Adding Collaboration to Data Discovery
I read with great interest (since I was on a panel at the GigaOm Structure Conference discussing it) the excellent Sector Roadmap by Andrew Brust on Data Discovery. In this report, Andrew identifies six “Disruption Vectors” for the data discovery market, and assigns a rating to each vendor.
The identified vectors were self?service, data blending (data virtualization), mobile, the cloud, storytelling via data presentation and access to non-relational sources.
When asked on the panel if there was anything else that should have constituted a disruption vector, I responded with collaboration. Some agreed and some did not, but judge for yourself as I describe the importance of collaboration in business intelligence tools.
Elements of collaboration include an embedded discussion forum or a workflow component making it easy and delay-free for the user community to cooperate and partner to reach business conclusions. It includes star-ratings, simple comments, interactivity, interfaces to internal social networks, unstructured data, security and bookmarks. Sound familiar? Sounds like a modern web session to me.
Collaborative BI is the combination of Collaborative Interaction, Information Enhancement and Collaborative Decision Making. It is collecting and memorializing the communication over data that would naturally occur otherwise. It facilitates that communication and accelerates it. Communication that would normally require phone calls and emails now finds that it can happen within the confines of business intelligence. Communication enhances the value of the information assets we bring to the enterprise.
Among the elements of good collaborative BI are:
- Report Approval Workflow
- Prompts to action
Whether the scope of the collaboration is a data element or one of its uses, as in a report, is significant and requires design. For example, if a report is sales by region by month for this year, when does a comment about a low-performing region show up – for the report and/or the period and/or the region? Collaboration must be designed in this way. Another good practice is to encourage face-to-face or virtual meetings among users where there is a high concentration of collaboration activity. This indicates an ongoing, and unresolved, conversation and a meeting could be best to attain closure.
Another artifact of good collaborative BI is voting buttons whereby decisions over data can be voted upon. Also users can “follow” reports or its collaboration in much the same way as people follow blogs and/or the blog comments. Users can follow “subjects”, machines, customers, regions, etc.
Users can also be represented in a familiar way – with their picture!
Collaborative business intelligence, combined with a great data foundation, makes the very most of the limited analysis window of the user. Collaborative features should be considered essential in any business intelligence rollout accessing our information management stores.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions.