Business intelligence (BI) vendors and industry analysts have been talking about pervasive BI – i.e., "BI for the...
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masses" – for years now. But, by most accounts, BI has yet to break through to the desktops or BlackBerrys of most marketing managers, salespeople, shop floor directors and other business users.
A recent survey of BI end users and managers by the U.K.-based Business Application Research Center revealed that only 11% of respondents have BI deployed to more than 50% of employees in their companies.
Many factors contribute to the lack of business user adoption, but an important one is the technology itself. BI vendors are constantly touting innovations that will bring BI to the masses, but so far to little effect.
There is hope, however. Here are three technologies that could play critical roles in spreading BI to more business users.
1. Data visualization. Perhaps the most sure-fire way to spur business user adoption of BI is to improve data visualization technology. The easier it is for non-analysts to view and make sense of dashboards and other data visualizations, the more likely they are to use BI technology.
A handful of vendors, both large enterprise software companies and smaller data visualization specialists, have come up with enhancements to existing data visualization techniques to do just that. Among them is the ability to easily overlay multiple data sets on a bar graph or chart via drag-and-drop tools.
Other improvements include improved usability of heat maps, geographic mapping analysis and time-series analysis charts, according to analysts.
The amount of data that data visualization tools can analyze is also on the increase, thanks in part to in-memory technology. In-memory analytics engines load data into random access memory rather than disk, increasing query speed and lessening the amount of data modeling needed with traditional BI platforms.
And one open source predictive analytics language is enabling the creation of new types of data visualizations that make previous visualizations “look kind of tacky” in comparison, according to Marick Sinay, a financial analyst with a large multinational bank, who uses the technology on a daily basis. Called R, the free software language was designed for statistical computing and graphics.
2. Social media analytics. Social media analytics is an emerging discipline, and so are the tools that enable it. Currently, most social media analytics technologies require significant expertise to use and are far from perfected.
But Forrester Research’s Jim Kobielus thinks that social media analytics tools – as they become easier to use – will be integrated into traditional BI platforms. That makes it more likely that non-power users will get their hands on the technology and understand what the blogosphere is saying about their companies.
Facebook, the world’s largest and most influential social networking site, is doing its part to bring BI to the masses. The site offers page owners a number of analytics tools to monitor and measure referral traffic, demographic data and click-through rates, according to Alex Himel, a Facebook software engineer.
“By understanding and analyzing trends within user growth and demographics, consumption of content, and creation of content, [Facebook] page owners and platform developers are better equipped to improve their business with Facebook,” Himel said.
3. Unstructured data analysis. A related technology that could make BI more appealing to business users is unstructured data analysis.
More than 80% of corporate data sits in Word documents, emails and other unstructured forms, according to analysts. Much of the data business users interact with each day is unstructured. Improving the ability to access and analyze that data would probably prompt more business users to adopt BI technology.
Most current BI platforms are not well suited to unstructured data analysis, according to Forrester’s Boris Evelson. And text analytics tools have yet to reach a level of maturity that would be inviting for non-power users.
But a couple of vendors are experimenting with integrating enterprise search technology with more traditional BI platforms in hopes of solving both problems, Evelson said. If successful, the new tools could prove particularly useful for marketing analytics, such as parsing user comments and reviews from online forums.
There are other things that need to happen – including better end-user training – before BI truly comes to the masses. But improvements in data visualization, social media analytics and unstructured data analytics technologies would go a long way to making pervasive BI a reality.