When I wrote about mashups and business intelligence earlier this year, I played up the empowerment angle of users being able to combine disparate sets of data and produce custom reports with minimal assistance from IT. This ability becomes even more significant when they enjoy the ability to share their reports with other users, who might provide added insights.
I referred to an IBM demonstration of mashups created with its Cognos BI software that also included social elements such as ratings of mashups, tags, a list of most-discussed mashups and a wiki. I also cited a blog written by Quentin Gallivan, CEO of BI software-as-a-service provider PivotLink, in which he tapped mashups and social media as two of five trends he predicts will shape the SaaS BI market this year. (The other three: SaaS BI growth will outpace on-premise BI. Business users will demand more pre-packaged analytics. Companies will insist upon SAS-70 Type II certification a non-negotiable requirement for security.) Gallivan wrote:
For more operational decisions, mash-ups will allow users to assemble all of the relevant data to make a decision, while social capabilities will allow users to discuss the relevant data to generate "crowdsourced" wisdom. As a result, business users will be able to make decisions with greater confidence and understand how their decisions impact both the company's and their individual performance.
Sharing data, through mashups and other means, is one of the aspects of social BI discussed by Alta Plana Corp. analyst Seth Grimes in an Intelligent Enterprise post. He mentions several examples of sharing capabilities. Both TIBCO and Tableau Software, for example, give users the ability to embed visual analytics into blogs and other content. He also mentions IBM's Many Eyes, a system created by the company's Visual Communication Lab that gives users tools to create visual displays of any data they want to upload as well as the ability to discuss the data with other users.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=i
While ManyEyes' visualization capabilities have wowed folks, the collaborative elements also really grab users, I wrote in a post about Many Eyes way back in 2008. I shared a quote from Pat Hanrahan, a professor of computer science at Stanford University whose research includes scientific visualization:
When analyzing information, no single person knows it all. When you have a group look at data, you protect against bias. You get more perspectives, and this can lead to more reliable decisions.
The next step beyond these kinds of sharing are collaborative tools that enable collective work by teams, writes Grimes. He quotes Lyzasoft founder Scott Davis, who says his company offers social-media tools and processes that help users "work in fluid, self-organizing teams to create, share, analyze, enrich, critique, rate, relate, modify, forward, and repurpose quantitative and qualitative information components in ways that make relevant information easier for everyone in the community to find, understand, and apply to decisions."
The way things are going, few enterprise applications won't feature at least some social elements. I've written about the growing numbers of vendors adding social features to business process management software, a logical trend considering how many folks within organizations (and often outside organizations as well) are touched by business processes. Earlier this week Metastorm introduced an online work flow-modeling tool called Metastorm M3 designed to involve more users in the modeling process. It also rolled out Metastorm Smart Business Workspace, which allows organizations to create browser-based communal workspaces that include dashboards, chat software, links to reports and various widgets.
Grimes mentions a third element of social BI, incorporating social-media analysis into enterprise data analyses, which he promises to tackle in a future blog post. It'll be tough, he says, because it will involve challenges including text analytics, data cleansing and integration, and examining not just content, but the networks that generate it.
I wrote about some of the hurdles earlier this year, sharing results of a Kognitio survey that found just 14 percent of BI practitioners said they wanted to incorporate data from social channels as part of their ongoing data analysis efforts. Doing these kinds of analyses will take many BI pros outside their comfort zones, said John Thompson, CEO of Kognitio's U.S. operations, when I interviewed him. They are comfortable working with financial data and sales data, which has clear structure. But, said Thompson, "How do you take 140 characters of what Tim and Donna said, put it in a database and run a trend line on it?"