Unless they’re holding corn, silos are generally bad for business. In the world of big data analytics, silos are particularly troublesome because the general rule of thumb is, the more data you have, the better your business strategy.
Silos stunt business growth by disabling your ability to make informed business decisions, including:
- Developing long-term strategies to capture market share.
- R&D decisions such as when — or if — to bring a product to market.
- Streamlining potential inefficiencies of your business.
Data silos occur when a business model segments a particular subset of information and hoards it without applying it to the entire business application. It’s a bunker mentality that shouts, “It’s mine!” as those in the silo seek to hold on to their own personal bastion of big data power. So the silo fails to share, cross-reference or interpret “their” data within the big strategic picture of an enterprise organization.
Imagine if the sales team refused to share their CRM data with marketing. Or if the oncology department of a hospital failed to share outcomes related to a particular treatment modality. What if R&D failed to share data on their latest product design tests?
Is it possible that the biggest hurdles data managers are currently facing are not how to effectively analyze and use all the data a business captures, but how to access it across multiple data silos?
Elevating Big Data Toward Business Intelligence
Effective business decisions cannot be made in a vacuum, which is really the best argument for eliminating data silos. Pinpointing isolated pockets on legacy systems and then integrating them into big picture competitive intelligence is job one in our current era of big data.
Interestingly, we’ve heard that some SaaS vendors are potentially feeling threatened by the move toward big data analytics. In the same way that departments will feel protective of “their” data, SaaS vendors may perceive the process of full data integration as having a negative impact on their livelihood.
I believe it was Aristotle that said, “The whole is greater than the sum of all its parts.” True enterprise-level competitive intelligence benefits most when all of the cross-functional parts and pieces of data are analyzed and reported.
Mining Big Data for Business Intelligence
When silos happen, it’s time for an intervention.
Mining big data should be like the snake that ate its own tail; the strategy should drive data capture while the data should also pinpoint competitive intelligence.
Eliminating silos means establishing big data warehouses designed to break down the silos found across finance, HR, sales, marketing, web analytics, R&D, and other departments. Setting standardized rules for capturing, storing, and analyzing data while improving security is just one argument that will help foster the elimination of data silos.
Big data analytics requires bridging the gap between data silos and fostering collaboration between cross-functional departments to benefit the whole enterprise. It’s the new imperative for data managers.