Data in RAN: More is not always better
This post highlights how having more data in RAN does not necessarily lead to better decisions, emphasizing the importance of data quality, relevance, and context over sheer volume.
Data in RAN: More is not always better
As we move deeper into AI-driven RAN, one assumption keeps showing up:
More data means better decisions. It sounds logical. More visibility, more metrics, more insights. But in practice… the opposite is often true.
After working with optimization platforms, SON systems, and now SMO environments, I have seen how data can become more of a problem than a solution. Because in RAN, data is not the limitation anymore. Clarity is.
Here are some common misconceptions:
• * Many assume that collecting more KPIs will improve optimization, without considering redundancy and noise. • * It is often believed that all data is equally valuable, when in reality only a small portion is actionable. • * Engineers expect AI models to extract insights automatically, ignoring the effort required to prepare and contextualize the data. • * There is a tendency to prioritize volume over quality, leading to misleading conclusions and inefficient decisions.
In real networks, excessive data introduces new challenges:
• * It becomes harder to identify root causes due to conflicting or overlapping indicators. • * It increases processing complexity without necessarily improving decision accuracy. • * It creates dependency on data pipelines that may not be fully reliable or consistent. • * It delays reaction time, especially when filtering and validation are not well defined.
This is where the real shift needs to happen:
From data collection… to data relevance. From my experience, effective data strategies in RAN share a different mindset:
• * They focus on a reduced set of meaningful KPIs aligned with specific objectives. • * They prioritize data quality, consistency, and context over sheer volume. • * They define clear relationships between metrics, instead of analyzing them in isolation. • * They ensure that data supports decisions, not just dashboards.
Because in the end, data does not create value by itself. Decisions do.
And better decisions do not come from having more data… They come from understanding which data actually matters.
As AI continues to evolve in RAN, this becomes even more critical. We are not limited by how much data we can collect. We are limited by how well we can interpret it.
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