By Misha Vaughan, Oracle Applications User Experience
Editor’s Note: This is part 3 in a three-part series on the user experiences of working with big data. In the last post on this topic, John Fuller, Consulting User Experience Designer for Endeca, wrote about some of his team’s key requirements for designing usability into the user interfaces for Endeca Information Discovery. In this post, the emerging thinking on design principles for delivering all this power to regular end users is the topic. Thank you to peers John Fuller, Julia Blyumen, Edward Roske (@eroske), and Aylin Uysal for the inspiration of these themes.
Information visualization is a whole field unto itself, and education is now widely available on this topic, notably Edward Tufte’s work on Information Visualization.
When information visualization was discussed at a recent summit on user experience for big data summit, a specific new insight for me was that I saw a set of information visualization guidelines emerging for end users. I don’t mean data analysts or business analysts who are doing deep, big data analysis. I mean the end user, for whom the analyst is preparing data.
How do you present big data to an executive or a decision-maker in a way that is digestible? How do you take them from the big picture insight, down into the supporting details? Do you show them the trellis charts and say “see here?” Or do you take a more narrative approach?
In no particular order, these were my lessons learned about end user design principles for big data visualizations:
1. Make the invisible visible.
The entry into a big data analysis can be through seemingly simple information visualizations. Take a strategy from the newspaper industry’s use of infographics, such as the Huffington Post or USA Today. Through visualization, you can help the user better connect and interact with the data. Information visualization and infographics are a core part of making the results of big data accessible.
2. Show the forest, then the trees. This is also known as progressive disclosure.
With more and more data available in larger amounts, end users now need, more than ever, attention to how to cleverly and conveniently discover what they need to know. Then they need to be given the ability to explore that data.
3. It’s all about me, or staying in context of my task.
Making big data relevant to end users means considering how to display large quantities of data in the context of different enterprise use cases, such as human resources processes, financial processes, or sales processes. This can be any kind of data, whether it's pulling in transactional data, analytics, or social feeds.
4. Tell me a story.
Big data is, well, a lot of data. Providing narrative sources can add context and clarity to complex data. Doing this in a systematized way has even more interesting implications for enterprise use cases.
5. Make it mobile.
This one is kind of a no-brainer. This is about giving end users the ability to make this kind of data available on tablet-sized devices.
6. I can trust this, by you showing me how you got here.
Because of the complexity of the data, and the possible multiplicity of data sources, the ability to create confidence in the quality and the timeliness of the data are key to the experience. It also means showing the path or way an analyst arrived at a particular conclusion.
7. Make it fun to play with.
One of the delightful characteristics of big data is that there really is a lot of data you can play with. There is a sweet spot for the developer or designer who invents clever components that allow for the creative display and manipulation of complex levels of data.
8. One UI to rule them all.
End users don’t really care how many data sources you are bringing together. They just want the result. The best experiences will unify many data sources, transparently -- whether it’s Endeca, a data warehouse, or social feeds -- into one representation.
Again, I can’t claim credit for the concepts. I’m just summarizing what I learned on that day. If you want to see what this all means for Oracle Applications User Experiences, stay tuned and see what’s coming at OpenWorld 2013 this year.
Editor’s Note: This is part 3 in a three-part series on the user experiences of working with big data. In the last post on this topic, John Fuller, Consulting User Experience Designer for Endeca, wrote about some of his team’s key requirements for designing usability into the user interfaces for Endeca Information Discovery. In this post, the emerging thinking on design principles for delivering all this power to regular end users is the topic. Thank you to peers John Fuller, Julia Blyumen, Edward Roske (@eroske), and Aylin Uysal for the inspiration of these themes.
Information visualization is a whole field unto itself, and education is now widely available on this topic, notably Edward Tufte’s work on Information Visualization.
When information visualization was discussed at a recent summit on user experience for big data summit, a specific new insight for me was that I saw a set of information visualization guidelines emerging for end users. I don’t mean data analysts or business analysts who are doing deep, big data analysis. I mean the end user, for whom the analyst is preparing data.
How do you present big data to an executive or a decision-maker in a way that is digestible? How do you take them from the big picture insight, down into the supporting details? Do you show them the trellis charts and say “see here?” Or do you take a more narrative approach?
In no particular order, these were my lessons learned about end user design principles for big data visualizations:
1. Make the invisible visible.
The entry into a big data analysis can be through seemingly simple information visualizations. Take a strategy from the newspaper industry’s use of infographics, such as the Huffington Post or USA Today. Through visualization, you can help the user better connect and interact with the data. Information visualization and infographics are a core part of making the results of big data accessible.
2. Show the forest, then the trees. This is also known as progressive disclosure.
With more and more data available in larger amounts, end users now need, more than ever, attention to how to cleverly and conveniently discover what they need to know. Then they need to be given the ability to explore that data.
3. It’s all about me, or staying in context of my task.
Making big data relevant to end users means considering how to display large quantities of data in the context of different enterprise use cases, such as human resources processes, financial processes, or sales processes. This can be any kind of data, whether it's pulling in transactional data, analytics, or social feeds.
4. Tell me a story.
Big data is, well, a lot of data. Providing narrative sources can add context and clarity to complex data. Doing this in a systematized way has even more interesting implications for enterprise use cases.
5. Make it mobile.
This one is kind of a no-brainer. This is about giving end users the ability to make this kind of data available on tablet-sized devices.
6. I can trust this, by you showing me how you got here.
Because of the complexity of the data, and the possible multiplicity of data sources, the ability to create confidence in the quality and the timeliness of the data are key to the experience. It also means showing the path or way an analyst arrived at a particular conclusion.
7. Make it fun to play with.
One of the delightful characteristics of big data is that there really is a lot of data you can play with. There is a sweet spot for the developer or designer who invents clever components that allow for the creative display and manipulation of complex levels of data.
8. One UI to rule them all.
End users don’t really care how many data sources you are bringing together. They just want the result. The best experiences will unify many data sources, transparently -- whether it’s Endeca, a data warehouse, or social feeds -- into one representation.
Again, I can’t claim credit for the concepts. I’m just summarizing what I learned on that day. If you want to see what this all means for Oracle Applications User Experiences, stay tuned and see what’s coming at OpenWorld 2013 this year.