When I teach my master’s degree classes I use slides to present the theoretical, and then use workshops to illustrate how the theory applies to design and development of business intelligence solutions. In addition, I use examples or case studies to tie the theory to real world situations. This is how I connect the dots in the classroom.
In the real world the situations I discuss or encounter in enterprise BI, data warehousing and MDM implementations lead me to the conclusion that many enterprises simply do not connect the dots. These implementations potentially involve various disciplines such as data modeling, business and data requirements gathering, data profiling, data integration, data architecture, technical architecture, BI design, data governance, master data management (MDM) and predictive analytics. Although many BI project teams have experience in each of these disciplines they’re not applying the knowledge from one discipline to another.
The result is knowledge silos where the the best practices and experience from one discipline is not applied in the other disciplines.
The impact is a loss in productivity for all, higher long-term costs and poorly constructed solutions. This often results in solutions that are difficult to change as the business changes, don’t scale as the data volumes or numbers of uses increase, or is costly to maintain and operate.
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One might expect this behavior in enterprises that are new to BI and data warehousing, however, it exists in the most experienced teams in large enterprises. This result at many enterprises is that:
- Multiple generations of BI/DW systems are built because the previous version just did not work out as well as expected
- BI systems are very costly to build and maintain
- BI systems are difficult to change and augment
In the next “Connect the Dots” we will discuss how data modeling is disconnected from the rest of BI disciplines.
