Tuesday Jul 30, 2013

Key User Experience Design Principles for working with Big Data

By John Fuller, Consulting User Experience Designer, Oracle

Editor’s Note: This is part 2  in a three-part blog series on the user experiences of working with big data. In my last blog on this topic, I summarized the conversation from a one-day summit with a few key partners on the user experience landscape with big data.  In this blog, John Fuller, full time interaction designer for Endeca, shares some of his team’s key requirements for designing usability into the user interfaces for Endeca Information Discovery.

John Fuller
John Fuller, Consulting User Experience Designer for Endeca

About two years ago, we took look at the product we had and felt that there was a lot of opportunity that was, in many ways, fairly unique in the marketplace. It was at that point that we developed a set of core design principles to guide us in our work going forward.

We crystalized the things we thought were working well and sought to maintain that focus going forward. I wouldn't say that they were designed specifically with "big data" as the main focus; the principles are much more broadly applicable. We're focusing on helping people bring together a variety of data types in a fast and flexible way with lower cost, so from that perspective, we're targeting a really interesting part of the big data story.


Endeca healthcare demo
Screen shot from an Oracle Endeca Healthcare Demo showing how big data can guide the detection of healthcare problems.

We came up with 6 core design principles and details about what each one meant. One of the really interesting outcomes of this has been that the principles have really held up over time.

Here are the six core principles:
  1. Enhance Insight - The value of discovery tools lies in the insights they help discovery workers realize, by enhancing the natural ability of people to understand the answers that are in the data.
  2. Encourage Exploration - Discovery applications encourage exploration.  Users will want to ask new questions, pursue new avenues of exploration, and consider new connections and relationships across the diverse types of information presented by discovery applications.
  3. Coherence and Clarity - All elements of the experience should work together in a coordinated fashion. The way the system works is clear at all levels, making the results and implications of actions easy to understand.
  4. Readily Composable and Manageable - Creating, configuring, and managing discovery applications is straightforward and efficient. The product provides useful defaults, intelligent starting points, and encourages application builders to make good choices when composing discovery applications.
  5. Engaging and Compelling - Working with the tool is enjoyable, engaging, and satisfying, for new and veteran users. Endeca Information Discovery embodies and personifies the values and principles identified herein.
  6. Offer a Modern Application Experience - Discovery solutions “walk and “talk” like modern applications.
With every new project that comes up, the principles still seem new and fresh -- with new takeaways to guide the process. We're planning on adding more detail about the principles -- and several other topics -- on our blog, so if you'd like to hear more, check it out.


Tuesday Jul 23, 2013

The User Experience of Big Data in Oracle Enterprise Applications: Part 1 of 3

By Misha Vaughan, Oracle Applications User Experience

Editor’s Note: This is the first part of a three-part series on lessons we have learned about the user experience of big data, and trends in Oracle’s approach to the challenges of working with big data.

Misha Vaughan
Misha Vaughan, Director, Communications & Outreach, Applications User Expeirence
by Martin Taylor

I recently hosted a partner summit on the user experiences of big data at Oracle headquarters in Redwood Shores, Calif. The title of the summit was: “So You Have Big Data, Now What?”

The goals of the exchange were three-fold:
  • Assess where some key Oracle user experience partners -- Floyd Teter of EiS Technologies (@fteter), Edward Roske of interRel (@eroske), Mike Rulf of Core Services, and Ron Batra of AT&T (@ronbatra)-- were at in their conversations around the user experience needs of big data with their customers.
  • Discuss and sharpen our common understanding of the UX value propositions of some Oracle applications for big data. My particular interest was with OBIEE’s new information visualizations and Endeca Information Discovery’s UX.
  • Get feedback on a selection of forward-looking applications user experience innovation projects that intersect with big data. 
Below are my lessons learned from the conversation. Part 2, the next post in this series, is an email conversation with John Fuller, User Experience Designer for Endeca, on the key elements of designing user experiences for data analysts working with big data tools. Part 3 is a summary of what I see as the key UX design principles emerging in Oracle for a new class of design problems - making big data accessible to non-data analysts.

My Lessons Learned

Lesson 1: What customers are asking about “big data” and how they defining “big data”.

The general consensus was that some customers have already defined their strategy and are moving forward.  However, many customers are still trying to wrap their heads around what big data means for their institutions.  Our key partners see their customers’ understandings ranging across the following:
•    Big data is a massively large volume of structured data.
•    Big data is making sense of unstructured data, like Twitter feeds and Google search results (e.g., monitoring potential flu outbreaks).
•    Big data is about consolidating multiple sources of data, structured and unstructured, into one representation.
•    Big data is about solving wicked problems, for example, how to optimize something as complex as thinning a forest against needed output, aesthetics, and uncertain markets.
•    It is about discovering unlikely relationships in a large volume of data.

Lesson 2:  The big-data analyst is a highly specialized user role, and really needs the right user experience to be able to deliver the results companies are looking for.

Companies like Oracle are building the tools necessary for data analysts, such as Endeca's Information Discovery Tool.  Color me "wow" after seeing a demo by John Fuller.  Important tools in the toolkit are also OBIEE's "big data" visual analysis tools (thank you, Edward Roske).

This was a jam-packed conversation, and had so much in it that I decided to follow up with John and see if he would unpack the user experience requirements in more detail in a follow-up post. So stay tuned for that.

Lesson 3: It seems that there are really two user profiles we need to be concerned with in big data: the data analyst and the downstream producer, or possibly business analyst.

A recent study in the Wall Street Journal states that one of the biggest challenges of big data is finding professionals actually trained in the domain to help companies take advantage of this space. We know that the big business schools with IT programs will take the bait, but even that will not produce them fast enough. The rate of information is growing faster than our ability to sift it.

To take advantage of the sizeable investment required for a Big Data Project, a data analyst needs to enable a larger set of producers to leverage their data and share it with a larger audience. This may be a business analyst, or some other job title - but essentially this is a person who works with a lead data analyst to create the stories, visualizations, and associated analyses needed to communicate findings to a larger audience, which allows that lead analyst to get onto the next problem.

In my next post, I’ll write about Endeca, and the key elements of designing user experiences for data analysts working with big data tools.





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Misha Vaughan
Misha Vaughan, Director, Applications User Experience
@mishavaughan on Twitter

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