A lot of planning time is still spent arguing about which numbers are right. Route teams are trying to build workbanks, shape renewals and agree access, but half the discussion is about whether the underlying data is complete enough, recent enough or consistent across systems. When you are planning for a live rail network, that uncertainty is a risk in its own right.
We see that every day in the planning teams we work with across the rail network. Site assessments sit in one place, condition scores in another, and structures and level crossings are captured differently by different routes. Nobody is quite sure how many of the key assets have been assessed in the current Control Period. It is not that people lack data. It is that nobody can see, in one place, how healthy that data really is.
Turning data health into something planners can see
That is why we have introduced a Data Health Dashboard as part of our central asset data store and planning layer. Rather than relying on gut feel, planners get an explicit view of how strong the data is for the parts of the network they are working on. This is the heart of better rail planning data quality.
At the top level, the dashboard gives each route or geography an overall health score. Behind that score sit a set of rules that look at completeness, consistency and freshness. Are all the critical asset attributes populated for this type of structure. How many assessments are older than the threshold the team is comfortable planning from. Are there inconsistent records for control points or interlockings that need resolving before anyone commits to a scenario.
Because those rules are configurable, teams can weight what matters most for the decisions in front of them. A renewals programme with a strong safety driver might lean more heavily on recent condition and risk assessments. A programme focused on efficiency might care more about having complete unit costs and delivery history. Either way, the health score becomes a quick way of seeing whether the evidence behind a plan is strong enough.
Over time, planners can also see how data health is changing. Trends by Control Period or planning horizon make it clear whether the data picture is improving as new assessments and corrections come through, or whether gaps are opening up in certain routes or asset groups.
Supporting better planning conversations
The real value comes when planning teams use data health as part of their everyday discussions. Instead of a meeting starting with a general worry that the data might be unreliable, the team can open the dashboard for the relevant area and talk about what the score is telling them.
If the health score for a route is high and trending upwards, planners can be more confident building scenarios from that data, knowing that assessments are up to date and key fields are complete. If the score is lower, the dashboard shows which rules are driving that, which assets are affected and where the gaps are concentrated. That gives planners a clear list of data issues to resolve, rather than a vague sense that the data is a bit messy.
Because the rules are transparent, everyone involved in planning discussions can see why a score looks the way it does. People can challenge the thresholds, agree changes and then watch the impact of those changes over time. Data health becomes part of the shared language of planning, not a specialist concern that only a few people understand.
From today’s gaps to tomorrow’s better plans
Data health is not a one-off exercise. Once a gap is visible, teams can plan data improvement work alongside their planning milestones. Fixing missing attributes for a set of structures, commissioning new assessments in a problem area or resolving inconsistent records for a group of control points all become concrete tasks that feed directly into better planning.
As those fixes are made, the Data Health Dashboard shows the impact. Scores rise, problem rules drop away and planners can move from cautious assumptions to more confident decisions. Over time, this creates a feedback loop. Planning work highlights where data health is limiting decisions. Targeted improvements close those gaps. Future workbanks, scenarios and funding cases then start from a stronger, more reliable evidence base.
Crucially, this is not about chasing a perfect data set for its own sake. It is about knowing, at each planning decision, how far you can trust the data in front of you and where you need to be more careful. By making data health visible and measurable, we give planners a better foundation for the choices they have to make every day.
Giving planners confidence in their decisions
In the end, rail planning lives or dies on the quality of the evidence behind it. When teams can see the health of their data, understand the rules behind the scores and track improvements over time, they can build plans with more confidence and defend them more clearly to decision makers.
A central asset data store, combined with a planning layer that surfaces data health in this way, turns a long standing weakness into a strength. Instead of being held back by hidden data issues, planners can use data health as a tool to drive better questions, better scenarios and better outcomes for the network.
Using business intelligence tools such as our rail planning software platform gives you the confidence to make better decisions, improving the productivity and efficiency of all your rail planning projects. We can help you get the best results and the correct information every time. For more information about our product and to see how using business intelligence can significantly improve your planning for rail maintenance, upgrades and more, contact one of our team today for a demo of our rail planning platform.