Updated 2020-02-08

Data quality is fundamental and critical for utilites fiber operators.

Data quality are not high on the agenda – why is that?

Over the last 20 years, I have attended numerous meetings with FTTH operators discussing GIS features & functionality, workflows, mobility, automation, integration, web access and so on. It is always cool to plan all the nice things an application can do. Looking back, what we maybe should have spent more time discussing was the foundation for it all: the underlying data and the quality of it.

Why? Because data quality issues are not very sexy It is not about new fancy apps or functionality. Data quality issues can be complicated to solve, hard to detect systematically, and it is not easy (can be done though!) to set up a consistent business case for fixing data quality issues. “Hey management, you will have to ask the sales guys to take a break, because we are focusing on data quality issues, so we cannot design new network or connect new customers!” – No, that will not fly.

But on the other hand, if all the high level talks of automation, agile behaviour, integration, AI and process support are ever to become real, then it is necessary to adopt a new focus where data quality turns into a long term effort and investment.

A couple of Issues to consider

Have an Inventory Management System, that actually cares about data quality

Your GIS must have explicit data quality measures, that enforces data quality, that enables metering of data quality and gives tools to fix potential problem

Make data quality visible in the organisation

Assign data quality stewards and assign the overall responsibility for data quality clearly with a single individual on management level. Setup dashboards and use KPIs to show and track data quality level.

Data quality is a culture thing

Make it clear, that everyone involved in network design and documentation constitute small, important gears in the process. They do not produce spreadsheets, PDF documents or colored maps. They produce valuable data.

Fix it

Allocate resources needed. Fixing backlog of data quality issues can be a massive task and management should make the needed investment.

There is no simple answer or instruction as to how these issues are solved in a given organisation. It is necessary to prioritize, invest and set up specific activities in order to meet the data quality target, that supports the process necessary.