“Seriously, we are in the midst of the convergence of voice and data and that is challenging the infrastructure of the telephone companies. There are huge commercial interests in the basic technology, but even more so in content delivery and control of content.”
It’s important for a CSP to develop a convergent data strategy across a diverse set of tools within it’s B/OSS estate. What’s not so common is to have to develop this strategy of data convergence across multiple instances of the same tools.
Some examples of this phenomena are:
- A franchised CSP model where all franchisees share common tools
- A CSP with multiple different subsidiaries (eg a fixed line operator, a mobile operator, an ISP, etc)
- A regulatory body that enforces data standards on other entities within its jurisdiction (this is especially common within utility industries)
When it comes to convergence of data sets in these situations, there are definite pros and cons.
Pros include having a common data schema that your data modellers know intimately and can develop repeatable code for (ie common joins/merges on separate database instances).
The cons include having multiple instances of data sets means you have non-unique identifiers and managing the data can be a challenge.
Despite this, your strategy still isn’t so different to any other system-to-system mapping. You still need to come up with a strategy with the linking keys, data models and naming conventions that allow you to converge data sets across your estate.