Mobile data analysis and visualization app developer Roambi today announced Analytic 7, a new version of its product for Apple iPads, iPhones, and iPod Touch devices, with a redesigned UI for iOS7 and a new user-configurable visualization template providing a user-configurable dashboard.
The new more minimalist UI is a complete redesign for iOS7 and follows Apple’s lead with a flat look, smaller icons, and few graphic touches. It also sometimes seems to respond faster than the older version, although Roambi Co-Founder and President of Product Innovation Quinton Alsbury says that is an optical illusion –actually performance between the versions is nearly identical.
The new version is the product of several months of intense development, starting when Apple announced iOS7 to app developers. After using it, Alsbury says, “The original version looks a little old fashioned.”
The new visualization template, Roambi’s 11th, gives users more flexibility in combining and organizing different visualizations to create a graphics dashboard for their iPad or iPhone.
Designed for Mobile
While most other new-generation end-user data visualization products, such as pioneer Tableau, are designed for use by data analysts on laptops and desktops, Roambi was inspired by the first iPhone and designed from the start for mobile use. “The idea for the product was very simple. It was to create a new presentation app for data, oriented and designed exclusively and explicitly for use on mobile by non-data analysts who need data visualization as part of their jobs,” Alsbury says.
Almost from the start, it found a home in large corporations, which surprised the founders. These companies equip large groups of end-users, including field sales forces and whole marketing departments as well as operational personnel such as store managers, with Roambi. This is a very different audience from Tableau users, who are mostly intense users of data analysis and visualization working at their desks all day. A large percentage of Roambi’s users spend a great deal of their time away from their desks, either on store sales or factory production floors, for instance, or in business travel and meetings, or in the field meeting with customers. They are primarily consumers of data analysis rather than analysts themselves.
These different paradigms drive a very different product design. The most obvious difference is the interface, which is fully multi-touch rather than keyboard/mouse, point-and-click.
The largest difference, however, is that while Tableau and similar products are basically complex toolkits for full-time professional data analysts, Roambi is designed specifically for users who value ease-of-use and simplicity. They “want to pull their iPhone out of their pocket and see something in a few screen taps,” Alsbury says. A pharma rep walking in to meet with the doctor is having a fundamentally different interaction with data than a Tableau data analyst at a desk pouring over a dashboard doing ad hoc queries of Big Data.
As a result, Roambi provides users with 11 pre-designed templates. The user simply chooses a template and points it at the data using multi-touch commands and gestures rather than PC tools, and the software does the heavy lifting.
PC-based tools also presume a persistent high-speed Internet connection to background servers that provide data and compute power. Roambi does not. “If you are going to be mobile you are going to be using your device on airplanes, in restaurants, and other places where you won’t have a connection,” says Alsbury. “This is particularly true because the tablets companies buy for employees usually do not have a celluar chip because the cost of data connections is too great.” Therefore Roambi is designed to work off-line, downloading and storing the data as well as the full end-user application on the iPad or iPhone.
Finally, he said, PCs are basically work machines, and users have a certain set of expectations when using them. They have a different set of expectations for the experience provided by the apps on their tablets and phones. Roambi is designed to meet those expectations.
Strengths and Weaknesses
As a data analysis and visualization tool Roambi has several imitations. These include:
- Platform: It runs only on iOS devices, so users of Android, RIM, and Windows mobile devices are out of luck. This will limit its usefulness in the increasing number of BYOB businesses to those employees who carry Apple devices.
- Data Size: Because it must have its data stored locally on the device, the amount of data is limited to the available storage space on the user’s iPad or iPhone. However, a surprising amount of alpha-numeric data can fit into 1 GB of memory.
- Streaming Data Limitation: Because Roambi is designed to work without an active Internet or internal company network connection, it can only work with data “at rest”, not with streaming data such as Tweet streams.
- Visualization limitations: It provides 11 pre-packaged data visualization templates. Users have very little ability to customize those templates. It does not, for instance, analyze complex webs of relationships among individuals based on social media data.
- Design for Mobility: Because it was inspired by the first iPhone and designed from the ground up for that platform, it takes advantage of all the features of the revolutionary iOS UI.
- Portability: Because it is designed to run completely on the device, it can be used in places where network connectivity is unavailable, including in customers’ offices, outside, in many restaurants, and on airplanes.
- Fast Response Times: Users can create a completely new data visualization based on new analysis literally in seconds. This can be very useful in face-to-face meetings with customers, for instance.
- Glitz: Roambi is designed to look and feel like mobile consumer app. As a result it looks and feels almost like a game, making it much more accessible to a larger population of users, including those with no training or experience in data analysis tools.
Latest posts by Bert Latamore (see all)
- How the cloud and IBM Watson are helping breed a better guide dog - May 3, 2016
- OpenStack Summit on #theCUBE: A window into the future - April 25, 2016
- Hadoop World: Big questions about Big Data’s future #HS16Dublin - April 22, 2016