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Doug Henschen
Doug Henschen
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Analytics Showdown: Should Apps Be Simpler, Or Smarter?

Qlik, Tableau, and are betting on simple analytic applications, while veterans IBM and SAS push smarter. What do you need?

Download the complete 2015 survey.

The latest software releases from IBM, Qlik, Salesforce, SAS, and Tableau Software demonstrate different approaches to making data analysis more accessible. Think of the showdown as simpler versus smarter. Which strategy will win?

With both approaches, vendors of analytics and business intelligence software are injecting new energy into solving decades-old complaints about the complexity of this type of software.

We've been polling technology professionals on this topic since 2010, and the 2015 edition of our InformationWeek Analytics, Business Intelligence, and Information Management Survey once again finds that "ease-of-use challenges with complex software or less technically savvy employees" is the second most-cited barrier to success in data analysis (see chart below). For the third year in a row, only data quality is cited more often as an obstacle.

So what are vendors trying to do about the complexity of their software?

[From cloud dominance to mobile-first, here are the trends we saw in enterprise apps in 2014: Top 5 Enterprise Application Trends Of 2014.]

"There's a school that believes that if you can make the software smart enough, it can do the analysis," says Anthony Deighton, CTO at Qlik. "Another school thinks that if you make the tools easy to use, people can handle even sophisticated analyses."

Qlik falls into the latter, "simpler" group, says Deighton, and you can put and Tableau in that camp as well. Qlik and Tableau have been the fastest-growing vendors in the analytics and BI field in recent years, and both vendors (and their customers) attribute their success to simplicity and ease of use.

Qlik's QlikView software provides an in-memory data-discovery environment, while Tableau is known for its data-visualization software. Big BI incumbents such as IBM Cognos, MicroStrategy, Oracle, SAP, and others have all validated the Qlik and Tableau approaches by adding self-service data-discovery and data-visualization modules of their own.

Now Qlik and Tableau are upping the ante with even simpler tools -- Qlik Sense and Tableau Elastic -- aimed at a still broader base of users. QlikView is used first by analysts and power users who build data-discovery apps that less tech-savvy users can use to answer data-driven questions. Qlik Sense, the vendor's new application, is geared to self-service data visualization without the aid of analysts or power users.

"The problem with many data visualization tools is that they still have an analyst-centric model," says Deighton, meaning they require tech-savvy data analysts or business analysts to set things up. "Our focus with Qlik Sense has been to make the tools so easy to use, people can discover the insights for themselves."

Tableau goes mobile
Tableau, one of Qlik's biggest rivals, acknowledges that its Tableau Desktop software tends to be used by more data-savvy users, but the reports that those users publish on Tableau Server or Tableau Online can be "used by almost anyone," says Ellie Fields, Tableau's VP of product marketing. Using filters and other interactive controls, people ranging from salespeople and doctors to teachers and "other folks you wouldn't consider to be data analysts" are working with reports on Tableau Server and Tableau Online, Fields says.

Tableau's attempt to reach a broader base of users is Tableau Elastic, but it's not a dumbed-down version of Tableau Desktop. Elastic is a tablet app designed for a particular use-case -- such as when somebody emails you a spreadsheet, but you're not at your desk and wouldn't want to fire up a laptop. "If you're on the go you may not have much time, but you want to do better than reading columns and rows," Fields says. "With data visualization, you can absorb information in seconds that's not readily apparent looking at a spreadsheet."

Tableau isn't trying to replace Microsoft Excel, the most broadly used data analysis tool in the world, but it does see an opportunity for a tablet app that provides a visual view of the data stored in spreadsheets.

Tableau Software's Project Elastic is a beta app for tablets designed for mobile data analysis. Emailed spreadsheets become data visualizations.
Tableau Software's Project Elastic is a beta app for tablets designed for mobile data analysis. Emailed spreadsheets become data visualizations. shoots for simple
The biggest news in BI and analytics this year has been's Wave Analytics Cloud, which the company has out in limited release. It's big news because Salesforce has more than 100,000 corporate customers and roughly 25 million users, stats that few BI/analytics vendors can match. In contrast to the biggest players in this arena, such as IBM, Oracle, and SAP -- all of which have broad and mostly traditional (and IT-support-centric) BI suites -- Salesforce is promising a simple, mobile-first approach.

"The fact that there are only four buttons on the Wave mobile app shows that we want to reach every user," says Keith Bigelow, a BusinessObjects veteran who is now GM and senior VP of big data and analytics at Salesforce. "Our customers already have the competitors' products, but those products weren't designed to be used by every salesperson. We want our app to be viral, and that's not the typical approach."

Wave beta customer GE Capital, for instance, had lots of BI tools to choose from, Bigelow says, "but they were not able to build an app that was consumable by a salesperson."

Usability for all is in's bottom-line interest. "The single metric

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that SaaS vendors care about more than anything is adoption," says Bigelow. "It's a monthly subscription, and if you don't have usage, you don't have revenue."

However, as simple and usable as the mobile apps may be on the front-end, getting real value out of Wave Analytics will still require backend data integration work and developer/power user decisions about what data to combine and expose to end-users.

School of smarts
Among the vendors I would put in the "tools must be smarter" school of thought are IBM and SAS. IBM recently introduced Watson Analytics, a cloud-based service that promises to streamline high-end data analysis. Currently in beta and available (by invite only) after registering online, Watson Analytics makes heavy use of natural-language interaction. You start by uploading files, and you can then explore the data using visual tools. Alternatively you can type questions into an Internet search-style window, or apply use-case templates such as "Improve Campaign Effectiveness" or "Prevent Employee Attrition."

Despite the Watson branding, the service is not the same as IBM's cognitive computing technology, so you won't have to go through laborious training. Behind the scenes, the service combines technologies that IBM has been working on for some time. InfoSphere Data Refinery services, for example, are used to assess the data that you upload and automatically suggest corrections, such as deleting duplicates or merging records that appear to be related. The suggestions are presented in plain English, and users can accept or decline the changes.

Other IBM R&D at work includes Catalyst Engine, a tool that comes out of IBM's SPSS acquisition that's designed to automatically find correlations and trends in data. Watson Analytics also uses technologies from an IBM Labs effort called Project Neo that automatically suggest which type of visualization to use to best illustrate a trend, outlier, correlation, or other trait.

It's not as if IBM isn't thinking about making analysis simpler, since that is what all these natural language-interaction and auto-suggestion features deliver. But given the breadth of this online service, with multiple data-loading and data-cleansing options followed by multiple data analysis options and use-case scenarios, it feels more like a data analyst-oriented studio or suite rather than a simple data visualization tool.

Task descriptions within Watson Analytics are simple and straightforward. Multiple video tutorials hint that this broad studio isn't stupid simple.
Task descriptions within Watson Analytics are simple and straightforward. Multiple video tutorials hint that this broad studio isn't stupid simple.

SAS Visual Analytics is another broad option with built-in smarts. Introduced in 2012, this on-premises software also spots trends and patterns of interest within data sets and then suggests the types of visualizations that best expose those traits. In a Visual Analytics upgrade announced in October, SAS added "guided analytics" whereby users can define business goals, such as unit sales or profitability, and then see how other variables, such as marketing spend, material costs, or other inputs, will have to change in order to meet that goal.

This goal-seeking approach provides a new twist on widely available what-if analysis capabilities. Rather than wasting time manipulating data variables and then looking at the impact, you simply input the business outcome you're after -- like 25%-plus profit margin -- and the software shows you what conditions or combinations of conditions it will take to reach that goal.

Which approach wins?
Everyone wants software that's both simple and smart, no matter how tech-savvy they might be. IBM and SAS of course both argue that their smart features make their services and software simpler and easier to use, even for novices.

I put this idea of simplicity versus smarts in front of one super-experienced BI and analytics practitioner: Steve Rimar, senior staff IT architect at Qualcomm. The company has used a variety of different tools over the years, including Cognos, Microsoft, and BusinessObjects, but QlikView and Oracle OBIEE have emerged as Qualcomm's dominant corporate standards.

Qualcomm recently decided it needed something even simpler than QlikView to meet the needs of human resources and business executives. After an extensive review that also included Tableau Software, Qualcomm chose Qlik Sense for that role, and it's in the midst of big deployment. Familiarity with Qlik and the opportunity to swap and balance QlikView and Qlik Sense licenses had a lot to do with the selection, Rimar admits. But Qualcomm is keeping its options open and it's also doing a proof-of-concept project on Watson Analytics.

What's Rimar's take on software simplicity versus smarts?

"I always tell people that the future of BI is Google; it's a text box where you put questions in and get answers," Rimar says. "But today, no tool is smart enough to just tell you the right answers. You have to minimize potential errors by having power users pull the right data together so self-service business, finance, and sales users -- or whoever they may be -- can draw conclusions from clean data sets."

The future will undoubtedly bring more new products that combine simple, intuitive interfaces with built-in auto-suggestion features. Simplicity and software smarts both help, but for the foreseeable future, human expertise still will be required to put the right data together and then explore and visualize it to come up with valid conclusions.

Apply now for the 2015 InformationWeek Elite 100, which recognizes the most innovative users of technology to advance a company's business goals. Winners will be recognized at the InformationWeek Conference, April 27-28, 2015, at the Mandalay Bay in Las Vegas. Application period ends Jan. 16, 2015.

Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of ... View Full Bio
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