I have been waiting for the popularity of collaborative business intelligence to make it to vendors’ product roadmaps. Companies such as Dundas, TIBCO Spotfire, and Panorama have recognized the need to add this level of context to data.
However, my concept of collaborative business intelligence may differ from what vendors’ are touting and it’s important to explain my definition. Before I can do that, I need to quickly go over common BI activities.
Quants, data scientists, and other types of sophisticated information workers slice and dice data to better understand it. These specific views of the data are then presented to decision makers using dashboards, visual analysis tools, or reports.
Collaborative business intelligence is about adding human analysis to the data and the data presentation media. Imagine if a data analyst did an in-depth study on 10 years worth of delivery logs. They discover a serious late delivery time period and, upon further investigation, discover it was because of a hurricane in a particular region. If this data was presented as is without this additional context, a decision maker would have made a phone call to understand the anomaly or dig through the data themselves until they came to the same conclusion. It would be much faster and less redundant for the analyst to comment on the anomaly and record his conclusions on that data. This would then persist throughout the business intelligence system and would be associated with any presentation of this data.
It goes further than that though. The decision maker can add another comment suggesting an action plan to mitigate these types of anomalies. Sophisticated workflows can now be documented against the very data that started the workflow.
By recording the viewers’ interpretation and actions associated with the data, companies’ will bring more meaning to their performance measures that was traditionally not available in business intelligence systems.
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