Energy management – whether the foremost goal is to increase efficiency or correctly integrate/transition to renewables – is all about data.

Correct data collection practices and proper interpretation can lead to the discovery of previously unseen opportunities, increase ease of project oversight, and greatly improve the quality of project delivery. It is therefore essential to establish solid foundations from the outset.

However, too much focus is often placed on sustainable equipment upgrades instead, with large amounts of investment put into solar panels, efficient lighting, HVAC, (etc.), but these are not magic bullets. Proper data management, on the other hand, can reveal more immediate energy saving potentials, offer insight into which upgrades will be the most effective, and make clear the best practices to enhance equipment effectiveness after installation.

If data is to be used effectively, four things are of paramount importance: Where it comes from, where it is stored, how accurate it is, and the correctness of its formatting. This article first offers a broad view of automation and decision making in the data process, then discusses the business case and cost benefit, and finally takes the four foundational principles above and offers a list of reflective questions to aid in implementing best practices.

 

Data management is sustainability management

Evidence-based sustainability  should always be data-driven.

Utilising relevant, accurate and timely flows of data can assist in benchmarking, reporting, and the continual improvement of operations. 

In the chart below, raw data is acquired (through data management systems) and automatically processed using set quality-assurance parameters (business rules). This initial layer of automation reduces manual handling and the potential for human error, establishing  a system-centric model that assists in improving scalability and makes data accessible to multiple stakeholders in near real-time.

The days of excessive manual data entry (human input centric models), reviewing PDFs of various energy bills, and handling several large excel spreadsheets that only a handful of people know about are over.

Instead of listing the tens of relevant approaches, best software solutions, and proven management systems that apply to the data driven model focused on automation (on the left), I will first answer the important initial questions around – how do I fund this, and what is the ROI?

 

Investment in data infrastructure

The charted approach above establishes data mechanisms that will inform sustainability decisions. I may be biased, but it seems to me that getting this right is one of the most important contributions a sustainability manager can make to the initial and ongoing success of sustainability programs.

That said, there is a financial cost  to the establishment and ongoing maintenance of these mechanisms. So what is the business case for data management? How do I attract funds for a project, and what is the financial return?

There are no quick and easy answers to these questions. 

In general, relative to the upfront and ongoing costs, there may not be a specific cost benefit from the technology itself – it is enabling infrastructure.

My perspective is that the business case becomes clear when a more integrated and holistic view of the sustainability function is taken.

In this view, data management forms part of the ‘overhead’, or the base requirement to operate an effective sustainability program, acting as a foundation upon which cost benefit can be built. The success of any further implementations comes down to risk management and using the right tools to  deliver on sustainability targets. This process should be data driven – if you don’t have the right data, or don’t manage it correctly, how can you  make effective decisions?

Some questions to consider around data management could include:

Manual data entry 

• In the finance space, should we do accounting manually or should we use software (Xero, MYOB, etc.)? 
• In accounting for sustainability, what are the ‘tools of the trade’ and are we aligned with industry standards?
• Should we just keep billing manually and performing data entry ourselves since that’s how we’ve always done it?
• Do we get our data and other important information in a timely manner?

If we used billing & data entry software, or even smart metering, would there be a net cost saving? Data entry can be demoralising grunt work (our brains and mental health deserve better). Sustainable organisations should value people and culture.

Multiple stakeholders

• Who relies most on sustainability data? 
• How do they access that data?
• What linkages are there: who is sharing data, and what data has relevance across multiple departments?
• Does our sustainability team have data that no one else knows about that just sits in Excel gathering virtual dust?
• Would other stakeholders find this data valuable, and is not having it a barrier to incorporating sustainability into their decision making?

Quality assurance

• What is the quality of our data, and if we don’t know, how important is figuring out the answer?
• Are we receiving information from (existing) faulty systems or providing incorrect inputs?
• For example, that smart metering system which has not been maintained and may now have data dropping out (broken meter, vandalism, flooding, expired software subscriptions, or the “what is that supplier’s email again?” type of thing).

Existing system integrations

• Do we use PowerBI, and can we use that for internal energy data management?
• Is there someone in IT that knows how cloud storage (Azure, Amazon) and existing smart metering APIs can feed into our systems?
• Is IT aware of all our data processes, or are there data management systems operating in a silo?

Finance systems

• Are Different departments or types of sites (owned, leased) billed differently? i.e. are we capturing our full energy expense?
• How are future energy costs forecasted and budgeted for?
• Does energy budgeting for a site rely on sustainability data, or does an internal accountant guess, use averages, or base it on results from prior periods?
• In the example above, could the accounting department use sustainability data to more accurately budget for future energy costs?
• Would data being more accessible to all stakeholders help better control energy costs?

 

What next?

Data management is foundational to effective sustainability decision making.

It requires a few initial interventions (at a cost) and a culture of making continual improvements to systems.

In the long-term, it can assist in improving performance and reducing the risk of not delivering on organisational energy and/or carbon targets.

If you want to discuss some of the concepts in this article or are thinking about how to get started, we have a quick diagnostic survey to highlight possible improvements, which we can send to you upon request.

Thanks for thinking about your sustainability data!