Why Is Business Intelligence (BI) Important For Your Small to Midsize Company Series

Step 3: Standardize Methods for Data Representation and Data Integration

You have many business functions to manage, along with applications that support these business functions. But were these applications designed and developed to meet each function’s immediate operational needs? Since each developer has their own set of visions for the technical aspects of the system, you may experience inconsistencies in the definitions or representations of a customer or product depending on the application you’re using. These inaccurate representations are seen primarily in the data going into and coming out of the application. Furthermore, depending on how current the application’s technology is will dictate the differences in how the data sets are stored. For example, you may have various lists or filed-based data sets that require a more complex indexed database system, but you may not be able to access all the information you want to because the technology is incompatible or outdated.

So what does all this mean for your Business Intelligence system? When you present business intelligence results for review, seeing inconsistencies between generated reports casts doubt on the entire BI system. The most common inconsistencies are seen when data sets are brought together for analytical purposes, and in turn, your data elements, names, definitions, data types, etc. are also questionable. Ultimately, when two reports are not consistent, it raises suspicions about all the reports you produce.

How do you get accurate representations using business intelligence and reduce the negative impact of variation? To make forward progress and inspire increased trust in your reports and analyses, you need to make the following consistent across the board:

• Standardize the definitions
• Data element representations
• Table structures
• Overall data model used for core business information concepts
• Techniques and tools that you use to move data from the source systems to the data warehouse