The Power of Data Analysis in Research

For as long as humans have existed, we've been conducting research. We do it to solve problems, advance technology, understand diseases and develop treatments, inform policymaking, and, most fundamentally, expand human knowledge. At the heart of all research is data. Data, whether observations, statistics, surveys, or interviews, serves as the building blocks for all research.

The more we research, the more data we collect, and the more thorough our analysis will be. Data analysis illuminates patterns, trends, and relationships within the data and plays a pivotal role in shaping the outcomes and conclusions of research. In the sections below, we'll look at different data analysis methods, popular tools researchers use today, and how to make the most of your data.

On this page:

• Understanding Data Analysis
• The Data Analysis Process
• Data Analysis Methods
• Tools and Software for Data Analysis
• Choosing the Right Tools for Your Research
• Applications for Data Analysis in Research
• Challenges in Data Analysis
• Future Trends in Data Analysis
• Getting Started with Data Analysis
• Conclusion

Understanding Data Analysis

Data analysis is the most crucial part of any research. It allows researchers to understand the information gathered, test hypotheses, and draw conclusions. Analysis is the process of interpreting raw data through logical reasoning and applying various statistical and analytical techniques to understand patterns, relationships, and trends within the data.

The Data Analysis Process

Researchers must follow a methodical data analysis approach to ensure accurate results. Skipping steps can skew the outcomes and leave research findings vulnerable to scrutiny.

1. Plan and Design the Analysis: Determine the problem you want to solve before analyzing data. Defining the objectives of the analysis is crucial because it establishes the research direction and identifies the data you need to use to solve the problem.
2. Collecting and Preparing Data: Once the proper data is identified to use, it must be cleaned by checking for missing, inconsistent, and outlier data, ensuring accurate results from the analysis.
3. Analyzing Data: Once cleaned, apply statistical and mathematical to find patterns, relationships, or trends in the data.
4. Interpreting Data: After analysis, interpret the results and report actionable insights in ways non-data analysts can easily understand, e.g., using graphs or tables.

Data Analysis Methods

There are so many different reasons to conduct research and so many types of data that there is no one-size-fits-all approach to analysis. Instead, there are many methods, each with its unique purpose.

 

Qualitative analysis	Non-numerical data gathered and produced using observation methods
Quantitative analysis	Measurable and verifiable numeric data
Descriptive statistics	Describes the characteristics of a data set: mean, mode, median, range, and standard deviation
Inferential statistics	Tests hypotheses and draws conclusions about the larger populations from the data sample
Regression analysis	Uses historical data to understand how variables affect each other and potential outcomes
Machine Learning (ML)	AI that learns to identify patterns in large data sets and can help predict future outcomes

Tools and Software for Data Analysis

As digital technology has advanced, the number of data analysis tools available to researchers has exploded. Some of the most well-known data analysis tools include:

 

Microsoft Excel	It has extensive calculation and graphing functions and plug-ins ideal for data analysis, including pivot tables.
Microsoft Power Platform	Power BI, PowerApps, Power Automate, and Power Virtual Agents allow data analysts to manipulate, visualize, automate, and analyze data and connect to hundreds of data sources and services.
Python	A popular open-source programming language known for its flexibility and scalability, it can handle many different analyses independently and integrate with third-party ML and data visualization packages.
R	A commonly used open-source programming language that integrates with other languages like C and C++ that is harder for beginners than Python but has a vast collection of libraries for data analysis and ML
SQL	Structured Query Language that allows you to work with, edit, extract, sort, and filter data from multiple databases

Choosing the Right Tools For Your Research.

There is no universal data analytics tool that will address all your needs. Everyone who works with data at some point needs secondary tools and plugins. Here are some things to consider when looking for data analysis software:

 

• What is your budget?
• Is there a viable free version, or do you need a subscription?
• What is your technical expertise?
• What is the scalability and flexibility?
• Can the tool integrate with your existing data sources?
• Can it handle the volume of data you’re working with?
• Do you require a tool with modeling capabilities?

Applications of Data Analysis in Research

Data analysis is in high demand across industries, driving innovation that improves an organization's business outcomes and the lives of employees and customers. To understand how data analysis applies to different types of research, let's look at some real-world examples:

• Healthcare
• Finances
• Environmental studies

Example #1: Healthcare

Data analysis in the medical field has dramatically improved healthcare outcomes. For example, epidemiologists investigate patterns and determinants of disease occurrence and distribution within populations. Through data analysis, they've made great strides in identifying associations between lifestyle factors (e.g., smoking, diet, and physical activity) and chronic diseases like cardiovascular disease, diabetes, and cancer.

Example #2: Finance

Data analysis plays a central role in assessing and managing financial risks. Analyzing historical data and using techniques like value-at-risk (VaR) and stress testing allows risk managers to quantify the potential impact of adverse events on investment portfolios and implement risk mitigation strategies.

Example #3: Environmental studies

The world's leading climate scientists use data analysis in their research. They analyze large datasets of temperature records, atmospheric CO2 concentrations, sea level measurements, and other climate variables to detect trends and patterns over time. This climate data allows researchers to understand global warming better, assess its impacts on ecosystems and human societies, and inform climate policy decisions.

Challenges in Data Analysis

The insights you gain from analyzing data are only as good as the data they are based on. Knowing the common challenges associated with data analytics is essential for data analysts, both new and experienced. Some of the common challenges data analysts face are:

• Setting clear hypotheses and goals
• Understanding the data being analyzed
• Knowing the correct source data to use
• Determining the integrity and trustworthiness of the source data
• Maintaining the privacy and ethical use of data
• Communicating data insights using easily understood methods
• Remaining objective throughout the analysis

Any of these challenges can lead to incorrect analysis, impacting organizational decision-making. There are several ways in which data analysts can overcome these challenges, including seeking advice from fellow data analysts or taking self-paced or online training. By overcoming these data analysis challenges, data analysts can ensure they provide correct insights to improve an organization’s business outcomes.

Future Trends in Data Analysis

Many future trends will impact data analysis, especially from a technology and regulatory standpoint. These trends will allow data analysts to work with more data that can provide deeper business insights for organizations while ensuring that it is used ethically and remains private and secure. Some of the future trends that will impact data analysis include:

• Artificial intelligence (AI) and machine learning (ML) are changing data analysis by automating complex data processing tasks. These tools can identify patterns in massive data sets and provide highly accurate insights and predictions.
• Regulation: The European Union's General Data Protection Regulation (GDPR) went into effect in 2018, heralding a new era for data privacy. It levies harsh fines against any person or organization that violates its privacy and security standards, aiming to protect consumer data. As the volume of global data increases, other international governments will follow suit. 
• Quantum Computing: As organizations generate more significant amounts of data, the need for computers that can store it grows. Demand for more powerful computers to process vast amounts of data is rising. Quantum computing may be the answer, with its ability to store vast amounts of information using qubits and much less energy.
• Data Democratization: As analytics platforms evolve to become more powerful and intuitive, it will allow anyone, regardless of data analysis experience, to harness and analyze data. Self-service analytics significantly reduces the time and effort required to retrieve insights from data so that data analysts can focus on more specialized work.

Getting Started with Data Analysis

The typical starting point for a career in data analysis is through collegiate programs such as computer science, mathematics, and programming. However, you don't have to attend college to become a data analyst. Professional training courses on data analysis are a viable option if you want to start your data analysis career. For example, New Horizons offers online training courses in Data and Analytics, which fall into three categories:

• No-Code is appropriate for individuals who want to improve their data analytics skills without learning a programming language.
• Low-Code: Appropriate for those with limited programming skills or data analysis experience.
• Traditional Data & Analytics: Appropriate for those with programming and data analysis experience looking for courses for specific job roles.

New Horizons offers several Python training courses, as well as vendor-specific data analytics courses, such as:

• AWS Data Analytics Bootcamp: Four one-day AWS courses, including Building Data Lakes on AWS, Building Batch Data Analytics Solutions on AWS, Building Data Analytics Solutions Using Amazon Redshift, and Building Streaming Data Analytics Solutions on AWS.
• Microsoft Power Platform: Our subject matter experts show you how to do more with your data, including manipulating, visualizing, automating, and analyzing it using PowerBI, PowerApps, Power Automate, and Power Virtual Agents.

For beginners, completing small projects using public datasets can provide a great way to gain practical data analysis experience. Platforms like Kaggle, GitHub, and Data.gov offer publicly available datasets, providing a great way to apply theoretical knowledge and develop technical skills.

Conclusion

Organizations will always look for ways to improve and innovate; data analysts can help define and solve problems to help the organization move forward. By pinpointing patterns and extracting actionable insights from large quantities of data, data analysts can help guide organizations toward more innovative and customer-centric solutions. As data analytics tools evolve, they will allow even those with little to no data analysis experience to work with data and make better decisions that will help their organization reach and surpass its goals.