Sustainability and data management – establishing the foundations

Energy management, energy efficiency, demand response and renewables all have one thing in common, data.  

Data is central to opportunity identification, project delivery and oversight, it is the foundation of sustainability decision making.

While solar energy, lighting, HVAC, refrigeration, compressed air, motor and many other equipment upgrades can present positive financial returns, data management in many aspects is part of the supporting infrastructure that enables such interventions.

Where your data comes from, where your data is stored, is the data accurate, are there manual preparations required to have the data in the correct format? These are all central questions to delivering on the promise of sustainability more generally.

 

Data management (equals) sustainability management 

Evidence-based sustainability is data-driven.

It utilises relevant, accurate and timely flows of data to assist benchmarking, reporting and continual improvement of operations. 

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

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

 

Without giving a list of the tens of relevant approaches, software and systems here that apply to the data driven model focused on automation (on the left), I might attempt to help answer the important initial question around – how do I fund this, what is the ROI?

 

Investment in data infrastructure

The approach above is about establishing the enabling infrastructure for sustainability decisions. I may be biased, although 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 for the establishment and ongoing maintenance of the infrastructure. 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 this question. In general, relative to the upfront and ongoing costs there may not be a specific cost benefit from the technology in itself – it is enabling infrastructure.

My perspective is that the business case is clear via a more integrated and holistic view of the sustainability function in general.

In that sense, data management forms part of the ‘overhead’ or the base requirement to operate an effective sustainability program. This comes down to risk management and what tools are needed to achieve the performance required to actually delivery sustainability targets. It should be all data driven and if you don’t manage the data, how do you make effective decisions?

Some questions to consider around data management could include:

Manual data entry 

  • In the finance space, do we do accounting manually or do we use software (Xero, MYOB). In accounting for sustainability, what are the ‘tools of the trade’ and are we aligned with industry standards?
  • Do we just keep using manual billing and data entry since we have always done it this way?
  • Do we get information in a timely manner?
  • If bill data entry software or even smart metering was used isn’t there a net cost saving?
  • Data entry can be demoralising grunt work (our brains and mental health deserve better). Sustainable organisations value people and culture

Multiple stakeholders

  • Who relies on sustainability data? 
  • How do they access 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 sits in excel?
  • Could other stakeholders get value from this data and is this a barrier to incorporating sustainability in decision making?

Quality assurance

  • What is the quality of our data. If we don’t know then how material is this issue?
  • Are we receiving information from existing systems that are faulty or providing incorrect inputs?
  • For example, that smart metering program that has not been maintained where data droped out (broken meter, vandalism, flooding, expired subscriptions, or the “what is that suppliers email again?” type of thing)

Existing system integrations

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

Finance systems

  • Do different departments or types of sites (owned, leased) get billed differently i.e. are we capturing our full energy expense?
  • How are future energy costs forecast and budgeted for?
  • Does energy budgeting for a site rely on sustainability data or does an internal account guess, use averages or prior period results?
  • In the example above, could the accounting department use sustainability data to better budget for future energy costs?
  • Would data access by 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 longterm, it assists in improving performance and reducing the risk of not delivering on organisational energy and 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 we can send you. Thanks for thinking about your sustainability data!

 

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