The User Experience of Big Data in Oracle Enterprise Applications: Part 1 of 3
By mvaughan on Jul 23, 2013
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, 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.
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.