What does it mean to it mean to be a brand new customer to BI systems. Big data optimization sounds great, but there is a lot of behind-the-scenes work to ensure BI platforms are implemented well, deliver sound analysis, and provide the organization with real, tangible value.
Introducing an organization to BI is a learning process from the start. Many enterprises see where they want their BI platform to be, without having a baseline understanding of the roadmap to achieve that end product. Additionally, for organizations with smaller IT units, it is even more critical that the business engage a trusted partner who can act as a guide through the cultivation of a working BI platform. Step one is centered on developing the organization’s fluency in what BI can offer and how to extract value from these products.
By easing customers into BI with some simple data points to demonstrate what data will look like and what the basic product capabilities are, organizations begin to familiarize themselves with BI. Whether an organization has 50 people or 100,000 people, this small initial data deployment is key because it becomes the foundation for successful end-user adoption. Furthermore, this technique ensures the organization is capable of viewing consumer information through a different lens. For example, when there are sundry more data points and cross-sections, the organization will have a comprehensive understanding of what this data means and the value-add for the organization.
With the consumerization of IT, enterprise users also want to access organizational information through methods similar to their normal habits outside the office. The expectation for enterprise ease and access to information in a digestible format is higher,. In fact, this expectation tends to result in a common struggle facing new customers in the BI world – desire for immediate gratification from a BI investment. The process of intelligence gathering, analysis and feedback is complex, and it is crucial to build a strong data foundation before layering more composite pieces. Therefore, it is absolutely necessary to communicate timing estimates for building a comprehensive feedback loop of data gathering and subsequent analysis to prevent impatient customers or data integrity issues.
Of course, there also is a maturity curve for data – something we will be examining more later in the series – in which accuracy, consistency and value are evaluated and verified. Additionally, the accessibility and analysis of data is crucial as well to reach the BI paradigm – real-time BI feedback and business forecasting.