Everyone loves to talk about collaboration. It evokes positive associations – teamwork, sharing, and a host of others. And it fits our experience that great ideas and successful efforts usually happen when people come together.
Everyone loves to talk about collaboration. It evokes positive associations – teamwork, sharing, and a host of others. And it fits our experience that great ideas and successful efforts usually happen when people come together. So it is no surprise that collaboration has become a hot topic in the BI community.
But making collaboration happen, in BI or any other domain, is dicier. Good tools for collaboration are not enough by themselves. If people aren’t open to the give-and-take of collaboration, or the institutional culture does not foster it, the best tools in the world won’t create it.
Yet bad tools can keep collaboration from happening even if the people involved want to collaborate. Good tools, on the other hand, smooth the process of collaboration along.
Tools for Collaboration
What makes for good collaborative BI tools? Good tools invite interplay, and avoid imposing artificial barriers. Sounds good – but what does it mean? It means, in part, recognizing that individuals have a wide range of cognitive styles, influencing how they can most effectively present or absorb information.
Take a concrete example, people in a company looking at sales performance. Analysts see one picture, sales data that can be visualized in various ways, by city, product, or other variables. These can be handily displayed as graphics, and may show striking patterns.
Salespeople experience this data in an entirely different way. Perhaps not as “a picture” at all, but a series of narratives – individual encounters with customers. Some encounters fall through; others break through and end with a sale.
These narratives don’t lend themselves to bar graphs. Word clouds might be more helpful, or perhaps thumbnail descriptions of successful and unsuccessful sales encounters.
Matters of Perspective
In this hypothetical example, the analysts and salespeople belong, in a way, to two different cultures. Bringing them together is arguably a form of workplace diversity.
Successful collaboration happens when diverse people bring their very different descriptions of the sales environment together into one conversation. The salespeople look at the analysts’ graphs, the analysts hear the salespeople’s narratives – and both see connections they’d previously missed.
But for this to happen, you need tools flexible enough to handle these different ways of presenting and absorbing information. A tool that can only combine data streams into elegant charts and tables is great for the data analysts, but doesn’t help the field salespeople describe their experiences. A word-oriented tool is great for the salespeople, but gives no help to the analysts.
A pencil and restaurant napkin can do both, but how do you store the results for easy future reference? Yet as BI blogger Mike Ferguson notes, fluidity of use is a core virtue of collaborative BI tools.
To succeed at collaborative BI you need an adaptable toolkit, one that can produce charts and word clouds – and make them easy to work with, talk over, and save for reference. A good collaborative BI suite is a facilitator, not a specialist.
Next Steps:
For more insight read our Q & A on Cloud BI with Shawn Rogers (@shawnrog), Vice President Research for Business Intelligence at Enterprise Management Associates. You can also check out last month’s On-Demand Webcast “Cloud Analytics and the Consumer” featuring Rogers.