Big data visualization is the process of displaying data in charts, graphs, maps, and other visual forms.
It is used to help people easily understand and interpret their data at a glance, and to clearly show trends and patterns that arise from this data.
Big data visualization not only makes understanding and interpretation of data faster and easier, it’s also a way of identifying and highlighting observations that might not be as noticeable when viewing a list of numbers and values.
Furthermore, raw data often comes in a variety of formats, so creating data visualizations is a vital party of an intensive process of gathering, managing, and transforming data into a format that’s most usable and meaningful.
Big Data Visualization makes your data as accessible as possible to everyone within your organization, whether they have technical data skills or not.
The best data visualizations are those that clearly communicate an idea and simplify complex data, and they can be used by as many people as possible.
Why big data visualization is essential for your organization.
We live in a data-driven world. Organizations nowadays generate and gather more data, faster than ever before and we consume data at a breathtaking rate. Plus, data comes in a wider variety of formats — both structured and unstructured.
So, the volume of data and its multiplicity of forms require better clarification in order to extract the best insights, make them as accessible as possible, and add the most value to your organization.
The way to achieve this is to use data visualization techniques to make the most sense of all your data and bring it to life for the largest number of people who can use it, understand it, and interpret it.
What big data visualization can do for you
Big data visualization ensures that you get the most value from your data for your organization, your customers, and your partners. It’s something that more and more business leaders consider a vital part of their BI and analytics. Big data visualization helps you:
As data has proliferated, so have the ways of visualizing data. The best ways let you get the most out of Big Data from the greatest variety of sources. With every dataset, you need to decide which kind of visualization is most effective for illustrating what the data reveals. It’s therefore important that your BI and analytics platform can handle the most useful ways to visualize data, such as:
Simple “gauge indicator” visualisations show you immediately whether you’re above or below target, and whether you’re moving in the right direction..
Line charts are great for showing the relationships between data points. Often, they’re used to show changes and trends or to compare multiple components over a certain period of time.
Bar charts are used to compare the quantities of different categories or items. The length or height of each bar shows a value of each individual category or item.
Pie and donut charts are split into segments that represent numerical values. The angle of each segment corresponds to the illustrated value. Using a chart of this kind, you can see how different parts of a whole compare to each other.
Area charts are useful because they give a sense of the overall volume, as well as the proportion of this taken up by each category on a chart. They work well when you’re looking for insights into resource and financial planning and allocation.
Pivot tables combine a graphical element in a table with key numbers, to provide you with an instant visual reference to compare entries, alongside exact figures. They’re useful if you want to compare components of one category.
This type of data visualization shows markers, such as dots, squares, or pluses, and shows the variation of two data items on X and Y axes. Each marker’s position shows the value of the item that marker represents. Scatter graphs are useful when you have numerous data points, and you want to see how they correlate and how closely they are related, based on how close or spread out they are from each other.
Bubble charts work a lot like scatter charts, but the markers in bubble charts are represented by bubbles, which add to the data shown by representing the size of individual items or categories in relation to others, as well as their value shown on the X-Y axes. So, this type of data visualization shows the between at least three measures, with two measures being represented by X-Y axes, and the third measure being the bubble size.
Heat maps put data points in a familiar layout, like a world map or a football field, and they show value using different colors or varying intensity of the same color. They’re useful when you have one data category with a wide value range, because they make it easy to see overall and specific trends and identify weak points, and pinpoint opportunities.
Treemaps put color-coded rectangles next to and inside each other, weighted to reflect their share of the whole. You can use treemaps to compare value between and within categories, while retaining a sense of which are more important.
Otherwise known as summary resource grade charts, these are like pie charts, but instead of showing each value’s share of the whole by the size of the angle, all the sectors have equal angles, and the value is shown by the how far it reaches from the center of the circle.
Funnel charts show the decreasing values as customers move through the sales funnel. You can easily see your conversion rates at each step, so you can identify quickly where you are losing people in the process.
There are a host of tools that can help you create stunning data visualizations. Choosing graphical tools depends on numerous considerations, the type of project, and the software used by your data science teams.
Most effective of all is to use a BI and analytics platform like Sisense that works with a whole range of these tools to help bring your data to life, reveal insights, and even predict trends that can revolutionize your organization.
Sisense enables you to connect with your data, wherever it may be, using a wide array of connectors with which you can power your dashboards, and join all of your present and future data to quickly transform it into information and insights. Some key examples, among many others, are as follows:
R is a free programming language and software environment for statistical computing and graphics.
Python is a free, open-source programming language, which has several libraries for data visualization, such as Matplotlib, Seaborn, Ggplot (sourced from R) and Bokeh with interactive elements and zoom.
Neo4j is an open-source, NoSQL, native graph database implemented in Java and Scala, mainly used in graphics algorithms, applied to Data Science, to build oriented databases for predictive and recommendation models.
A wide range of other tools and graphics libraries are available for generating graphics such as Matplotlib, a comprehensive library for creating static, animated, and interactive visualizations in Python; and SalesForce Einstein, the AI technology developed for Salesforce, that aims to give sales and marketing departments more comprehensive and up-to-date views of customers and sales prospects.
While companies and organizations share many features and requirements, each has its own specific characteristics and needs. So, when you’re considering which BI and analytics platform enables big data visualization that best meets your requirements, bear in mind the following considerations:
The amount of data you need to manage, and the number of connected devices generating data, is rapidly growing all the time, so it’s vital that the big data visualization capability in your BI and analytics platform has the capacity not only to handle your current data, but a massive increase in data that you can expect in the future.
The more data there is, the more formats it can have. Make sure that your data monetization supports a wide range of formats. In addition to basic file types like Excel, delimited text files, XML, JSON, EBCDIC and the like, ensure that it can handle other common formats like SQL Server, Sybase, Oracle and DB2. And it’s increasingly important that your choice of big data visualization tool can also handle enterprise software like SAP, SAS, Marketo and others, plus data from cloud services and the leading cloud-based data warehouses.
As your organization grows and changes, you should be sure that you can scale up your big data visualization capability and that it can grow with you. It’s important that your chosen platform has the power and technology for scaling up, in terms of including functions in your BI and analytics platform should also be adept at coping with other changes that your organization might experience to meet changing demands quickly. From granular customization of functionality to adapting to new requirements and driving shorter times-to-insights, it should ensure performance regardless of the challenges ahead.
Ideally, your choice should precisely meet your specific requirements, so consider how well your choice of platform architecture is designed with custom tailoring in mind. With the right platform in place, customization will be simplified and will no longer be a daunting prospect, and even the most in-depth changes can be handled with ease. So, make sure that the platform you choose is built with customization in mind; is purpose-built for developers so they can reduce development time; and can embed analytics everywhere so that you can integrate analytics into your products and services. Sisense’s technology can be used for terabyte scale data and analytics, and to serve multiple users concurrently. We harness the true processing potential of today’s 64-bit computer, multi-core CPUs and parallelization capabilities, enabling a single commodity server to deliver the same data processing power as much larger clusters.
To maximize the value that you get from your data and your BI platform, it should empower more users across your organization to analyze, visualize and act on business data. Can it automate repetitive tasks? Does it offer coding-free drag and drop functionality that enables non-technical users to transform data easily? Plus, your platform should enable AI throughout so that it learns from your users and data to solve problems intuitively.
See how the leading vendors of data monetization tools and analytics stack up in the most respected industry analyst reports:
Value delivered, early innovation, and customer success are what make us the leading Visionary in the Magic Quadrant.
Sisense earned top rankings from customers for ease of use, ease of doing business with and quality of support.
“Overall Leader” for customer experience and credibility with high scores for value & integrity put us consistently above the rest.
There’s a lot to consider when choosing a data monetization tool, but a lot to be gained by making the right decision. So, in addition to the overview you’ve seen so far, look at these comparisons between Sisense and the other leading data monetization tools, to see why you should choose Sisense:
Developers, cloud data teams, and analysts from some of the world’s leading companies and global enterprises use the power of Sisense to effortlessly combine complex data from a variety of sources and build analytics apps that deliver insights to everyone in the organization.
These power users, plus business users from a wide range of functions, are what we identify as the builders of your strategy. Sisense gives them the power to identify, analyze, and visualize the data that influence the course of your organization, with powerful decision-making capabilities that are potentially game-changing.
The Sisense BI and analytics platform dramatically accelerates the time it takes to build, infuse, and deploy intelligent analytics apps that unleash user creativity and engagement. Whether it’s interactive dashboards, self-service analytics, or white-labeled BI apps, Sisense delivers the industry’s lowest TCO at scale, all on a hybrid-cloud platform designed to leverage all your data together, and infuse analytics everywhere, no matter where it is.
Sisense empowers your users to make decisions and challenge assumptions by equipping them with the insights they need, when and where they need them. We help everyone in your organization drive change and build the business using innovations in AI and ML that deliver BI with more impact than you ever thought possible.
It’s not just what we do, it’s how we do it. It takes more than patented technology to make your business successful. You need the product to work in your network, with your requirements, under your constraints. And that’s when our customer-centric culture kicks in. We work with you on every installation, every upgrade, and every project to make sure you feel the value of your BI platform in your business. Sisense scores consistently above the overall sample and is the leader in terms of customer experience and vendor credibility.