Having recently attended the International District Energy Association’s CampusEnergy2024 conference, we were inspired to connect some common advice frequently given to college students with recommendations for improving district energy operations. Just as students are encouraged to collaborate, focus, and plan, organizations can apply these same principles to ensure a sustainable and successful future. One topic discussed during CampusEnergy2024 around collaboration, focus, and planning as a bridge to the future is data analytic initiatives. 

Implementing a robust data strategy enables district energy providers to reduce costs, improve sustainability, proactively address equipment issues, and drive overall performance excellence. With access to more data than ever before from smart meters, sensors, and other Industrial Internet of Things (IIoT), harnessing insights through collaboration, discipline, and foresight unlocks significant operational gains. Read on for recommendations inspired by student tips tailored to the needs of district energy providers who want to leverage data more in day-to-day operations with a district energy historian. 

Get Involved: Foster Cross-Functional Collaboration 

We constantly encourage students to collaborate with peers and get involved in activities outside lectures. Doing so builds connections and expands learning beyond textbooks. Similarly, collaboration across teams enables organizations to build impactful data capabilities tailored to real operational needs. 

When launching new data initiatives, bring together IT, engineering, operations, finance, and management stakeholders. Gathering broad input ensures buy-in on the metrics and key performance indicators (KPIs) that will provide meaningful insights versus “nice to have” data. For example, the operations team may want operational efficiency data from critical assets like boilers and chillers. But finance may be more interested in campus- or portfolio-level return on investment (ROI) metrics. Cross-domain collaboration spots these differences early when defining KPIs. 

And continue gathering user feedback after launching data analytics applications through surveys, interviews, and usage metrics. Target areas needing more training or refinement and continue to emphasize cross-functional collaboration and teams. Maintaining ongoing stakeholder participation maximizes adoption and impact and promotes a data-informed culture. 

5 Recommendations for Creating a Cross-Functional Data Analytics Team 

  1. Include representatives from critical business units – operations, engineering, finance, and IT. Cross-domain collaboration ensures alignment of priorities and needs. 
  2. Designate a data-savvy team to lead the agenda. The team lead provides the leadership to maintain focus and momentum. 
  3. Define clear goals and objectives for the team upfront. What KPIs, dashboards, or other outcomes will define success? 
  4. Develop a RACI (Responsible, Accountable, Consulted, Informed) matrix outlining roles and responsibilities. The matrix clarifies who owns what across the diverse team. 
  5. Maintain executive sponsorship with regular check-ins. The engagement shows leadership buy-in for the team to operate effectively across the organization. 

Set Routines: Operationalize Data Management Processes 

Students thrive on consistent routines around studying, activities, and responsibilities. Similarly, standardized processes are essential when implementing enterprise data analytics. Document playbooks and training manuals so insights can be generated reliably and repeatedly as new members join. 

Beyond technical processes, organizations need policies and protocols for data access, usage, and contextualization. Items include: 

  • Access policies that outline who can view and modify various data sources based on role. The policy prevents unauthorized or unnecessary access. 
  • Usage guidelines that specify how data insights can be applied and ensure compliance with regulations. 
  • Contextualization protocols that require providing units, definitions, update frequencies, and other metadata. Additional data aids interpretation. 
  • Data dictionary detailing the meaning and origin of data elements. 
  • Documented analytical methodology explaining KPI and other output calculations. 

In addition to policies and procedures, organizations need the right tools to enable effective routines. For example, data historians feature robust collection and visualization capabilities to ingest and view data from diverse sources. Data historians also simplify calculating KPIs and creating dashboards using point-and-click tools. Establishing consistent data collection and calculation routines improves output quality. 

With the right blend of technology, policies, and documentation, organizations can operationalize data analytics to provide trusted insights that drive sound decisions. And again, ongoing stakeholder participation maximizes adoption and impact. 

5 Recommendations for Creating Effective Policies and Procedures 

  1. Identify critical processes that need documentation, such as workflows, approvals, and controls. Prioritize based on risk, complexity, and impact. 
  2. Establish standardized templates for policies and procedures to streamline creation. 
  3. Involve process owners and teams in development to incorporate insights from those doing the work. 
  4. Keep language simple and actionable. Avoid overly complex policies that are hard to follow. 
  5. Make documentation easily accessible through central repositories like intranets. 

Stay Focused: Carefully Select Key Performance Indicators 

Students must prioritize the most critical academic topics rather than trying to master everything. With thousands of data points across assets, district energy organizations must also identify the most critical KPIs. Avoid “analysis paralysis” by focusing on metrics tightly linked to priorities like operational excellence, sustainability, equipment health, and strategic goals. 

Data historians aggregate disparate data sources and enable deriving meaningful KPIs. For example, combining data across systems and sites allows for calculating overall efficiency and project impacts. Historians provide tools to calculate KPIs from raw data, giving you the tools necessary to turn raw data into meaningful insight. 

Beyond simply creating KPIs, KPIs should be contextualized and shared via dashboards, enabling better insights versus raw data alone. Additional context allows all teams to make better decisions using trusted, accessible metrics. Careful selection and thoughtful application of KPIs drive data-informed excellence. 

This is why organizations should leverage stakeholder input and use cases to determine the highest-value KPIs. Keep the number focused on what matters most for decision-making. Prioritize areas with the most significant potential for optimization. 

5 Recommendations for Adding Context to Data 

  1. Provide clear definitions for data elements and metrics. Define acronyms, units of measure, formulas, etc. 
  2. Document the source systems and raw data feeds for aggregated metrics. 
  3. Include refresh rates and times for all reported data points. 
  4. Add analyst names or owners for any derived metrics or complex visualizations. 
  5. Label axes, scales, and benchmarks on charts and graphs. 

Look to the Future: Take a Long-Term View

Students balance immediate academic requirements with long-term visions. Similarly, organizations leveraging data analytics should adopt a multi-year roadmap. Launching new platforms and dashboards is just the beginning. 

Leverage current technologies like data historians for aggregation, normalization, and visualization. But look ahead to future capabilities as well. Techniques like machine learning and AI uncover deeper intelligence over time as more data accumulates. These emerging technologies move beyond reactive monitoring to proactive diagnostics and issue prediction. For example, fault detection algorithms can analyze sensor data to detect anomalies and predict potential equipment failures before they occur. Identifying failures in advance prevents costly downtime. 

Establish a strategic vision and technology roadmap for continuously improving capabilities and unlocking new data use cases. Treat data analytics as a long-term priority, not a one-off initiative. With the proper foundation and vision, data insights can transform operations for years to come. 

5 Recommendations for Creating a Data Analytics Roadmap 

  1. Define strategic business objectives and how data can support them. The objectives will help align the roadmap to key priorities. 
  2. Assess current analytics maturity across people, processes, and technology. Identify capability gaps. 
  3. Develop data governance standards to manage quality, security, and lifecycle. 
  4. Catalog existing data assets and systems. Determine new data needs. 
  5. Prioritize initiatives based on business value, effort, and dependencies. 

Next Steps 

The parallels between student success skills and optimizing operations through data are clear. Focusing on collaboration, consistency, priorities, and the future enables harnessing data analytics to drive district energy excellence. To learn more about HanAra’s data and analytics solutions, do not hesitate to contact us today. Our team is ready to partner in assessing your needs and implementing proven solutions tailored to your operations.Â