Social BI – Less About Social Data and More About Collaboration

This post is written by Steve McDonnell, Spotfire Blogging Team.

This post is written by Steve McDonnell, Spotfire Blogging Team.

When you think “Social BI”,  you may have the urge to think of “tweets”, “likes” and other online social activities being monitored and mined.  However, Social BI is much more than analyzing your customer’s social data.  It’s about making the business knowledge you uncover easily available to others. It’s about tapping into people who share your interests in getting answers to similar questions or solving similar problems.

Social BI in the Enterprise

Mark Lorion, head of marketing for Spotfire, described Social BI best during a tweet chat – It’s about “analytics being more collaborative.” For an enterprise, this means collaboration across organizational boundaries – something that is now much easier to do with Social BI functionality built into leading BI tools. According to a recent interview with Forrester advanced analytics analyst James Kobielus (@jameskobielus) , “social” in the enterprise means merging data, insight, and employee opinions and discussions into a stream that combines structured and unstructured data.

 Social BI Trends

Kobielus says that, although there is a lot of discussion about Social BI, it’s still very much a “leading-edge” solution for companies. One of the reasons for this is that Social BI changes some of the fundamental ways in which we think about business intelligence. Kobielus explores these changes in greater detail in a webcast titled BI in the Cloud: A self-service alternative to “Big BI”.

Social BI Challenges

 With Social BI, we begin to combine “official” sources of data from a data warehouse with additional sources of user-supplied data, knowledge and opinions that are not necessary “official” and don’t come from a data warehouse. Working with Social BI data becomes more complex because you have to consider the reliability and motives of the source data as you conduct your analysis, and it often includes much more unstructured data than data that comes from a warehouse. But while it may slow adoption of Social BI, it’s not likely to impede it. The benefits of “going social” far outweigh the risk that can be introduced into the process — risk that can be successfully managed as part of the social business intelligence process.

So the next time you think of “Social BI”, move past the understandable urge to associate it with only social data.  Think also in terms of collaboration. And if you wish to be social while also staying informed on future Social BI topics follow us on Twitter, Facebook and our blog.