantitative methods and qualitative methods in research.
To explain the key differences between qualitative and quantitative data, here’s a video for your viewing pleasure:
Gaining a better understanding of different techniques for data analysis, and methods in quantitative research as well as qualitative insights will give your information analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis processes.
Why Is Data Analysis Important?
Before we go into detail about the categories of data analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.
Let’s start with customers, arguably the most crucial element in any business. By using data analysis to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more.
In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.
From a management perspective, you can also benefit from analyzing your data as it helps you make business decisions based on facts and not simple intuition. For example, you can understand where to invest your capital, detect growth opportunities, predict your incomes, or tackle uncommon situations before they become problems.
Like this, you can extract relevant information from all areas in your organization, and with the help of a dashboard software, present the data in a professional and interactive way to different stakeholders.
7 Essential Types Of Data Analysis Methods
Before diving into the seven essential types of data analysis methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive analysis up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.
a) Descriptive analysis – What happened.
The descriptive analysis method is the starting point to any analytic process, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights to your business.
Performing descriptive analysis is essential, as it allows us to present our data in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, but it will leave your data organized and ready to conduct further analysis.
b) Exploratory analysis – How to explore data relationships.
As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there’s still no notion of the relationship between the data and the variables. Once the data is investigated, the exploratory analysis enables you to find connections and generate hypotheses and solutions for specific problems. A typical area of application for exploratory analysis is data mining.
c) Diagnostic analysis – Why it happened.
One of the most powerful types of data analysis. Diagnostic data analytics empowers analysts and business executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.
Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics, e.g.
c) Predictive analysis – What will happen.
The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Like this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.
With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge on the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.
e) Prescriptive analysis – How will it happen.
Another of the most effective types of data analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.
By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key business areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics, and others.