To say that big data holds the key for enterprise success in the current landscape would be an understatement. In fact, according to a study from Accenture, 79 percent of decision-makers agree that organizations that don't embrace data analytics are at severe risk of losing their competitive edge – and could even be forced to close down.
It's imperative that businesses are able to leverage the insights their informational assets can provide to maintain a favorable market position. But what happens when those datasets and assets are siloed and inaccessible to analysts and stakeholders?
The importance of data collaboration
"Organizations that don't embrace data analytics are at severe risk of losing their competitive edge."
In this type of environment, data collaboration is absolutely critical. Any information, assets, databases or sets that can't be included in analysis – because they are locked away within specific departmental resources, or are maintained as tribal knowledge – could be causing missed opportunities when it comes to reaping actionable insights.
At the same time, however, this is a pursuit that requires cross-departmental effort.
"The ability to collect, access and analyze massive amounts of data has reached the point where no single entity can do all the work; great data collaboration is a necessity for success at any level of business," wrote Dataversity contributor Charles Roe.
Exposing tribal data
Data collaboration and the ability to include specific, valuable data within analysis is especially imperative when it comes to tribal knowledge. This key information is typically siloed within documents or datasets held by a certain, internal subject matter expert or department, but not commonly shared with those outside the group.
Supporting access to this type of data asset, however, can make all the difference within analysis.
"Once that complex data blending has been done, there are significant benefits to the wisdom of the crowd, aka data analysis collaboration," Roe noted. "Various experts in different disciplines can review and weigh in, external partners can collaborate on shared data insights, and new answers can be asked in a groupthink manner with iterations within the data analysis."
Supporting collaboration: The data catalog
In order to enable this level of collaboration and ensure that tribal knowledge and other subject matter isn't siloed and inaccessible for analysis, organizations require a robust data catalog. This advanced tool can crawl data assets from across the company and catalog them in place without disrupting existing databases and other resources.
In this way, siloed data assets become a thing of the past, and the organization can put itself on a path toward success, supported by in-depth and valuable data and analysis collaboration.