A picture is worth a thousand words – especially when you are trying to find relationships and understand your data – which could include thousands or even millions of variables. To create meaningful visuals of your data, there are some basic tips and techniques you should consider. Data size and composition play an important role when selecting graphs to represent your data. This paper, filled with graphics and explanations, discusses some of the basic issues concerning data visualization and provides suggestions for addressing those issues. From there, it moves on to the topic of big data and discusses those challenges and potential solutions as well. It also includes a section on SAS® Visual Analytics, software that was created especially for quickly visualizing very large amounts of data. Autocharting and "what does it mean" balloons can help even novice users create and interact with graphics that can help them understand and derive the most value from their data.
If you are working with massive amounts of data, one challenge
is how to display results of data exploration and analysis in a
way that is not overwhelming. You may need a new way to look
at the data – one that collapses and condenses the results in an
intuitive fashion but still displays graphs and charts that decision
makers are accustomed to seeing. And, in today’s on-the-go
society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time.
SAS® Visual Analytics is a data visualization and business
intelligence solution that uses intelligent autocharting to help
business analysts and nontechnical users visualize data. It
creates the best possible visual based on the data that is
selected. The visualizations make it easy to see patterns and
trends and identify opportunities for further analysis.
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
Published By: Workday
Published Date: May 09, 2018
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"Today’s business users want to use all types of data to create compelling, shareable visualizations. But charts and graphs alone may not convey all the information, especially when they are part of a complex series. An audience can best understand analytic results when those results tell a story that connects all the pieces together. The right visuals can also reinforce the lessons buried in the data.
Stories are powerful mechanism to communicate with people. Stories stick and make insights actionable, so it goes without saying that storytelling is a very powerful (soft) skill. In this webinar, you'll learn how to effectively apply storytelling best practices to get your message across. Especially in the world of BI, it is getting more and more important to effectively communicate business results.
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Adobe Audience Manager uses identity management tools and device graphs to tie device IDs to individuals or groups. If a customer starts using a device you don’t recognize, you can use second-party data available through a network or co-op to supplement your own, building a complete view of your customer. This allows you to send consistent messaging across devices, whether your customers log in to your site or not.
This paper looks at research into the link between visual perception and understanding, and translates the findings into practical techniques that you can use to communicate more clearly with your data.
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Managing expectations before, during and after the adoption of visualization software is crucial. Users should know what the rollout process will look like and how it will take place, and have clear goals for using the tool. Make sure that the desired outcome isn’t just look-and-feel. Creating beautiful charts and graphs is not a substitute for practical business decisions.
With the emergence of people-based marketing, an identity graph is the foundation required to recognize people across channels and devices at every step of the customer journey to deliver true 1:1 marketing at scale.
HubSpot has compiled over 50 marketing charts and graphs based on original research and data from a variety of sources, including analysis of over 2,500 business customers, surveys with hundreds of businesses responding, and analysis of the data in from the HubSpot Grader tools.
If you are working with massive amounts of data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data – one that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. And, in today’s on-the-go society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time.
SAS® Visual Analytics is a data visualization and business intelligence solution that uses intelligent autocharting to help business analysts and nontechnical users visualize data. It creates the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis.
The heart and soul of SAS Visual Analytics is the SAS® LASR™ Analytic Server, which ca
Get started by creating the best type of chart for your data and questions. From there, you’ll quickly find you’re not only answering your initial questions, but telling amazing stories with your data.
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