What is Data Visualization and why is it important?

What is Data Visualization?

A pictorial representation of data depicting imperative information like complex relationships within large datasets constitutes what one usually refers to as Data Visualization. The objective is to refine large amounts of data into visual graphics and identify different trends, outliers, and patterns in data. It provides a visualized context to the given information or data through elements such as graphs, charts, maps, etc.

Visualization gives you answers to questions you didn’t know you had.” – Ben Schneiderman

The growing library of visualization types has encouraged users to gain versatility in data analysis and management. Visualization is classified as ‘Exploratory’ – which helps the user find the story in the picture, and ‘Explanatory’ – which tells a story to the user.

These basic categories are further divided into 5 sub-categories which constitute different types of visualizations as given in below:

Data Visualization for Business Analysts

Data visualization has been a boon for both operational savvy as well as technical business analysts handling multiple projects at a time. It provides the much-needed solution for analysts and project managers to understand business problems, rules, and of course, the requirements.

Modern-day tools have made it possible for business analysts to process heaps of data and interpret information with a specific strategy. Not only business analysts, but even decision-makers are also benefitting data visualization to comprehend trends and issues while making profitable business decisions.

As per a recent Harvard Business Review Analytic Services Plus Survey of more than 700 business leaders:

Data Visualization Vs. Business Intelligence

Both data visualization and business intelligence are a crucial tool for businesses to stay atop the contemporary market. However, one should not confuse data visualization with business intelligence.

While data visualization is all about visuals, the main focus is on output. You represent information using a convenient visual format, interpret the information beneath, and communicate your findings to important stakeholders through visualization.

Business intelligence, on the other hand, concentrates more on the input. Focus is mainly on gathering data, organizing it, recognizing hidden patterns, and extracting meaningful information to drive business decisions.

“Data is what you need to do analytics. Information is what you need to do business.” – John Owen.

This above quote is enough to emphasize the fact that while data visualization takes you through a data ride exploring various data sets, business intelligence provides you with information that can enable accelerated business growth.

Why is Data Visualization important in Data Analytics?

It is a well-known fact that our brain can easily process data in a visual format, deciphering the underlying patterns and trends rather than sifting a vast amount of spreadsheet rows.

James Haight of Blue Hill Research says Our human brain might not easily understand complex statistical models but can learn and predict patterns through neural networks, and even recognize these patterns later. Not to forget, visual images are the most popular input to our brain’s recognition process.”

Colin Ware, in his book ‘Information Visualization’ says: “We acquire more information through vision than through all of the other senses combined. More than 20 billion are so neurons of the brain devoted to analyzing visual information provide a pattern-finding mechanism that is a fundamental component in much of our cognitive activity.”

As per a recent survey by TWDI Research on about 453 respondents (mostly including business executives, Users, Data, and IT professionals), about 57% of users in many organizations use data visualization for developing basic reports, snapshot reports, and/or scorecards. About 26% are currently implementing data visualization to create and offer alerting functions to stakeholders, while 33% use data visualization for implementing visual data discovery and analysis.

The figure below demonstrates a detailed report on how organizations are implementing data visualization for activities like report creation, alert creation, and data discovery and analysis.

Figure 1: Importance of Data Visualization in Companies

Given below 5 points illustrate importance of data visualization in data analytics:

How do I pick the right Data Visualization tool?

With the evolution of big data and the emergence of various technologies to access information, a wave of technological transformation has engulfed almost all the industries. However, data would be a mere liability unless you have a proper tool to present it, understand it, and leverage it to gain insights more making effective business decisions.

As per James Foster (A computer professional)’s interview with Tech Trends Journals, business administrators must explore a few key components within their business before approaching any visualization tool.

If you limit the process to the vendor and your IT team, you will get good visualization that works well for the IT team, but it might not be useful to the actual consumers of the data, Foster says.

A right data visualization tool would be the one that proves to be the best option for providing an engaging and intuitive channel of communication. An effective tool would provide you with a platform to automate the process of creating visualizations from data sets dealing with thousands of data points.

Given below factors are crucial to pick up the right data visualization tool:

1.Data Mashup:

Often, the requirement is to have multiple data sets and reports on a single platform with a standard look and feel. For example, you might need a report which shows both social media and paid search data in a single place. This requires a tool that allows you to collect data from multiple sources on a single dashboard.

2. Data Transformation:

Unless and until you require to have a simple tool to view your data trends and patterns, you should always count on a tool that allows you to customize available data sets. You can create new fields, create custom aggregations, and update original data into more useful and understandable language.

3. Data Connection:

Even though there are a plethora of data sources available in the market, not all visualization tools can afford connecting to each one of them. Whether your data resides in any search engine, website, or an internal or external database, your visualization tool should connect to the source without any hurdles. In fact, a useful visualization tool would be one that allows for fetching live data by a mere click on the refresh button.

4. Data Size:

The size of a data set is pertinent in deciding the speed and performance of your data visualization tool. Look out for a tool with maximum allowable data size such that your data does not get truncated while importing.

5. Data Collaboration:

Sometimes, the visualizations your tool creates is not limited to you or your team members accessing the tool. Different stakeholders need access to various data visualizations to make timely decisions. Count on tools that allow you to share your reports through external links and export the data in the required format.

6. Advance Visualizations:

Often requirement of a visualization tool goes beyond the mere view of trends and patterns in data. You need tools that allow you to interact further with data through drilling and filtering, add machine learning models directly to your dashboard, and perform what-if analysis on data.

7. Budget:

Last but not least, the amount you can spend on a tool is important before making a choice. Choose a tool that provides a balance of benefits and cost, where you can fulfill all business requirements with reasonable price and maximum return of investment.

Given below are important results from a recent survey by TWDI Research on how different users in your organization use data visualization tools:

  • 50% give importance to data visualization as a single source of truth, while 34% think it as somewhat important.
  • 54% use tools for self-directed BI and data discovery, while 31% think it as somewhat important.
  • About half of the respondents (47%) implement data visualization for advanced analytics.
  • About 27% give importance to tools that allow user collaboration, while about 38% find it somewhat important.
  • 55% find tools essential for performing aggregations and measures.
Figure 2: Chart showing factors affecting choice of visualization tools

Additionally, Forrester analysts Boris Evelson and Noel Yuhanna point 6 traits that distinguish an advanced visualization tool from a simple static graph tool. These include:

How is the demand for data visualization services?

Data visualization has evolved a long way from maps and graphs in the early years to the use of automated data visualization tools in recent years.

The global data visualization market is expected to grow at a rate of 9% in the forecast period from 2019 to 2027. A surge in demand for cloud technology and the urge for swift decision making are the two factors behind this increase. Moreover, new technical regulations and pressure from customers are also pushing businesses around the globe to adopt data visualization as one of the key areas of development.

Given below are a few key takeaways from a recent report by Mordor Intelligence:

  • In 2019, 55% of Amazons’ sales were the result of its exclusive efforts to understand client needs by analyzing enormous data using visualization tools.
  • As per Seagate Technology PLC, the volume of data is expected to increase to 163 Zettabytes by 2025.
  • According to Wharton, the use of data visualization would lessen business meetings by 24%
  • The retail sector is the upcoming market for data visualization with more focus on increasing connectivity, communication, and a huge amount of data generated.
  • North America is the most prominent market for data visualization with the increasing use of advanced technologies and strong foothold of visualization tool vendors.

Data visualization is among the top 25 hottest skills in demand, with many companies looking out for data visualization experts to visualize data, find insights, and convey the information effectively to both technical and non-technical persons. More is the data assimilated by organizations, and more is the need to create meaningful visualizations to explore and interpret information.

What is the best tool for visualizing data?

As mentioned above, a lot of data visualization tools are emerging in the market, providing businesses a much-required platform to transform large data sets into meaningful visualizations.

Even though there are a lot of tools in the market, only a few can manage to be the foremost in terms of best features, support, value, and optimal price. In most of the cases, there are tools that standout either in terms of supported visualization types, in-built connection support, provided documented support, or other options.

Given below are the top 5 data visualization tools you can explore for your business:

1. Tableau:

Tableau is the most widely used data visualization tool with a lot of visualization creation options. Its advantages include outstanding visualization capabilities, easy to use the platform, ease of connectivity to multiple data sources, and many other features. However, the high cost with inflexible pricing and lack of scheduling and auto-refresh options make it tougher to choose over its counterparts.

2. Sisense:

Sisense is one of the emerging data visualization tools with its user-friendly interface, excellent support, and ease of integration with different data sources. However, disadvantages like difficulty to maintain, complicated analytic cubes along with the limited type of visualizations subdue its advantages.

3. Dundas BI:

Dundas BI is one of the oldest data visualization tools offering out of the box interactive visualizations. Along with rich visualization features like rich scorecards, various chart types, data labels, diagrams, and relationships, it provides users the flexibility to write direct SQL queries against any type of data source. However, compared to its peers, it lacks modern-day predictive analysis support as well as 3D visualizations.

4. Domo:

Domo is a mobile as well as a cloud-based data visualization tool providing both micro and macro level visualization features with unlimited data storage, a wide range of data connectors, real-time dashboards, and outstanding collaboration capabilities.

However, its lack of support for augmented analytics (machine learning and natural language processing) makes it a difficult choice to make.

5. BIRD:

BIRD might not be that popular as the above-mentioned tools, but it can definitely be claimed as a promising newbie outshining most of its counterparts. Its reasonable price, a wide variety of interactive visualizations (even 3D!), easy to use features, ease of report scheduling and sharing, and not to forget augmented analytics support make it the best choice among its peers.

The figure below shows Gartner’s magic quadrant for the year 2020, and we are pretty sure BIRD has features suffice to be included in upcoming quadrants within the next 5 years.

Figure 3: Gartner’s magic quadrant 2020

What is the Future of Data Visualization?

Gone are days when you would be amazed at your first 3D pie chart creation. Recent years have seen an unexpected escalation in the number of masterpieces incorporating the perfect blend of art and science through interactive and storytelling animations and multimedia.

Given below info graph summarizes what is in store for data visualization by 2025:

Data Visualization Trends for 2020

Increasing investment in digitization, along with data-generating systems, has seen a massive surge in the information available to organizations. Organizations need to always be on a cutting edge to adapt themselves to the changing ecosystem. Growth in cloud technology adoption, urge for data visualization tools to deliver a single source of truth, and never-ending demands of end-users are sure to impact data visualization trajectory in 2020. Advanced visualization techniques have made data visualization one of the hottest and appealing topics in 2020.

Given below are the top 5 data visualization trends to look forward in 2020:

Trend #1: Simple visualization will not be enough:

Data visualization would go beyond graphs and charts to enriching types like google charts, search consoles, maps, etc. These advanced visualizations would provide much required interactive and exploratory environment to empower you in making profitable business decisions through extracted insights.

Trend #2: Visualization would go social

The integration of social media with data visualization is sure to engage a lot of users. Social media visualizations like GIFs, images, and YouTube shots are sure to grab social media users’ attention.

Trend #3: Visualization will enhance storytelling

The past few months have seen a fair share of major events throughout the globe, and data visualization has played a significant role in communicating the exact stories and experiences in the world. Storytelling has been a significant area of focus in 2020, with various visualization tools being able to create exciting and crucial stories from complex data.

It is the context around the data that provides value, and that’s what will make people listen and engage.Christy Petty writes in a Gartner blog post ‘Use Data and Analytics to Tell a Story.’

This implies how storytelling enhances interaction with data and helps users engage in ways beyond what standard facts and figures cannot achieve.

Trend #4: Augmented Analytics is the new Trend

The rise of artificial intelligence has made businesses much smarter than before. Augmented analytics with a combination of machine learning and natural language processing capabilities has redefined the searching capabilities of many BI tools.

Augmented analytics will be a dominant the driver of new purchases of analytics and business intelligence. Similarly, data science and machine learning platforms, and of embedded analytics by 2020 –  Gartner predicts.

Trend #5: Companies will go hybrid

2020 is surely going to be a year of hybrid solutions adopted by companies. Data storage, along with product features, is going to cloud-based as well as on-premise supporting, or even both. Data visualization tools, undoubtedly, need to adapt to this new trend.

As per a recent report by Mordor Intelligence, the retail sector is the most benefitted sector with the growth of data visualization. Retailers across the globe are embracing data visualization to understand customer behavioral patterns at every stage of the retail process.

The figure below shows growth in retail sales until 2020 in the US market with the implementation of data visualization:

Figure 4: Growth in US retail sector with data visualization

In a Nutshell

2020 will be a year when organizations will be split into those who find opportunities in data and those who perceive data as a threat. Various organizations are expected to take the lead in implementing advanced visualization solutions to automate the creation of actionable insights. By 2025, data visualization would be dominated by augmented analytics and IOT, with business actions being driven and made frictionless by artificial intelligence.