This may seem like an odd concept, but give it some thought. Every customer and business user is currently used to the idea that they can share, rate, discuss and learn from others. This idea became an expectation and it could and should apply to Business Intelligence and also to a company Users as well! – A Business Intelligence(BI) tool that supports mobile, self serve data prep, plug n play predictive analysis and smart information visualization will deliver business users with sophisticated tools and algorithms which are easy to use and provide access to data that’s simple to share and personalize. Business users can use these tools to become Citizen Data Scientists and in so doing, a company will start to see the emergence of power users who take a creative, insightful approach to data analysis.

When users report and share the information the next thing to do is allow readers to comment and share it on social media. They even may need to analyze the reports and data. Understanding the social media using data analysis helps IT employees and executives gain knowledge of its benefits to their organization. Some organizations even spy on messaging apps like BBM to get valuable data of competing companies in the same niche. This is a bad approach, when it comes to fair competition. Other company might hire more sophisticate means which could make things more complicated. It is important to look before these matters.

Important data analysis help Information and Technology Scientists do more research. Many of these choices do NOT require 100% accurate data. Rather, they require agility and enough, strong data and analysis to see a trend or a model or spot an opportunity or a challenge. Users need to drill down and dive into information without having to ask for assistance from IT or an analyst. This agility will move your business along with reliable data and prevent delays. By balancing high quality data with popular self serve data preparation, in a social/sharing environment, your organization can balance resources and measure and manage data quality vs. Data popularity so the social facet of data analysis could work hand-at hand with the quality data approach.