Our why as a corporation is evident: to struggle for privateness, selection, and sustainability for good houses, and for each one who lives in a single. However who does stay in them?
In December 2024, we launched the primary House Assistant survey to seek out out. Our objective was easy: to make House Assistant extra inclusive and approachable by listening on to the various neighborhood of people that use it.
Within the spirit of constructing within the open, at present we’re thrilled to announce the anonymized outcomes of that survey are actually freely obtainable. On this put up, we’ll run you thru what the survey coated, why and the way we’re publishing this information, what the info will not be (learn: identifiable), how we’re utilizing this info to enhance what we do, and alternatives for additional understanding and analysis.
TL;DR
- We ran our first House Assistant neighborhood survey in December 2024, gathering 8,616 responses on how individuals arrange and stay with their good houses.
- We have now now revealed the anonymized outcomes on Zenodo, after a strict anonymization course of – together with k-anonymity overview, GDPR compliance, and reducing 12% of responses as a precaution.
- This information is already shaping our work, from redesigning “areas” in House Assistant to tailoring the House Assistant Join ZBT-2 and informing our privateness analysis.
- The survey had limitations (it ran too lengthy, and was English-only), which we’re addressing in future analysis.
- Discover the info your self on Zenodo, or the total set of analysis questions and methodology on our GitHub web page.
A snapshot of the survey
To provide somewhat context, the survey ran for about seven weeks, closing in early February 2025, and gathered 8,616 submissions – providing significant insights into House Assistant customers and the day-to-day realities of operating the platform. Most respondents mentioned an curiosity in expertise drove them to create a sensible residence, typically prompted by transferring into a brand new home, or a renovation. Solely round a 3rd began their good residence with House Assistant itself, with the remainder arriving from different platforms. Maybe unsurprisingly, amongst those that selected House Assistant, probably the most generally chosen causes had been native management (89.3%), its open supply nature (84.0%), and highly effective customization (81.7%).
Past how individuals received began, we needed a fuller image of who they’re and the way they stay. The standard person within the examine ran a considerable setup, managing anyplace between 11 and 200 gadgets, and most of the people (75%) did so in a house they owned, and infrequently throughout two or extra flooring. Over half of the households surveyed had a resident cat or canine, and 1 / 4 of respondents spoke multiple language at residence.
We additionally requested about skilled background and neurodivergence, since understanding the vary of experience and wishes throughout the neighborhood shapes how we talk and design: 78.3% of major customers labored or studied in expertise or engineering, and 15.7% recognized as neurodivergent, in contrast with 68.1% who recognized as neurotypical, and 12.3% who had been not sure.
These are only a handful of the fascinating issues we found through this work, and we’ll point out some others all through this put up. For the total particulars, you will discover the entire checklist of analysis questions, methodology, and different supporting paperwork on our GitHub web page.
Why publish the dataset?
Sharing the data now we have to assist open supply good residence analysis is an apparent transfer. A wealthy dataset is a robust device, and one we imagine everybody ought to have entry to. Publishing the outcomes helps different researchers by permitting them to construct on our work, and means a better variety of pondering and creativity could be utilized to the info. It additionally makes analysis extra reliable as others can examine the info behind the examine as an alternative of counting on conclusions, and progress is quicker when researchers don’t must re-collect the identical information from scratch.
A part of our mission is to tell society about open supply, moral options to Large Tech good residence gadgets. And a technique we do that’s by conducting analysis that generates data, and sharing findings publicly so everybody can study from them. In doing so we’re aligning our group with the ethos of open science: the strategy to scientific publication that believes all publicly helpful analysis and its information ought to be overtly obtainable for all to test, construct on, and profit from – not locked behind journal paywalls. That’s why we’ve revealed on Zenodo, a trusted and broadly used open analysis repository constructed and run by CERN, a global scientific group and pioneer in open supply, open entry, and open information.
Simply as we’d in any formal analysis setting, we maintain ourselves to this excessive commonplace, guaranteeing our strategies and findings are rigorous, verifiable, reproducible, open, and truthful.
Championing FAIR information
To be clear, making information technically obtainable on-line isn’t the identical as sharing it pretty. That’s why we comply with the FAIR information framework – a broadly used commonplace that ensures revealed information is:
- Findable: the dataset has a everlasting hyperlink (a DOI) so it may possibly all the time be situated and cited.
- Accessible: anybody can obtain it, no gatekeeping.
- Interoperable: it’s in an open, commonplace format that any device can learn, with a codebook explaining each column.
- Reusable: it comes with clear documentation and an open license so others know the way they’re allowed to make use of it.
FAIR information is what makes info genuinely helpful to a researcher, as an alternative of simply seen. However earlier than we get into how we’re utilizing this information, let’s take a look at what the info isn’t, and what we did to make sure something shared was in step with our ideas.
Our publishing strategy: Privateness first
As a basis, we worth privateness a lot that it’s one in all our three core ideas. So it ought to come as no shock that we adopted a strict protocol to anonymize all information earlier than publishing: no particular person responses, no accounts, no identifiable individuals. Right here’s a full rundown of all of the actions we took to guard person privateness:
We eliminated any info that would level to an individual
Names, technical identifiers, timestamps, free-text feedback that would comprise private particulars, and delicate info pertaining to well being, incapacity, faith, or different family members (for instance, companions and youngsters) had been all eliminated. The truth is, we deleted a lot figuring out info that the shared dataset is not thought of “private information” below the Basic Knowledge Safety Regulation (GDPR) – a key European privateness regulation.
We made positive nobody stood out by means of k-anonymity overview
Eradicating apparent identifiers isn’t sufficient. Generally a uncommon mixture of unusual particulars (equivalent to a long-time person with an uncommon setup) might make somebody stand out. That’s why we used k-anonymity overview – a knowledge anonymization approach that checks whether or not sufficient individuals share the identical mixture of traits that no particular person could be picked out from the group. We ran validated assessments that appeared for precisely this. The place wanted, we grouped solutions into broader classes: for instance, describing somebody as utilizing “5–6 protocols” moderately than itemizing their precise protocols. This ensured every respondent blended in with many others moderately than being distinctive.
When doubtful, we minimize the info
In any case these steps, a small variety of responses nonetheless had uncommon sufficient mixtures that we couldn’t be absolutely assured they’d mix in. Slightly than take any possibilities, we merely eliminated them from the dataset. Ultimately we omitted about 12% of responses (roughly one in eight) purely as a precaution. We might moderately publish much less and make certain than threat the privateness of our customers.
We checked it legally
Our strategy was iteratively reviewed by our authorized advisors, and each choice and step is recorded so it may be independently examined.
We revealed supplementary materials
Together with the dataset, we launched further documentation so our actions could be adopted, together with:
- Anonymization protocol and k-anonymity overview.
- README file documenting particulars concerning the survey and the dataset.
- Anonymization and k-anonymity evaluation scripts.
- Full survey query checklist (though not each column on this checklist is included within the dataset because of privateness, the survey construction itself is efficacious for different researchers).
- Descriptive statistics, masking response distribution for each closed-ended query within the survey.
At each choice fork, we selected the extra cautious choice, and have deliberately made all our processes clear. In any case, the entire level of this survey is to study with and from our neighborhood, and for us that doesn’t cease on the final query. Which brings us to what the info revealed, and the way it’s already shaping our work.
Understanding social dynamics within the good residence
Our survey outcomes confirmed that most individuals who responded stay in a sensible residence with a companion or household. In that context, we discovered that it’s usually one individual – 94.4% of whom are males – who units up and maintains the good residence, whereas their companions (87.4% of whom are girls) and any youngsters within the family work together with linked gadgets moderately than by means of House Assistant itself. In different phrases, one individual tends to configure and keep the system, whereas everybody else merely lives with it.
This survey information aligns with earlier analysis on social dynamics within the good residence. For instance, research of multi-user good houses have discovered that whoever installs the gadgets tends to finish up with outsized management over how they’re chosen, managed, and stuck. In some instances, this may result in discomfort round privateness because of this – each from the expertise itself, and from a way of being noticed by the individual managing it (pointing to the potential for this sort of management to be misused), as research by Christine Geeng and Franziska Roesner and Nils Ehrenberg and Turkka Keinonen have individually discovered.
Line Kryger Aagaard’s work has proven that the good residence expertise is usually gendered, too. Annika’s personal analysis on cohabitation in good houses explored this straight, taking a look at what occurs when one individual units up and maintains the expertise whereas the opposite lives with it, and discovering energy imbalances that require {couples} to barter management. It’s a dynamic we’re actively working to revamp for (for instance, see ongoing UX design discussions on “House Assistant for the entire family”), not simply between companions however throughout all kinds of relationships within the residence.
How we’re making use of the info
The survey information has already helped deepen our understanding of the person expertise, and the way we optimize it. For instance, info just like the variety of flooring in a house, its measurement, and whether or not it’s owned or rented knowledgeable design adjustments for “areas” in House Assistant. Equally, when our industrial companion Nabu Casa was growing what grew to become the House Assistant Join ZBT-2 to attach Zigbee and Thread gadgets, we offered them with insights from this survey so they may tailor the machine to the wants and precise dwelling situations of our neighborhood.
On a broader stage, the dataset helped us perceive how the neighborhood pertains to the inspiration’s values of privateness, selection, and sustainability – which in flip helped form how we interpreted information inside our privateness analysis, and the way we developed key organizational positions, equivalent to that specified by our Privateness place paper.
Likewise, studying about how present customers arrive at House Assistant – whether or not they begin with it as their first platform or migrate to it from elsewhere – helps us perceive the expansion of the person base, and the way the inspiration can higher assist our neighborhood to advance our mission (hold a watch out for an in depth report on this quickly).
In fact, as with every scientific analysis, the survey has limitations. It’s necessary to call them, each to be clear and since they open alternatives for future enhancements.
Limitations of the survey
The survey was too lengthy. A lot of you pointed this out to us, and regardless of having timed the survey at about 20 minutes throughout assessments, we had been clearly a bit off. In response to Typeform (the survey platform we used), the typical completion time was over 40 minutes! We recognize anybody who began the survey, and are particularly grateful for all who caught with it till the tip. Having such rigorous, engaged neighborhood members is an effective downside to have. However it may possibly additionally skew outcomes, since solely these with the time and curiosity to complete an extended survey are represented. Due to that, and the actual fact the info doesn’t seize responses of those that dropped out of the survey – respondents characterize a dedicated core of House Assistant, not all customers.
As some neighborhood members identified, the survey being solely in English additionally restricted its attain and probably its accuracy. Quite a few House Assistant customers that talk languages apart from English weren’t represented, and the place non-native English audio system did fill out the shape, it’s attainable that some questions could not have landed clearly.
Language boundaries, biases, and blown-out survey instances are all issues that ought to be taken into consideration when deciphering the info. Analysis gaps are regular in academia – we point out them as a result of we’re conscious of the place we will enhance, and to set ourselves up for future analysis that’s as inclusive as attainable. However there’s additionally a chance right here… For those who’re a researcher engaged on good residence expertise in academia or trade, these gaps is likely to be a helpful place to begin on your personal work. For those who’d like to debate this analysis or discover future tasks collectively, attain out to us through e mail or be part of the UX design or person analysis discussions on GitHub. We’d love to listen to from you.
Future analysis
A lot of you shared nice concepts for extra survey questions, masking the established order of good houses, future plans, targets, and desires, different methods of interacting with the neighborhood, and present friction factors inside House Assistant.
The beneficiant suggestions on the content material, framing, and even the styling of the survey from neighborhood members was enormously appreciated, and factors to one of many tensions we bumped into whereas designing this survey within the first place: there’s an excessive amount of to cowl! With so many fascinating matters to discover, narrowing the questions down was a problem, and a giant a part of why the survey ended up so long as it was.
However that’s additionally the great thing about person analysis: there’s all the time a lot to study! For that motive, we’re planning one other neighborhood survey, so keep tuned!
Retaining the dialog going
At its core, this survey was about listening: changing our guesswork together with your details and opinions, so we will construct House Assistant for many who truly stay in a sensible residence, not simply who we assume does. Publishing this dataset overtly is one other manner of placing our ideas into follow – letting anybody dig into what we’ve discovered for the advantage of everybody.
None of this could exist with out the 1000’s of you who took the time to reply, and caught with a survey that ran longer than we meant. Thanks. Right here’s to extra open, trustworthy conversations with the neighborhood that makes this all attainable.
