The Real Problem: Bad Sensor Data at Scale

Industrial data travels a long way before it becomes a KPI on a management dashboard. It starts at sensors, passes through PLCs and interfaces, is stored in historians such as the AVEVA PI System, and is then used in visualization tools, reporting systems and analytics platforms.

Along this journey, many things can go wrong. Sensors drift. Signals become stale. Limits are configured incorrectly. Metadata is inconsistent. Connectivity issues create gaps. In many environments, there are no data quality checks at scale and no data quality SLAs in place. This is not a theoretical issue. As highlighted in the Timeseer introduction, 90 percent of consumed sensor data is not verified, structured or monitored.

The business impact is significant. Reporting may rely on unverified aggregated data. Control loops may perform poorly due to bad measurements, leading to increased energy usage. Digital twins may diverge from reality. Data scientists often spend the majority of their time validating and cleaning data instead of generating insights. In short, without trusted data, digital transformation initiatives lose efficiency and credibility.

Timeseer shifts the focus from building better models to building better data.

Timeseer: From Data Downtime to Data Trust

Timeseer is a specialized platform for sensor and time series data quality management. It continuously monitors, profiles and verifies industrial data streams and detects defects before they impact operations or decision making.

The platform operates along three core principles: profile and verify, condition and protect. With more than 100 built in data quality indicators, Timeseer can detect drift, broken correlations, stale data, missing values, improbable limits, values outside defined ranges and many other anomalies. It supports segmentation, heatmaps and custom checks to quantify the reliability of data at scale.

Beyond detection, Timeseer also enables conditioning of data. Missing values can be imputed intelligently, metadata enriched, artefacts filtered and signals aligned across different sources. Finally, the protect layer introduces data quality SLAs, alerts and even certificates for data trust, turning data quality into a governed and measurable discipline.

Customer examples presented in the Timeseer material demonstrate tangible business impact. In utilities, continuous monitoring of smart meter data detected broken check valves within months and prevented severe operational issues. In process industries, data quality improvements reduced the time spent on data preparation in analytics projects by up to 50 percent. These examples show that data quality is not an IT topic alone. It directly affects efficiency, safety, compliance and profitability.

Timeseer x Amitec: Trusted Data in the AVEVA PI Ecosystem

Amitec brings decades of experience in industrial data infrastructures, especially around the AVEVA PI System. With more than 50 years of combined PI expertise and a strong footprint across Europe, we design, implement and maintain robust data architectures for energy, manufacturing and process industries.

In many environments, the PI System acts as the digital backbone for operational and energy data. Timeseer integrates seamlessly into this landscape. It connects to historians and cloud environments, scans time series data and feeds verified and enriched data back into the ecosystem. This means that data visualized in PI Vision, analyzed in Seeq or used in corporate reporting can be continuously validated and monitored.

By combining Timeseer’s data observability capabilities with Amitec’s deep PI integration know how, customers receive a holistic solution. We do not only monitor server performance and interfaces. We monitor the data itself. This closes a critical gap between infrastructure monitoring and business value.

How Amitec energy:center and Timeseer Work Together

Energy management is one of the most sensitive areas when it comes to data quality. Regulatory frameworks such as ISO 50001 require continuous improvement of energy related performance and rely heavily on accurate baselines and energy performance indicators. The Amitec energy:center has been designed exactly for this purpose.

The energy:center covers the full spectrum of energy management processes. It supports energy monitoring, energy controlling, ISO 50001 mapping, objective management, audits and continuous improvement loops. It integrates directly with the AVEVA PI infrastructure and enables structured EnPI and baseline management according to ISO 50006 principles. It also provides audit trails, KPI prediction, reporting and commercial energy controlling including validation against utility data.

However, even the best energy management system depends on the quality of underlying meter and process data. If energy meters are misconfigured, if signals are stale, or if limits are incorrect, EnPIs and baselines lose credibility. Audit processes become more complex and manual validation effort increases.

This is where Timeseer adds decisive value to the energy:center. By continuously profiling and verifying energy relevant signals before they are used for KPI calculations, Timeseer ensures that EnPIs are based on trustworthy data. It detects drifting meters, reversed counters, missing data and anomalies that would otherwise distort energy balances or forecasts. Data quality SLAs can be defined for critical energy streams, and deviations are flagged proactively.

The result is a powerful combination. The energy:center structures and operationalizes energy management according to ISO 50001. Timeseer guarantees that the data feeding this system is reliable, validated and fit for purpose. Together, they create a transparent, auditable and high performance energy management environment that supports compliance, sustainability goals and measurable cost savings.

In a time where energy efficiency, regulatory compliance and ESG reporting are strategic priorities, trusted industrial data is no longer optional. With Timeseer and Amitec, it becomes a competitive advantage.