PI System Compression Tuning: Balancing Data Fidelity and Storage Efficiency

Compression is one of the most important—and most frequently guessed-at—parts of PI System administration. Every PI Point produces a time series. The question is not whether the PI Data Archive should store data, but which events it needs to retain to represent the signal accurately.

If compression is too aggressive, meaningful changes can disappear from the archived trend. If it is too permissive, the archive stores more events than the application actually needs. The right setting is different for different signals, and it should be based on observed behavior rather than a default copied across the tag database.

Compression is a data-quality decision

PI System data reduction typically involves two different stages. They affect different parts of the data path, but they work together to determine what reaches the archive and how faithfully the historical signal can be reconstructed.

A PI Point can have a healthy-looking current value while its historical archive contains too few events to represent important variation. Conversely, a tag can retain nearly every incoming event even when the additional detail provides little operational value.

What is data fidelity?

Data fidelity is a measure of how much information is retained in the archived signal compared with the incoming raw snapshot signal. It is useful because archive size alone does not tell an administrator whether a configuration is appropriate.

A lower event count may mean efficient storage, or it may mean that the archived trend has lost meaningful changes. A higher event count may preserve detail, or it may simply retain noise and redundancy. The relationship between archived volume and retained information is not linear.

• Increasing the number of archived events generally increases data fidelity, but it also increases storage and processing requirements.
• Increasing compression generally reduces the number of archived events, but it can also reduce the detail retained in the historical signal.
• The useful target is usually a point on the curve where the required fidelity is preserved without storing unnecessary data.

The right target depends on the use case. A control loop, laboratory measurement, energy balance, and long-term production KPI may all require different levels of detail. There is no single compression value that is correct for every PI Point.

Why static compression rules fail

Many PI Systems begin with default settings, engineering rules of thumb, or values copied from similar tags. Those approaches can be useful starting points, but they do not measure the behavior of the actual signal.

Two tags with the same engineering units and span can behave very differently. One may be stable and easy to compress. Another may contain frequent changes, short-lived spikes, oscillations, or irregular behavior that requires a different setting. A value that works well for one tag can therefore be too conservative for another or too aggressive for the next.

The challenge becomes more significant as a PI System grows. Reviewing trends and tuning exception or compression values manually across hundreds or thousands of PI Points is slow, difficult to repeat, and hard to validate consistently.

How to interpret compression tuning results

PI Nexus+ Compression Tuner evaluates selected PI Points against a configured target data fidelity and groups the results into practical categories.

These classifications are more useful than a simple list of recommended values. They help an administrator prioritize where storage can be reduced, where data quality may be at risk, and where the signal requires closer attention.

How PI Nexus+ Compression Tuner works

PI Nexus+ uses a scan-and-review workflow so compression changes are based on observed point behavior.

A practical operating model for PI administrators

Compression tuning works best as a repeatable maintenance activity rather than a one-time cleanup exercise.

1. Start with a clear fidelity target. Define what the archived data must support before choosing a value. The target for an operator display may differ from the target for a calculation, report, or model.
2. Scan representative groups. Organize points by process area, instrument type, or operational purpose so results can be interpreted in context.
3. Investigate hard-to-compress points. Do not force every signal into the same optimization pattern. A point with unusual behavior may need a different collection strategy or an engineering decision.
4. Use trend comparison for high-value tags. Visual review is especially important for critical measurements, fast-changing signals, and tags used in downstream calculations.
5. Apply changes in a controlled way. Use selective updates when the result needs additional review, and bulk updates when the scan results are consistent with the chosen target and change process.

Compression tuning should be measurable

The central question is not “What compression value is normally used for this type of tag?” It is:

How much of this signal do we need to retain, and what is the most efficient setting that preserves it?

PI Nexus+ Compression Tuner makes that question measurable. It observes the incoming signal, evaluates the current configuration against a target fidelity, identifies the tags that are undercompressed or overcompressed, and provides trend evidence before changes are applied.

For PI administrators and engineers, that creates a practical feedback loop between configuration, archived data quality, and storage efficiency—without relying entirely on static defaults or manual tag-by-tag guesswork.