From Data to Decisions: What Makes Data Credible

In this episode of The Pulse by GRESB, we explore why data credibility is about more than accuracy alone. The conversation examines the importance of complete, traceable, and contextualized utility data, the common challenges organizations face in collecting and validating it, and how trusted data supports better benchmarking, operational performance, and decision-making. Tune in to learn how a strong data foundation can help real asset managers turn information into meaningful action and long-term value.

Can’t listen? Read the full transcript below. Please note that edits have been made for readability.

Aidan: Hi everyone and welcome to The Pulse by GRESB. I’m your host today, Aidan Versfeld. I’m a manager on the member relations team here at GRESB, and today we’ll be diving into a topic about data credibility and completeness. We’ll discuss what makes data fit for purpose at GRESB and how completeness, consistency, and reliability determine whether data can truly support benchmarking and decision-making. Joining me to discuss the topic today is Joost De Smedt, the CEO from nanoGrid. Joost, thank you for joining. How are you doing today?

Joost: I’m fine, thank you. And you?

Aidan: Very well, thanks.

Joost: Thanks for inviting me.

Aidan: Yeah, it’s great to have you on board. Could you maybe introduce yourself a bit more and explain a bit about what nanoGrid does?

Joost: As you said, my name is Joost De Smedt. I’m founder and CEO of nanoGrid. nanoGrid is, now for about 15 years, in the business. We do monitoring of utilities data mostly. We are the data layer in the utility landscape, collecting all kinds of data: water, gas, electricity, district heat, but also outside temperatures, anything that can help with understanding the data — and not only supplying data, or finding data, or tracing data, but also trying to explain the data.

We do this for two kinds of customers. One is the real estate company, and the other focus is on the users — I mean, the occupiers of the buildings. We do this all over Europe and that’s basically what we do.

Aidan: Good. Good to hear. Then you are definitely set up as a critical expert on this topic. To set the stage, we can first ask the question: what do we actually mean by credible data in the real assets industry? Why is completeness just as critical as accuracy?

Joost: Well, completeness and quality — which means data quality and data quantity — are, so to say, bedfellows. I mean, the one will not go without the other. You can have good quality, but if it’s not complete, well, there you are. We call it the time series recording.

If we miss like two days of data, all the rest can be as good as it can be, but you will have to extrapolate and manipulate the data. So credible data is the data that is complete and fully validated, which is a hassle. I mean, it’s not easy. And then you only have the data, which means you don’t know what the purpose is.

So that’s another thing. You also need the full data context to have fully credible data, which means you will have data that is actionable, which is important because you need to do something with it. Okay? Having the data is not enough.

Aidan: Yeah, and the importance is obviously having that data, but also the ability to understand that data and make those decisions from it.

Joost: Exactly.

Data is not only the data point — it comes with a location, it comes with a technical context, it comes with a business context. This data point can be part of a cost center for the customer. So the context is really important.

Aidan: And within that context, and looking at the organizations you work with — the real estate managers particularly that you work with — what are the common challenges that those companies face when collecting and validating utility data?

Joost: Well, this might sound a bit silly, but the first point is to make sure that you have the right meter. That’s number one, because it’s not because someone points you at the main meter that it is the main meter.

You have to check it on invoices, on meter numbers, where it is located. Is it a main meter, a grid meter? A sub-meter? What’s the purpose? So before you do any measurement or any monitoring, the first step in your traceability is: is it the right meter? And is the conversion factor of that meter known? Did you check it? And that’s where the traceability starts. So that’s the first challenge.

Aidan: Yeah, understood. The crucial start is obviously having metering at a point that is accessible and understandable.

Joost: Exactly. And it’s also more and more the case that the grid providers already have a sensor on a meter, which means that it is not accessible anymore. Then you have two possibilities: either you ask the grid provider for an API or for the data, where you lose a bit of the traceability, or you make sure that you can read the meter by a second output or even by putting an optical sensor that reads the meter index.

And that is also so important for your CSRD reporting — to make sure that you have the full traceability from the meter itself to the final data point. So the importance of a meter is first of all your ESG reporting.

A second importance is when you want to invoice one of your tenants, or you have a multi-tenant asset. So you need at that point to make sure that the meter is trustworthy.

So knowing what the meter is, what it is for, and what the business context of this meter is comes first.

Aidan: And you made reference to the reporting of that data. So looking at things in maybe a bit more of the GRESB context — incomplete or unreliable utility data, how does that cost participants in GRESB scoring and peer comparability?

Joost: Okay. Now you’re making a very big step, Aidan. The big step is we have now found the right meter, and you’re now reporting. So the first thing is to make sure that the other challenges are in place, which means that this meter needs to communicate the full traceability over the data center, making sure that the data is correctly validated, the anomaly detection has been done, et cetera. So this is also a very important part before you can start reporting. Now, to answer your question about gaps or inconsistencies in data: first of all, if these first steps are done correctly and your traceability is there, and you have enough redundant data — which is an important part, okay? — there are lots of grid providers that provide you with the data. There is invoicing, there are meter readings, for example. There are a lot of ways to check if the data that comes in is correct or not. That’s the data validation and the data enrichment.

If incomplete data comes into GRESB — and it’s not only GRESB, it’s also the known ESG platforms — if they don’t know the origin, your problem is the dependency on input data. It is not real-time. There is limited granularity. It comes from different sources. And you have a compliance problem, which means that your customer automatically has a compliance problem. It’s a dilemma, honestly, because you can do all your best to do reporting, but if the data that comes in is incomplete, you know that much of your data supply has already been processed. There have been extrapolations, there have been changes in the data before it comes to you. It is sometimes untraceable what has been done to the data, and that’s why this data should be auditable. We’re doing everything possible to have auditable data so that an external company like a Deloitte or KPMG or whoever can audit the data and make sure that the data that comes into your platform or any other platform — be it for analysis, be it for ESG reporting, be it for any stakeholder that needs the data — is as complete and as trustworthy as possible.

Aidan: I mean, that’s an important step before reporting to GRESB and any other systems. GRESB relies a lot on third-party assurance and verification of that data. It’s weighted a lot more when it comes to scoring your data. But as you said, we do not know the starting point of this data. We do not know the accuracy that is reported to us unless it is assured and verified. And if it isn’t, then comparability gets skewed against other peers. So yeah, that assurance piece, that in-between piece between reporting and collection, is major for us and for mandatory reporting solutions as well.

Joost: And that’s also why I was talking about the enrichment of the data. Lots of companies are now phasing out gas, okay, as a combustible, and they’re changing to other ways — like heat pumps, for example, okay? If your data comes in and all of a sudden gas stops, and you don’t have the information that they swapped to heat pumps — this is what I call enrichment of the data — well, you will probably ask what happened. Do you have a gap in the data because the gas falls out? So you need all this extra data, this enrichment, to make sure that the full context of the data is understandable and actionable. That’s why we don’t only collect the consumptions, but also the outside weather conditions. Why do you explain that there’s more gas or less gas? Well, because outside it’s colder or warmer. What we call the metadata, the enrichment of the data — but also a change in tenants, a change in the purpose of the building, et cetera. All this enrichment, all this metadata, is important.

Aidan: And so as the journey goes on for improved enrichment and better completeness and more real-time data, how does operational value increase for sustainability teams and asset managers as that progresses?

Joost: Absolutely. Very good point, because they need this for predictive maintenance, for example. If I say actionable data, it’s not only that you are happy to have true and correct data, but this single source of truth — so to say — this platform with all of the data is of huge importance for the local facility managers and technical engineers, because they can trust the data and they can see what energy efficiency they can bring to their daily operations. It’s good for them to have a very granular overview so that they can configure their BMS systems based on data that is really trustworthy.

Aidan: Yeah, and I think I’ve seen similar things particularly in Europe at GRESB. At a certain point, where at least 75% data coverage is reported to GRESB, we’ve deemed that a substantial and relevant level to now raise the standard — to really give more weight to the performance of those assets and how we are using that data to make those improvements in terms of energy or water efficiency. So that aligns a lot with what we’re seeing on the ground.

How do you see this currently evolving? Is the industry evolving quickly from simply collecting data to actually ensuring that it’s credible and useful?

Joost: Absolutely. For now, people are happy we have the data. You talk about 75%, which is already, if you take the industry average, huge, okay? 75% coverage is huge. The customers that we service, we try to have 90 to 95%. There will always be a part of the buildings that is in construction, et cetera, which is fair. But if you have 80 to 85%, it’s great — but then you have the data. It will evolve to the first question: is this trustworthy data? Is the data correct? Because if we want to decide upon this data, and AI is coming everywhere — through doors and windows, AI is coming — and AI will decide based on data. Whether your next HVAC system needs to be put in winter mode or whatever. So this data will be crucial.

First question: we have the data? Nice. It is complete. Can we trust the data? And the next step is: can we count on it for automation? Can we count on this data to automate my building, or to automate operational decisions?

Aidan: Understood. And then looking a bit more directly at how we retrieve this data — direct metering versus invoice or estimate-based utility data — how significant is the distinction between those two when it comes to credibility?

Joost: In my opinion, very significant, because the traceability is important. So first of all, for the grid meters — so you have your solar panels, you will have your battery in place, you have your EVs, which are also important in your GRESB reporting — all of these, what we call the main consumptions, you want them directly and not via invoices. In your invoice, sometimes the grid providers use average predictions and things like that.

So, if you want clear, neat, and correct data, make sure that you have the data directly from these meters, the grid meters. Then your invoices are very important because they’re considered correct and serve as a single source of truth. So we need these as redundant data, and that’s important.

Also, BMS systems — you have your solar platform data, you have anything you can find as a data source. Please send it. Your historical data is so important. Give all your historical data, even if it’s a flat Excel file, I don’t care.

Joost: But give the data so that we can supplement the data we measure now with history, so that we can predict future developments.

Aidan: And you mentioned a few techniques that companies can use, but if you were to summarize in maybe one or two suggestions for organizations that are maybe struggling with data collection, what are some quick wins that they can implement to improve that?

Joost: First, have an inventory of the meters. That sounds very logical, but it is not always practiced. If you have your meter inventory — what meters exist and how to classify them — are these meters grid meters? Are they important for my accountant, for my finance department? They may not be grid meters, but they may still be important for invoicing your tenants. And then all the meters that you need eventually for BREEAM, for reporting sustainability like CO2, air quality, whatever — these are, so to say, less important, but they’re still important for your reporting.

But the first ones, if you ask me for the quick wins: go for your grid connections and your on-site buffer and production. So what do you already have available and what is already trustworthy, and where are the gaps?

Aidan: Yeah, very interesting. It’s great to hear that a lot of organizations can take very small steps just to get them started.

Joost: But if you start, don’t start writing your meter index on the back of an envelope. Think future-proof. Make sure that you have your hardware in place and that you start with the most important meters first. Even if you have a small budget, start — and think about the data layer. Think about it this way: your grid provider is not interested in data; they’re interested in your consumption because they want to invoice it. Your building management system is not interested in data except for controlling your building and your comfort. That’s what they are for. Think about the data layer. And in the future, you will need your sub-metering. So in any case, you will need a data vendor, a data partner.

Your grid provider will not provide you with the outside temperature and degree days. And your BMS system will not integrate your historical data either. So this data layer is important. But even if you have a small start, start in a professional, state-of-the-art way.

Aidan: Well, thank you, Joost. I think we’ve about run out of time for today’s episode, but thank you very much for joining me. It’s been very interesting to get your insight and expertise on the topic, and it’s been a pleasure talking to you.

Joost: My pleasure. And it’s always a pleasure to talk about utilities and about consumption for me.

Aidan: Thank you to everyone listening. I’ve been your host, Aidan Versfeld, and I’ll see you next time on The Pulse.