Today, all companies are reliant upon data science in order to make stronger data-informed decisions, streamline processes, and secure the competitive edge in the market. Data analysis and data visualization are two interconnected components of data science and play an important role in extracting insights from data and communicating those insights to important stakeholders.
On one hand, data analysis focuses on understanding data and extracting information from the patterns and trends within data sets. Data visualization, on the other hand, represents the findings of the data analysis process in a way that’s easy for non-data analysts to understand and interpret. The two processes are interconnected and have the same goal: extract valuable insights from data sets and communicate those findings in a way that can improve decision making and advance the organization.
What is Data Analysis?
We hear about data all the time, but how do companies actually use that information to inform their business decisions? That is the work of data scientists who play increasingly crucial roles in informing organizations of strategic actions they may choose to take.
Data analysis is the process of cleaning, organizing, and interpreting data sets with the intention of extracting valuable insights from the patterns, relationships, and trends found in the data. Those insights inform company decisions, help develop organizational goals, inform marketing campaigns, and transform products in ways that are more aligned with consumer trends, customer profiles, or purchasing habits.
What is Data Visualization?
Data visualization is the process of transforming findings into graphical or other visual representations. The goal of data visualization is to represent complex data in a way that can be easily understood and interpreted by a broader audience. This audience may include other company teams, external stakeholders, or consumers: in other words, folks who may not come from strong data analyst backgrounds.
Part of a data analyst’s role is to determine the best way to visualize data. This will depend on your audience, the type of data, and the story you’re helping to communicate through the data. These data visualization techniques may include:
Graphs and charts: could be bar graphs, line graphs, scatter plots, pie charts, or other visual graphs.
Maps: are used to showcase geographical data, spatial patterns, and distribution over space.
Infographics: are a common way to combine numerical data with text in order to tell a story in a clear and concise way.
Dashboards: are an interactive way to display data. Users are able to explore different aspects of data sets and gain a more holistic view of the data.
In short, data visualization is a crucial component of data analysis. It’s a process in its own right that requires an understanding of your audience, the information they’re looking for, and how to best visualize your findings such that complex data is easily interpreted and key insights are highlighted.
If you’re interested in a more in-depth take on data visualization, read about how to improve your data visualization skills on our blog.
The importance of data visualization in data analysis
Data is everywhere, but it’s meaningless if decision makers and other stakeholders can’t intuitively engage with it. This is why proper data visualization becomes so crucial. Data visualization is a major aspect of the data analysis process specifically because it brings data sets to life that may otherwise seem dull or overwhelming to many business professionals.
Clearly communicating findings in a way that stakeholders can easily engage insights is what ultimately transforms data sets into a powerful resource for businesses. Here are a few simple ways in which proper data visualization may improve your organization’s decision making processes:
Clarity and understanding: in the most basic sense, data visualization transforms messy data sets into easily digestible visuals that folks with non-technical backgrounds can easily engage and pull insights from. Providing a clear and comprehensive visual makes complex data sets less intimidating and offers an intuitive way for professionals to take part of the data analysis process.
New insights: through presenting data in an alternative way, analysts and stakeholders alike may arrive at new insights or patterns that may have otherwise gone unnoticed. Through visualizing the data in an interactive way, viewers are empowered to take part of the analysis process, ask questions, and gain insights that may relate more directly to their areas of work.
Enhanced decision-making: data is powerful because it leads to data-informed problem solving and decision-making. Well-designed data visuals can also communicate possible implications of alternative decisions and give greater insights to the potential outcomes of different choices and strategies.
Streamlined communication: effective data visualization ensures that all teams and stakeholders have a shared understanding of the data analyst’s findings. This collective knowledge fosters collaboration and facilitates strong communication between teams and across disciplines.
Comparisons: data visualizations provide graphics that can easily compare data sets, differing variables, or time periods. These graphics are particularly useful in looking at trends, comparing quarters, and noting transformations over time.
These are just a few of the many ways that data visualization advances the data analysis process. As we know, data analysis is crucial in informing organizational strategy today, but only insofar as a data analyst can communicate their findings to other tech professionals and make the case for their data-informed recommendations. Through making that data digestible to a larger audience, data-based insights become more reliable, relatable to specific teams, and can be expanded upon to strengthen business decisions.
Popular data visualization tools and techniques
The world of data analysis continues to expand as more businesses invest in data sets and softwares to aid the data visualization process. That said, some of the most prominent data visualization tools include:
Microsoft Excel for data analysis and visualization: Microsoft Excel is one of the most widely used spreadsheet tools that offers built in data visualization tools like charts and graphs. This program is recommended for folks who need quick data visuals for simple data sets. That said, Excel does not offer many of the newer data visualization tools that other specialized software may include.
Tableau: Tableau is a powerful business intelligence tool that allows users to create interactive and shareable dashboards and data visualizations. The software is suitable for complex data sets, supports a wide range of data sources, and offers numerous chart and graph types.
Power BI: similar to Tableau, Power BI is a strong business intelligence tool that supports users in creating interactive dashboards and data reports. It’s Microsoft’s business analytics software and thus works well alongside other Microsoft products.
QlikView and Qlik Sense: the Qlik tools allow data analysts to explore data, create interactive dashboards, and generate dynamic visualizations. QlikView is focused on offering pre-designed, complex dashboards and reports to end-users, while Qlik Sense’s interface is a self-service business intelligence platform that emphasizes user-friendly interfaces and collaborative development for both developers and non-technical users.
It’s up to organizations to determine which softwares, tools, and techniques they wish to take advantage of in the data analysis and visualization processes. This will depend on the type of data organizations are delving into, as well as the audience that will later receive and engage with the data visualizations.
Delving Deeper into Data Analysis and Data Visualization
Data analysis is proving to play a major role in many of the fastest-developing industries from informational technology to financial services. In 2020, the World Economic Forum ranked Data Analysis as the second fastest growing job in the United States, with 25% growth before 2030. That said, if you’re looking to advance in the field of data science, earning a data analysis and visualization certificate, signing up for a data analysis and visualization bootcamp, or simply taking a data analysis and visualization course may be a valuable decision.
If you’re committed to diving deeper into the world of data analysis and visualization, here are a few ways we’d recommend expanding your credentials:
Earning a data analysis and visualization certificate
There are numerous data analysis and visualization certificate programs to choose from. Choosing the right certificate program is crucial in measuring your knowledge and skills against the industry standards. Equally, it’s important to select a certificate program that teaches the skillsets you’re seeking in order to be successful in the specific areas that you’re looking to advance. Knowing this, here are a few of the most popular data analyst certifications in 2023:
Google Data Analytics Professional Certificate: Google offers a flexible online certification that provides a solid foundation to the world of data analytics. The course is a “learn at your pace” program, with most participants completing the program in just six months. The certificate is designed for beginners with no prior technical experience with the intention of teaching the basics of data cleaning, visualization, and analysis alongside instruction of how to utilize spreadsheets, SQL, and R programming.
Microsoft Certified: Power BI Data Analyst Associate: Microsoft’s Power BI certificate will teach participants the basics of Microsoft Power BI, instructing on how to identify and acquire data, transform data into data models and visuals, and later share those visuals and insights with relevant stakeholders.
IBM Data Analyst Professional certificate: the IBM Data Analyst Professional certificate is a flexible online course that promises to prepare folks with little to no experience in data analysis for entry level data analyst positions. The course walks through Python, SQL, Excel, and IBM Cogno analytics.
Signing up for a data analysis and visualization bootcamp:
If you’re looking to jump deeper into the world of data analysis, we’d recommend checking out our Data Analytics Bootcamp:
Bootcamps offer an immersive opportunity to dive deep into the subject matter in a relatively short amount of time. With Ironhack, you’re able to sign up for a full-time or part-time data analytics bootcamp that will have you learning an up-to-date curriculum alongside industry experts.
Our Data Analysis Bootcamp begins with data prework and is followed by three modules that slowly increase in intensity. We begin with an introduction of data analysis and Python; this work folds into advanced data analyst work and finally, you’ll go through the fundamentals of machine learning–something that’s unique to Ironhack, earning our bootcamp the “Intelligent Award: Best for Machine Training”.
If you’re interested in learning more, check out our Data Analytics Bootcamp and start transforming your career.