This paper is essentially about complementary software systems. It is about how business intelligence complements analytic processes and applications - and how both of these are complemented by data governance (including data quality and master data management) and data integration (including data virtualisation, data replication as well as extract, transform and load).
However, it is not just important to recognise that these various technologies work together but also, we believe, it is necessary to think about them as a whole rather than as piecemeal parts. In other words, if you are planning a business intelligence deployment, for example, then you also need to know how that will work alongside any analytical applications or data mining implementations that may already be in place. Further, you need to consider how you will leverage data integration technologies to support your deployment as well as any implications that the business intelligence may have with respect to data governance.
To put this simply:
- Business intelligence and analytic processes are frequently embedded within business processes that require both functions.
- You need to ensure that the data you will be querying is fit for purpose: that the quality, timeliness and completeness of the information you are analysing is sufficient for the task in hand.
- You need to know how you are going to get the data from where it is right now to the user, in the most efficient manner, in the right format.
While we will explore these points individually in the sections that follow (because it will be easier to follow that way) what we are actually building up to is that all of these considerations are interconnected and should be treated as such.