We’ve all been there: returning from an energizing industry conference, head swimming with new ideas, business cards stuffed in pockets, and a notebook filled with hastily scribbled insights. 

But as you return to daily operations, a crucial question emerges: how do you translate that inspiration into action? 

This year’s IDEA2025 conference left a powerful three-word motto that’s been hard to shake: Aggregate, Integrate, Innovate. These words could easily become our rallying cry for HanAra, a data management and software company. But more importantly, this concept perfectly encapsulates how organizations should approach their industrial data strategy in an increasingly complex landscape. 

Three Pillars of Industrial Data Excellence 

Before we dive deeper into data aggregation, let’s understand how these three pillars work together to create a comprehensive approach to industrial data: 

Aggregate Your Data 

The journey begins with aggregation: collecting and combining data from disparate sources across your operations. 

This critical first step transforms overwhelming volumes of raw data into meaningful, manageable information sets that reveal patterns and insights previously hidden in the noise. 

Integrate Your Data 

Integration moves beyond mere collection to embedding data insights throughout your organization. 

Data integration means breaking down silos, ensuring insights reach every level of your operation, and connecting systems so information flows where it’s needed most. 

It’s not just IT’s responsibility. It’s everyone’s business. 

Innovate With Your Data 

Innovation completes the cycle: leveraging your aggregated, integrated data to discover new efficiencies, create new processes and procedures, and solve previously intractable problems. 

Without innovation, organizations risk falling into patterns that limit growth and agility. 

While each pillar deserves extensive exploration, let’s focus on data aggregation, a foundation for successful industrial data strategies. 

Industrial Data Aggregation 

What is Industrial Data Aggregation? 

Data aggregation in an industrial context refers to gathering and summarizing data from multiple sources into more meaningful, actionable formats. 

It transforms overwhelming volumes of raw operational data into insights that can inform (and sometimes drive) decision-making. 

Think of it as distilling signal from noise. 

It’s about identifying what matters most amidst the constant stream of information generated by your industrial systems. 

Types of Data Aggregation

There are different types of data aggregation. Three main categories are: 

Time-Based Aggregation 

Time-based aggregation combines data over specific periods such as hourly, daily, or monthly. 

For example, a manufacturing plant might aggregate energy consumption data hourly to identify peak usage patterns, or a utility might aggregate customer consumption data monthly for billing purposes. 

Spatial Aggregation 

This approach combines data across physical locations or operational units. 

For instance, analyzing the total number of equipment failures across a specific production line or facility during the past 30 days, or examining performance metrics across different regional facilities. 

Categorical Aggregation 

Categorical aggregation combines data based on specific categories or classifications. 

An example would be calculating the Energy Usage Intensity (EUI) for all residential buildings within a portfolio over the past year or aggregating maintenance events by equipment type. 

For example, in a district energy system managing multiple buildings, EUI comparisons over time help track energy performance year over year. 

But aggregation becomes even more powerful when paired with building categorization (e.g., commercial vs. residential) and normalized data like square footage and heating/cooling degree days. 

By aggregating energy consumption by building type and adjusting for size, operators can identify high-energy use types, discover operational anomalies, and prioritize energy efficiency investments across their portfolios. 

Methods for Effective Data Aggregation

When aggregating industrial data, you can apply several mathematical methods: 

  • Minimum/Maximum: Identifying extreme values within a dataset, such as peak demand or lowest efficiency points 
  • Average: Calculating the mean of collected values over time or across units 
  • Summation: Adding all values together, particularly useful for consumption or production metrics 
  • Median: Finding the middle value in a sorted dataset, which helps eliminate outlier influence 

The method you choose depends heavily on the question you’re trying to answer and the type of data you’re working with. 

For instance, if you’re performing time-based aggregation of a totalizer (like a flow meter), summation is appropriate (or change difference). However, averages or medians provide more meaningful insights for sensor readings that fluctuate.

Pro Tip: When aggregating data, be mindful of Simpson’s Paradox. The paradox is a statistical phenomenon where trends apparent in different data groups disappear or reverse when combined. 

Understanding your data’s context is crucial to avoid misinterpretation. Though the Simpson’s Paradox may be more prevalent in social-science data analysis, it is still important to remember. 

Benefits of Industrial Data Aggregation

Effective data aggregation delivers several key advantages: 

Focused insights: Aggregation lets you concentrate on identified patterns rather than drowning in data points. 

Holistic understanding: By combining disparate sources, you gain a more comprehensive picture of your operations that individual data streams can’t provide. 

Simplified communication: Aggregated data is easier to present to stakeholders and incorporated into reporting dashboards, making insights accessible to non-technical team members. 

Operational efficiency: Identifying trends through aggregation enables more efficient resource allocation and process optimization. 

Challenges of Industrial Data Aggregation

However, data aggregation also presents specific challenges: 

Loss of detail: Aggregation inevitably obscures some nuances and details in the original data, potentially hiding important anomalies. 

Methodology transparency: The separation between raw data and insights means stakeholders sometimes must understand “how the sausage is made” to interpret results properly. 

Irreversibility: Once data is aggregated, you often cannot reverse-engineer individual data points. For example, knowing the average energy usage of all residential buildings doesn’t tell you about any specific building’s performance. 

Context preservation: Maintaining the contextual information that gives meaning to aggregated data can be challenging but essential for accurate interpretation. 

Industrial Historians and Data Aggregation

Industrial data historians play a crucial role in the aggregation process. These specialized time-series databases capture, store, and aggregate operational data from industrial systems at scale. 

They are the backbone for many aggregation strategies, providing the technical infrastructure to implement the Aggregate-Integrate-Innovate approach. 

Modern historian systems offer configurable aggregation capabilities that support various business needs while managing the trade-offs between detail preservation and meaningful summarization. 

They enable real-time aggregation for operational decision-making and long-term trending for strategic planning. 

From Aggregation to Action

While aggregation forms the foundation, it’s a step in the data value chain. The true power emerges when aggregated data is properly integrated throughout your organization and used to drive innovation. 

Effective aggregation enables better integration by providing clean, relevant data that can be meaningfully incorporated into various operational systems. This creates the foundation for innovation; whether that’s predictive maintenance algorithms that reduce downtime or efficiency improvements previously invisible in the noise of raw data. 

The Aggregate, Integrate, Innovate theme from IDEA2025 provides a robust framework for maximizing industrial data value. Successful industrial data aggregation isn’t just a technical exercise; it’s a strategic approach that aligns data practices with business objectives. 

As you implement your post-conference action plan, start with aggregation, and you’ll be well-positioned to innovate your way into the future. The journey from raw data to actionable insight isn’t always straightforward, but it delivers tremendous value. At HanAra, we’re helping industrial teams like yours navigate this path daily. Want help turning your data into insight? Reach out to start the conversation.Â