Power users and self service line of business (LOB) users are always asking for more data access and easier to use tools that enable them to bypass traditional IT in favor of analytic freedom. Most of these users will push back hard on an environment that restricts thier ability to wander about in enterprise data.
Providing power and freedom to these users has incridible upside.
- Faster time to insight
- Less work for IT
- Aligning business knowledge with the analytic process
- pluggin more team members into data driven insights
These are just a few of the positives that come from self service or discovery based business intelligence. So what are the pitfalls of all of this freedom? I see two major disconnects that need to be addressed as this type of BI beomed more pervasive.
- Governance - Adding desktop data is a common choice for self service users. They often mash up information from a wide variety of sources many of which are not avialble to the rest of the enterprise. This can enhance or detract from the overall analysys and leave many in the dark as to how certain decisions are being made.
- Enterprise Value - The insights created by LOB and power users can be extremelly valuable to the rest of a team or the company in general. Self service solutions that don't supply a common and easy path to share and leverage these insights is missing a key feature. I am partial to solutions that balance freedom and control.
So the question is where does the control belong and how tight should it be. Does some governance and control add to the value? I think it does but the challenge is finding the sweet spot between freedom and a walled garden. If you want adoption you can't fence the power users in.
For your entertainment - Bing Crosby and the Andrew Sisters sing Don't Fence Me In.
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Thursday, March 5, 2015 at 6:48PM 
Innovative companies are adopting advanced analytics to take action and match the speed of their business. This is especially true in the world of manufacturing where complicated process driven activities benefit greatly from smarter, faster analytic insights and actions. Collecting and analyzing process data from sophisticated manufacturing processes requires a flexible and agile infrastructure that supports a wide variety of disparate data sources often spanning sensor and machine sources that are combined with instrumentation and testing data, machinery and production data, customer and market data, supply chain information, 3rd party benchmarks and a wide assortment of system data. 
