We adopt the data work framework developed in previous work to explicate the methodical ways in which the energy data furnished by the new interactive IoT system was articulated in the interactions between advisors and their clients (Fischer et al. 2016). To briefly recap, this framework orients us to discrete phases of data work, including ‘anticipation’, i.e., the articulation work that occurs during installation; ‘rehearsal’, i.e., the articulation work that occurs before an in-home visit; and ‘performance’, i.e., the articulation work that occurs during in-home visits. However, setting aside these particular phases observable in our case we would contest that ‘data work’ understood more broadly is indeed a necessary feature of any efforts to make sense of and exploit data; it is merely the specificity of the setting we have studied that makes it perspicuous in the particular guises that the following sections unpack (cf., Bannon et al. 1993). We elaborate key findings by offering conversational extracts or ‘vignettes’ from the field studies. To provide topical continuity, most vignettes relate to the same case (one household). Vignette 1–4 have been taken from the installation visit, in V5–7 advisors discuss the same household’s data in a workshop, and V10 is taken from the advice visit. V7–8 relate to a different household. It is worth noting that despite necessarily elaborating specific cases, there is nothing special about the selected vignettes; we have selected them as exemplars of the ordinary, routine doing of data work running through our data corpus. The abbreviations used to refer to the speakers are advisor (A), client (C), and researcher (R) who accompanied advisors on in-home visits to gather data and help install the sensor kit. Numbers are added if more than one of these is party to the interaction.
Anticipation
Anticipating data is the first job of work involved in making the technology work in situ. It is done by administering a questionnaire during an in-home interview with the client. In the first deployment (Fischer et al. 2016), the questionnaire was used to profile the property (e.g., fuel type, heating system, appliances), the occupants (e.g., number, type and age of people living in the home), their everyday routines (e.g., how they use the heating system, dry their clothes, ventilate the home, etc.), and to establish the client’s main concerns (e.g., damp and mould, high bills, cold home, etc.). This ‘contextual data’ helped the advisors make subsequent sense of the data produced by the sensor kit.
As a result of the first deployment, advisors amended the questionnaire to gather more contextual data in order to better understand the indexical character of sensor data when rehearsing for the advice visit. Thus, a new section was added to the questionnaire to capture property information (age, type and wall type), energy efficiency measures, and specific issues with condensation, damp, mould and draught. It was further amended to capture more detail about the occupants, particularly health conditions exacerbated by cold and damp, such as asthma, and whether or not anyone relied on any electrical medical equipment. The questionnaire was also amended to capture not the just the type of heating system and the make and model of the boiler and thermostat, but also the settings used by the client, whether or not they used a programmable timer and if so how they used it, and whether they were using any secondary heating sources such as electric heaters. Further detail was also captured about the occupants’ cooking habits (e.g., how often they typically used the hob, oven, and kettle), and a checklist to capture any frequently used electrical equipment. The locations sensors were placed in were also noted for future reference.
The questionnaire is an ‘accountability system’. As the following vignettes illustrate, it is not simply used by the advisors to elicit contextual data - not simply a matter of asking questions and noting down responses. Rather, the questionnaire is used methodically to make the IoT system accountable to clients: to articulate reasons for placing the IoT system in the client’s home, and to articulate what the sensors are, the data they gather, what purposes it will be used for, etc. Seen and treated as an accountability system, the questionnaire thus allows advisor and client to mesh their actions together and collaboratively introduce the technology into the home, situate it, and project its subsequent use. The following vignette illustrates how in the course of administering the questionnaire the IoT system gets introduced into the home.
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Vignette 1. Introducing sensors into the home
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Introducing sensors into the home
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A: Okay, so I think they [previously] asked about whether you find that you’re cold? That your home is colder than you’d like it to be sometimes in winter?
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C: Yes.
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A: And that there were some difficulties with damp and mould?
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C: Yes, in our bedroom.
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A: Is it mainly the bedroom or is it in other rooms?
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C: No, it’s just our bedroom.
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A: Just the bedroom, OK.
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C: And like in the window and all over the ceiling.
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A: OK. We can put the sensors in. They’re very good for seeing how humid the room gets and the temperature, and indicate whether it can lead to damp and mould. So we can kind of test that in the room for you?
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C: Yes.
As the above vignette makes visible, the introduction of sensors into the home is ‘occasioned’ (Zimmerman and Pollner 1970), and occasioned in a number of ways that make the introduction accountably reasonable. Thus, we can see that the current interaction between the advisors and client is occasioned by prior contact between the client and CSE, which warrants the advisors being in the client’s home ‘here and now’; the warrant being that “your home is colder than you’d like it to be” and that there are “some difficulties with damp and mould”. It is this warrant that occasions the elicitation of contextual data and results in the articulation of a specific problem “in our bedroom”. This, in turn, occasions the proposal to “put the sensors in” and “test” how “humid the room gets and the temperature”, both of which “can lead to damp and mould”. In home after home we see the same methodical procedures at work in the articulation of contextual data and the occasioning of warrants, problems, and proposals making the introduction of the sensing kit an accountably reasonable thing to do.
We also see how elicitation of contextual data enables advisors and clients to collaboratively situate sensors in home after home. Thus, as the following vignette makes visible, occasioning the introduction of the technology into the home enables advisors and clients to work out just where to situate sensors in the home.
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Vignette 2. Situating sensors in the home
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Situating sensors in the home
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Client leading advisor and researcher upstairs to bedroom; advisor continues to elicit contextual data:
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C: [Enters bedroom] Excuse the mess.
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A: You can smell the - kind of mouldy - the dampness.
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C: All the clothes around are ready to move because we can’t put them in the cupboard anymore; because it’s all just like that in cupboard
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R: Oh gosh! OK.
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C: So excuse (…)
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A: Is it alright if I take a photo of (…)
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C: Yes. You can see it’s quite bad in here.
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A: Have you tried wiping it down at all?
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C: Yes. I quite constantly keep wiping it. The window is normally open as well, but the house is cold to keep the window open. I don’t know if you want to put it up on top of the wardrobe?
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R: Yes.
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C: Or on top of there, whichever one. Just put it there or something.
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R: We’ll just pop it up there, is that alright?
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C: Yes, that’s fine.
It is clear, then, that sensors come to be situated in specific locations with reference to the particular problems that occasion their introduction into the home; that there is a reflexive relationship between the articulation of problems, which warrant proposals being made to introduce sensing into the home, and the actual placement of sensors. A sensor is not, and cannot, be placed just anywhere. Rather, there is a close coupling between just where a sensor is placed and the reasons that accountably motivate its introduction. It is also plain to see that in the course of situating sensors advisors seek to understand clients’ problem management practices (e.g., constant wiping down). There is more to eliciting contextual data than simply filling in a form then, and there is more to situating sensors than coupling them to problems. Sensors are also placed to help the advisors understand the impact of domestic routines on the home and the client’s problem(s). Thus sensors are placed in locations that enable the advisors to understand occupancy patterns (through CO2 sensing), heating patterns (through temperature sensing) and the impact of routine activities such as cooking and bathing (through humidity sensing), etc.
The placement of multiple sensors around the home introduces a degree of complexity into the situating of sensors, as the following vignette elaborates:
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Vignette 3. Checking the sensor kit
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Checking the sensor kit
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A: Right, we’ll check now if the sensors are all working.
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R: XN in the kitchen is working. XD, upstairs bedroom, that one’s working. JE, above the room thermostat has not sent anything yet.
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A: OK. JD then is above the - on the room thermostat, I think. No, JD’s in the bedroom.
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R: No, XD’s in the upstairs bedroom.
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A: JD is the bedroom one but you’ve got it as XD.
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R: Oh, that’s why. So JD is there, that’s it then, that’s fine. XD is the outside one on the wall (…)
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A: Right.
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R: Great.
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A: So yes (turning to client), it shows the hub is all working and they’ve all sent readings in the last few minutes. So that’s fine, we know that it’s all working.
As the vignette makes visible, situating multiple sensors requires the advisor and researcher, and indeed anyone who might be doing this work, to “check” that the sensors are working. This is done by looking for “readings” on the hub, which turns not only on technical knowledge of sensor communications (e.g., waiting for a refresh) but also on the situated particulars of ‘just this’ installation. Thus, checking that the sensors are working also turns upon pairing readings with sensors (denominated by two-letter IDs) and sensors with locations, which as the above vignette shows is occasionally problematic. The problem is resolved by working through the mapping and matching sensor IDs to locations.Footnote 2.
Once checks are completed and any issues resolved, the advisor returns to the business in hand and the projected future use of the data generated by the sensor kit:
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Vignette 4. Projecting future use of the data
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Projecting future use of the data
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A: Okay. So, do you know when the 26th - is it Tuesday?
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R: Yes, Tuesday.
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A: Whether you might be available in the afternoon, then we can pop back?
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C: Yes.
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A: It’s sending all the readings now so we can look at the data, particularly around the mould issue.
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C: Yes.
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A: We’ll offer some advice.
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C: Yes.
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A: Then we’ll leave the sensors in for another week. So in about two weeks? Around the 26th of January?
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C: Yes.
Thus, having installed the sensor kit, and checked that it is working, the advisor and client make specific arrangements to “look at the data” and for the advisor to “offer some advice” to the client “particularly around the mould issue.” Eliciting contextual data is more than a matter of simply completing a questionnaire then. When we look to see what’s done in the doing (Crabtree et al. 2012) of filling in the questionnaire, we see that the articulation of contextual data warrants the doing of a technical job of work that methodically provides a) for the introduction of IoT technology into the home, b) for situating it in particular locations with reference and respect to specific problems, and c) for the future use of data generated by the sensing kit to address those problems. We note too, that the improvements made to the contextual data capture instrument are indicative of an effort towards more systematic elicitation of contextual data or metadata. Metadata provides crucial information on the indexical relationship of sensor data to the sites and practices of its production. Without metadata then, it would be very difficult for advisors to make sense of the data, or to use it in a meaningful way within the subsequent provision of energy-related advice.
Rehearsal
The next stage of data work centres on ‘rehearsing’ the collected sensor data in preparation for the advice visit. Rehearsal involves reading through the data to identify distinct patterns, which is done methodically in searching for ‘peaks and troughs’ in the data; speculating on the causes of these observable phenomenon, which draws on technical knowledge (e.g., of normal and abnormal heating cycles), local knowledge furnished through collection of contextual data, and common-sense knowledge; and annotating these data for discussion and verification with the client (Fischer et al. 2016). Three advisors took part in a workshop to work through the collected data and prepare themselves for the advice visits. The workshop served as a further site for observing how IoT sensor data is articulated and made sense of. The same basic jobs of work apply as reported in our previous study (ibid.) – i.e., identifying patterns, leveraging different bodies of knowledge to speculate on their causes, and annotating the data in preparation for the advice visit – but they are now accompanied by new methods for working with complex, multi-sensor datasets; methods that initially revolve around “getting an idea” of what a complex dataset produced by multiple sensors might be telling them:
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Vignette 5. Getting an idea: identifying remarkable patterns
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A: So what I would do first is to just look at the temperature data to begin with. So just to try and simplify first of all so we get an idea. [Filters out temperature data].
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A: Bloody hell, there’s some big fluctuations there. So the blue one at the bottom is the external [temperature]. Outside it goes down to nearly 0 up to 10. Inside some of the rooms are actually going down - above the room thermostat is going down - to 10 degrees. 10, 11 degrees is the lowest temperature. That’s actually quite low.
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A: So then what I do, I make some rough notes from that [temperature data] - if there are any particular periods to have a look at, or any peaks or troughs that need a bit further investigation. Then I look at the humidity. This is me just trying to find a way to kind of work through things really. So the external humidity, 70 up to maybe 90 the whole time over the last few weeks. In the different rooms it’s quite variable. Overall it’s not steady - problematic humidity given for all of the rooms, they are quite fluctuating; of all the rooms the upstairs bedroom is the most humid.
The vignette makes it visible that the advisors first need to “simplify” multi-sensor datasets to identify remarkable patterns, such as temperature or humidity fluctuations, and thus “get an idea” of what the problems are in a particular home. The simplification is done methodically by filtering out the noise created by multiple sensor feeds, focusing down on single data sources, and noting down remarkable occurrences (e.g., that the temperature is quite low, or that humidity is variable and problematic).
The advisors may then turn towards “correlating” multiple data sources to further elaborate energy-related patterns in the home:
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Vignette 6. Correlating multiple data sources
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A: In the lounge they’ve got the humidity, temperature and CO2 showing. They might have been away then because the CO2 is fairly quiet, it’s almost stable just there; not a lot happening. So there, the CO2 goes up. There. Temperature up there. So you’ve got roughly CO2 peaking with the temperature increases and it’s - just put in the gas use as well now, and turn off the humidity for a moment. Just trying to see if the CO2 and temperature coincide with gas use in terms of the heating, which - so yes, there’s a gas increase slightly behind the temperature increase. So that, yes, seems to correlate. And that one there where you’ve got the temperature staying high and then the CO2 is remaining quite high for a while.
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R: And that’s yes, sort of evening time, isn’t it?
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A: Yes.
As this vignette makes visible, the correlation of multiple data sources enables advisors to infer particular patterns. That, for example, the inhabitants of the home have “been away” from home because CO2 readings are “stable” and it’s plain to see that there’s “not a lot happening”. Conversely, correlating multiple data sources enables advisors to infer that and when people are at home as the reading starts to peak alongside a “temperature increase”, an occupancy pattern that is confirmed by “putting in the gas” to see if it “coincides” with CO2 and temperature, which it does. Taken together, single data sources enable the advisors to identify particular classes of problem in the home, and multiple data sources allow them to infer the patterns of human action that are implicated in their production. Thus, and for example, the correlation of multiple data sources enables an advisor to identify issues implicated in remarkable patterns seen through a single data source (such as occupancy and temperature fluctuations).
A further methodical feature of data work is found in the way that advisors earmark remarkable patterns and associated data for discussion in the advice visit. This was one of the main practices we sought to support digitally by means of the interactive system. However, this has turned out to involve not just the interactive system, but additional notes on paper, as this advisor explains to a colleague.
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Vignette 7a. ‘Noting’ remarkable patterns
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A: So, I’d probably go about putting in a note there (pointing at screen, Figure 3), annotating it as an area to explore with them [the client]. And I would try to use a kind of notation system, writing down an actual note on paper with the time and roughly what I would say. Just so I know where to go back on here (pointing at screen) - an indication, like as simply as possible.
‘Noting’ remarkable patterns and associated data is done methodically through the use of an ad hoc ‘notation system’. Thus the advisors have come to exploit the interactive system alongside paper, which is used to index or signpost the particular bits of data they want to discuss with the client. The notation system thus helps them locate relevant sections of data in the digital system. When prompted to elaborate this ‘notation system’, the advisor did so by example:
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Vignette 7b. ‘Noting’ remarkable patterns (cont’d)
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A: On this one [points at laptop] - we’re looking at Saturday 16th to Monday the 18th - I’m writing down the actual times of the period I’ve selected on the screen, and I’m going to call it an Occupancy [writes note on piece of paper]. If I think there’s going to be more than one, I’d give them a number. So I’m calling this ‘occupancy one’, just to try and make it simple. [Puts pen down and adds an annotation in the interactive system - Occ1: low C02 and all temp. Out for the day? (see Figure 3 inset)] So yes, low CO2, and all temperatures. And so what I do is I write on my home visit sheet [i.e., the piece of paper] what I’ve selected - so all temp and CO2 - so I know what to select to show the person: were they away? So now I know there’s something there [in the data].
The notation system involves both digital and physical annotations. The former is provided for through the interactive system. The latter is provided for through a bespoke accountability system: the “home visit sheet”. The sheet not only indexes the digital annotation, making it easy to locate specific parts of the dataset during an in-home visit and associated queries, it also lays out and defines an order of business to be addressed during the home visit (occupancy one, two, three, and so on). Thus, the home visit sheet is a coordinating device used methodically to order interaction between advisor and client. It provides a situationally-specific schedule of work that parses the overall dataset produced by the sensing kit and surfaces particular issues that need to be worked through with the client.
The advisors call the parsed dataset “reference data”. Reference data is data extracted from the overall dataset that the advisors deem to be relevant to understanding and addressing the client’s problem in some way, whether it be identifying remarkable patterns that articulate particular problems, such as temperature and humidity fluctuations, or raising queries about data that stand in need of clarification, such as the causes of low C02 and temperature data. The preparation of reference data for the advice visit involves the use of a “checklist”:
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Vignette 8. Assembling reference data
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A: I looked at the data yesterday and, using [the] checklist, I just went through some things with the data. Checking the maximum and minimum temperature recordings, looking at the differences in rooms, the variations between rooms, any sort of fluctuations within rooms, temperature patterns, anything sort of out of the ordinary.
The “checklist” formalises a professional practice for looking at the data, orienting advisors to “anything out of the ordinary” - maximum and minimum data points, differences, variations, fluctuations in and between rooms, etc. Finding “anything out of the ordinary” turns upon the various orders of knowledge and reasoning that the advisors possess and exploit in looking at and reading the data.
It is in the interplay between looking and reading that the advisors come to find things that are out of the ordinary and in turn, as the following vignette makes visible, formulate potential solutions to particular problems:
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Vignette 9. Formulating potential advice
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A: (…) relating it back to the initial inquiry, which was that her home was colder than she’d like in winter, she sometimes struggles with her energy bills. I’ve got some sort of tips on using her heating system more efficiently, like making use of the room thermostat. I don’t think she does at the moment. Possibly turning down the heating in the back bedroom, because that’s often above 21. Like it goes up to 25 on quite a few occasions. I don’t know if it’s just because it’s a small room.
Formulating potential advice turns upon sensor data (e.g., temperature readings), technical knowledge (e.g., of normal and abnormal heating cycles), common-sense knowledge (e.g., that abnormalities could, in this case, be caused by small room size), and contextual knowledge (e.g., of the client’s problems and practices). The combination of sensor data with these different orders of knowledge and reasoning enables advisors to go beyond offering general advice and provide situationally specific advice instead. Thus, and for example, an advisor can offer a client “tips” on using the heating system more efficiently such as “making use of the room thermostat”. Such fine-grained tips are provisional, however. They may resolve the client’s problems, but whether they do so or not has yet to be ratified.
To sum up, the ‘rehearsal’ stage of data work involves articulating problems and their potential solution. This is done by simplifying the dataset to identify ‘remarkable’ patterns in the data and correlating data sources to identify issues potentially implicated in their production. The work turns upon the use of technical, common-sense and contextual knowledge and the design and use of methods for ‘noting’ remarkable patterns and assembling ‘reference data’. Reference data parses the overall dataset and surfaces ‘anything out of the ordinary’, which enables advisors to formulate ‘tips’ that may resolve the problem situation. Reference data is indexed through the production of a home visit sheet, which allows advisors to quickly locate relevant data and defines a situationally-specific schedule of work ordering subsequent interaction between advisor and client in the performance of data work.
Performance
The final stage of data work is methodically occupied with the ‘performance’ of data during the advice visit. This involves advisor and client articulating the remarkable patterns identified during rehearsal and pre-visit speculations as to their causes. The work here remains the same as detailed in (Fischer et al. 2016) and sees advisor and client articulating the relationship of data to problems, and tying problems to the client’s activities, practices and routines. In this way advisor and client verify or respecify pre-visit speculations, shape solutions around domestic priorities, and articulate future energy-related practices and their benefits. While our prior studies have shown how simple line charts are drawn upon as a collaborative resource supporting tailored advice-giving, data work now revolves around the interactive system and the articulation of multiple data sources earmarked on the home visit sheet. The following extract, drawn from the advice visit to the home with damp and mould problems encountered in vignette 1, provides an exemplar of the ways in which multiple sensor data is drawn upon to articulate problems and solutions.
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Vignette 10. Articulating problems and solutions with reference to multiple sensors
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A: Let’s just have a look at the temperature to begin. You see this is - all the different colours? So the purple one is the living room, and then the grey one the kitchen. The kind of olive coloured one is the upstairs bedroom (…)
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C: Yes.
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A: and in the hall is the pink one. So you can see they’re all quite similar in terms of the pattern (…)
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C: Yes.
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A: which is good. So you’re just popping your heating on then it’s just going through the house, which is great. It’s not like one room is particularly colder than the other. If we look at the - this shows, on this side, the average temperatures. The upstairs bedroom is the room with the mould, isn’t it?
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C: Yes.
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A: So we can see that on average, the highest temperature is about 20, the lowest is just above 11, and the average is 16 (…)
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C: Yes.
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A: In general the World Health Organisation recommends about 18 to 21 for health. So really you need to heat a bit more. What it’s showing is that the temperature is at 16, humidity [inaudible] high. You’ve got the condensation and mould up there, so increasing the temperature a little bit is going to kind of help to decrease that.
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C: Yes.
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A: I appreciate like maybe you haven’t got the money to heat a bit more, but we can maybe have a look at things that might be able to help you.
This vignette makes visible the methodical way in which advisors go about articulating a complex dataset produced by multiple sensors. Just as in rehearsal they filter the dataset to focus down on a single data source “to begin” with, though this time the filtering is driven by items earmarked on the home visit sheet. In this particular case the data source is related to the primary problem that affects the client’s home: temperature, which is indicative of potential damp and mould problems. The vignette also makes it visible how in articulating the data from a single source advisors explain what the data visualisation “shows”, thus offering an orienting description – “the purple one is the living room, and the grey one the kitchen… olive one the upstairs bedroom,” etc.
The data allows the advisor to articulate particular patterns (e.g., average temperatures throughout the home), and in turn to assess these (e.g., that they are “good”), and to relate them to normal expectations (as prescribed, for example, by general health guidelines). Visible discrepancies between the two enables the advisor to propose potential remedial action (e.g., “to heat a bit more”). Multiple data sources are introduced into the interaction to drill down into the problem (e.g., humidity in the upstairs bedroom) and drive home the advice: temperature is low, humidity high, so increasing the temperature is going to decrease the humidity levels. While multiple data sources enable deeper articulation of the client’s problems and their causes, this vignette also makes it plain to see that the advice they enable may also be problematic: e.g., increasing the heating in a low-income household.
That is not to say that nothing can be done about a client’s problem and advisors routinely go beyond data work to work through potential solutions (Fischer et al. 2016). In this particular case, the advisor and client left the data behind and went to inspect the damp and mould in the upstairs bedroom. In doing so the advisor articulated a range of options for managing the problem, including frequent wiping down, leaving the window ajar to increase ventilation, opening the curtains to let the sun warm the room, putting a reflective panel behind the radiator to increase heating efficiency, having the landlord check the roof insulation above the problem area, and turning the heating off when no one is in the house (a pattern also made visible by multiple data sources, particularly temperature, gas and CO2).
Overall, what is evident in the fieldwork observations is that the articulation of complex multi-sensor datasets frames and guides advice giving in important respects. In order to deal with the complexity of these datasets, advisors filter down to single data sources related to items earmarked for discussion and in doing so reflexively articulate and explain what the data is about and what it shows the client. Perceived problems are initially articulated with reference to a single data source, with multiple data sources being subsequently drawn upon to drill down into and elaborate problems. While client’s problems are often obvious, in that they already know what they are, multi-sensor datasets allow advisors to understand and articulate their causes (e.g., that low temperatures and high humidity are at the root of a particular damp and mould problem) and offer tailored advice to remedy the situation. The tailoring of advice is done with respect to the client’s circumstances and may also be informed by multi-sensor datasets, which is to say that such data is not only drawn on to articulate problems but also to articulate viable local solutions.
Reflective workshop
After the sensor kit had been collected from clients’ homes, we conducted a reflective workshop with the advisors to understand their perspectives on the CharIoT system, how it fared within their work, and which features they found most supportive and useful for facilitating energy advice. When discussing the feature set of the interactive system, the advisors emphasised how they had used the system to highlight critical issues relating to health. As one advisor put it,
“ … flagging issues - being able to look at these graphs and immediately identify, you know, where homes are being under heated, and therefore potentially causing a health hazard.”
The advisors found value in the interactive system and appreciated the ability it gave them to “immediately identify” and “flag” potentially problematic issues.
The advisors also appreciated the use of multiple sensors, as this enabled them to compare rooms to one another and drill down into problems:
“The fact that we’ve got y’ know a number of sensors around the home rather than just one has been really beneficial as well. So some rooms are heated to 15 degrees and others are at a regular temperature. That room that’s heated to 15 degrees, in one instance that’s flagged repairs, things that need to be fixed, broken thermostatic radiator valves; or they’ve flagged you know just poor control where as a result of not heating that room to the temperature that it needs to be, you’ve got, you know, damp and mould issues.”
The use of multiple sensors has been “beneficial” to professional practice, enabling the advisors to both identify material problems with the home’s infrastructure (e.g., things that need to be fixed) as well as issues related to the use of that infrastructure (e.g., poor temperature control).
The advisors furthermore pointed out that the process of collecting and using sensor data made their role as experts more credible to their clients, and increased their clients’ engagement with the business of giving energy-related advice:
“ … being able to present that to householders and show them the relationship helps them engage with the issues … In previous visits people don’t necessarily believe what you’re saying, so being able to point that out …”
The perception that the sensor kit and interactive system helped clients engage with issues and “believe” what the advisors were saying underscores findings from our previous work, which suggests that IoT data can and does play a constructive role in building a trusting relationship between advisor and client.Footnote 3.