What it is, why it matters, and best practices.
This guide provides examples and practical advice to help you create best-in-class data visualizations of your own.
Data visualization is more than transforming data into graphical formats. It’s an essential capability within an organization’s business intelligence (BI) strategy. The most effective visuals tell stories that can help you better understand your data, identify and share new opportunities and insights, and respond to market changes faster.
Retailers use retail data visualization within retail analytics applications to better understand customer buying behavior, analyze market share, explore performance optimize and launch new store locations and more.
Learn how retailers can use a visual representation of data to analyze consumer browsing data.
Financial institutions use financial data visualization within financial services and banking analytics applications to gain insights on sales performance, profitability attribution, branch performance, credit pipeline forecasting and more.
Discover how banks use advanced techniques such as spatial analysis to explore loan portfolio performance.
Visualization of healthcare data within healthcare analytics applications can help organizations in this industry better understand clinical variations, revenue cycle management, labor productivity, patient readmission risk and more.
Discover the top 12 healthcare insights powered by visual analytics.
This type of data presentation helps you identify highest and lowest values, compare recent and older values, and recognize trends. Comparison visualizations give insight into which products sell best, or how this year’s sales compared to last year. Examples include bar charts, line charts and circular area charts.
This presentation type allows you to see the changing relationship among data points over time, or see the relative difference between parts of a whole. These help you understand your market share size, or analyze where you spend your budget. Examples include pie charts, stacked area charts or stacked bar charts.
This type of data presentation helps you spot outliers and commonalities, as well as see the shape of your data. For example, you might uncover insights about the number of customers in a specific range, or customer payment trends. Examples include bar histograms, line histograms and scatter plots.
This type shows correlations and clusters, and helps you see outliers. These insights uncover how advertising spend and sales are correlated, or variations between expenses and revenue across regions. Examples include scatter plots, or scatter plots with different bubble sizes.
Understand the size and scope of your data, including what kind of information you want to communicate, and the kinds of decisions you want people to make.
Find out what your audience wants to accomplish, and how you can best enable them to take action on the insights they uncover.
Size up your data and determine the visual technique you should use to present your story in the simplest way possible.
Rather than restricting users to a limited drill path, enable them to explore all relationships in the data, so they can get the whole story.
Help users explore data in a guided way by giving them access to the data that’s most relevant to their analysis, without requiring them to ask for it.
From websites and portals to apps and business processes, let users visualize their data anywhere they make decisions.
No matter how pleasant your visuals appear, if the underlying data doesn’t tell the right story, users won’t get value from them. To avoid telling incomplete, misleading or inaccurate stories, understand your data first. And be sure to spot and resolve any data issues before you publish.
Trying to cram too much data into a visual can leave users confused and frustrated. Instead, limit the number of KPIs in your dashboard, use pie charts for limited data sets, choose colors carefully, and use the simplest format possible.
While many people feel comfortable using spreadsheets and ungoverned analytics tools to create their own presentations, this presents many challenges. Implement proper data governance practices to avoid inaccurate data stories, incomplete analyses, and non-standard visuals.
When users create visualizations by manually manipulating data in spreadsheets, they can make data and mathematical errors, waste hours of productivity, and distribute improper information. AI and machine learning can help you automate time-consuming tasks and overcome these challenges.
The best tools offer the flexibility to visualize data in the most relevant and intuitive formats and can combine data from multiple sources to give users the full picture.
Static charts and linear drill-downs stop short of answering user questions about their data. Tools should allow users to freely explore data in whatever direction their intuition leads them.
Data analytics tools use augmented intelligence to recommend visualizations that can help even novice users build their own analytics views and discover hidden insights.
People need access to visual representations of data and insights no matter which application they’re using. The best tools make it easy to embed analytics wherever people are working.
A best-in-class, self-service business intelligence architecture is just one way Qlik Sense® sets the benchmark for next-generation data analytics technologies. Our one-of-a-kind associative analytics engine and sophisticated AI empowers people at all skill levels to freely explore data, make bigger discoveries and uncover bolder insights that can’t be obtained using other analytics tools. With Qlik, you can support nearly any use case and massively scale users and data, empowering everyone in your organization to make better decisions every day.
