Housing Technology interviewed data experts from Asprey Solutions, Everon Group, IoT Solutions Group, IRT Surveys, Lashan Digital, Mobysoft and Plentific on end-user engagement and data literacy, data governance, integration and data standards, and how to achieve better data quality.
The best place to start
Viswa Gogineni, senior enterprise architect and founder of Lashan Digital, said, “The most useful change most housing providers can make is to stop treating data management as a series of one-off projects and treat it as a permanent organisational discipline, the same way they treat finance or health and safety.
“Each major data domain needs a named business owner, core terms must have a single agreed meaning across the organisation, and policies on data retention, access and lifecycle must be set by the business, not IT.”
Emily Shaw, senior director and head of product at Plentific, said, “The sector needs to stop treating data as a reporting problem and start treating it as an operational problem. Too many organisations are still trying to reconcile data after-the-fact; if data isn’t captured correctly during operational workflows, it won’t be reliable.”
Emma Mahy, founder and CEO of IoT Solutions Group (IoTSG), said, “Start with your culture, not technology. Data quality is everyone’s responsibility, from repair operatives updating contact records to a director signing off an asset investment. Build habits of ownership into daily workflows, establish what the master data-source is for each data type, and make accuracy a shared standard, not an IT problem.”
Pasi Oksa, group chief technical officer of Everon Group, said, “The most effective starting point is to treat data as a strategic asset rather than as a by-product of systems. That means moving away from fragmented, on-premise platforms towards more integrated, cloud-based environments where data can be accessed, shared and updated in real time. This allows housing providers to combine information from multiple sources, such as housing management systems, telecare and resident services, and turn it into actionable insight.”
Are data standards right for you?
Marius Buragas, technical director at Asprey Solutions, said, “External standards such as HACT and Open Data Exchange provide useful reference points, particularly for organisations that haven’t yet established a clear internal data structure.
“However, adoption across our sector remains relatively limited, with many housing providers continuing to work with data models shaped by legacy systems and historical practices. Greater consistency in core terminologies, such as around assets and components, would reduce misunderstandings both within and between organisations.”
Chris Woods, client development manager at IRT Surveys (part of the Mears Group), said, “Adopting external data standards improves consistency, comparability and credibility. This facilitates reporting and benchmarking across the sector and reduces ambiguity in definitions, particularly for regulatory and ESG reporting.
“Data standards can also improve integration with housing providers’ partners and contractors, while providing a recognised framework that gives confidence in the reported data. Data management should focus on building trust, evidence and usability, not just structure.”
Jon Gill, head of data and analytics at Mobysoft, said, “External data standards are becoming more important as organisations rely on multiple systems and partners. They provide a common structure that makes integration and comparison easier. Although they aren’t a complete solution, they do help to create more joined-up and scalable data environments.”
Plentific’s Shaw said, “While data standards are helpful, their adoption will be a challenge to scale across social housing. Unless data standards are embedded into housing providers’ systems and enforced in their daily processes, they don’t change outcomes.
“The focus should be less about agreeing standards and more about making them unavoidable in practice by baking them into housing providers’ core workflows.”
Data lakes, architectures & platforms
IoTSG’s Mahy said, “Data lakes are only relevant to housing providers with the analytical maturity to use one; a data lake without governance is just an expensive swamp.
“For most housing providers, the priority should be integrating their existing systems cleanly and building dashboards that drive action. Scalable cloud platforms achieve most of what a data lake promises, but with far lower overheads.”
Everon’s Oksa said, “Advanced data architectures such as data lakes can play a role, particularly where organisations are managing large volumes of diverse or real-time data from connected systems, but they aren’t a prerequisite for improvements.
“For most housing providers, the priority should be ensuring that their core data is accurate, accessible and usable. And as organisations adopt more cloud-based services, the focus should remain on enabling data to flow smoothly between systems.”
Asprey Solutions’ Buragas said, “Data platforms, including data lakes, can offer value but only when the underlying data is already well understood and properly managed. Without that foundation, there is a risk of simply centralising poor-quality data and increasing complexity.
“Most organisations gain more immediate benefits from developing reporting layers focused on their key datasets before moving towards broader platform solutions. These approaches tend to deliver the greatest value when data ownership is clear, quality is improving and continuous data management is embedded within day-to-day operations.”
Lashan Digital’s Gogineni said, “A data lake on its own is just cheaper storage for raw data, so the real question is which architecture best fits your organisation.
“For most housing providers, a lakehouse is one of the most practical starting points because it’s relatively straightforward to run and offers a good balance of flexibility and control. Data meshes or data fabrics can give departments more autonomy but they’re more complex and expensive.
“However, regardless of your chosen data platform or architecture, the organisational layer (such as data definitions, ownership and policies) needs to be in place before the platform is built otherwise the new platform will just inherit the same inconsistencies, albeit running on newer technology.”
Mobysoft’s Gill said, “Data lakes can be valuable, particularly for unstructured or high-volume data, but without good data quality, clear definitions and proper governance, they can become difficult to manage and add little value.
“For most housing providers, the higher priority should be getting their core systems, processes and data standards sorted out first. Once they have those foundations, a data lake or similar solution might be worth considering.”
Is your data good enough?
IoTSG’s Mahy said, “Embed data verification into your existing processes rather than treating it as a separate exercise. For example, your customer-facing staff should always be prompted to confirm contact details, household composition and access preferences at natural touchpoints, not through annual data-cleansing campaigns. And don’t forget that simple system rules which flag incomplete or stale records do more for data quality than any audit.”
IRT Surveys’ Woods said, “Housing providers need to address how their data is created, maintained and used. That means defining clear rules for key data fields, improving data capture through validation, standardisation and reduced duplication, and establishing consistent identifiers for key entities, such as properties, residents and assets.”
Lashan Digital’s Gogineni said, “Most data-quality work in housing is reactive; a problem appears in a board report or regulatory return, someone fixes it, but the root cause remains unchanged. Improvements come from making data quality an ongoing responsibility instead of an incident response.
“Name an owner for each key data domain, agree what ‘good’ looks like and put visible measures in place and review them regularly. Where possible, fold those measures into existing operational reports such as for repairs, voids and compliance. Quality has more weight when it sits next to the metrics the business already acts on; quality tends to drift if it’s not visible to the people whose work depends on the data.”
Plentific’s Shaw said, “First of all, stop the typical cycle of ‘capture badly, fix later’. Data quality improves when data entry is controlled, definitions are consistent and ownership is clear.
“Most data problems aren’t technical, they’re behavioural and structural. If your systems allow work-arounds and no one owns your data, data quality will always degrade.”
Barriers to data integration
Everon’s Oksa said, “Legacy systems remain the major barrier to data integration, particularly where the platforms weren’t designed to integrate or share data easily. This is often compounded by inconsistent data structures and organisational silos, with different teams managing data in their own ways. As a result, data often exists in isolation, limiting housing providers’ ability to generate real-time insights across their wider ecosystems.”
Mobysoft’s Gill said, “Legacy systems and inconsistent data definitions are the most common barriers. Even where systems are connected, differences in how data is recorded can cause problems. Addressing this requires both technical work and better alignment between teams.”
Asprey Solutions’ Buragas said, “The most common barriers are linked to the (in)flexibility of existing systems and suppliers, particularly in housing environments shaped by mergers. Closed or restrictive supplier ecosystems can limit integration capabilities, while unclear data ownership can make it difficult to coordinate a coherent approach.”
IRT Surveys’ Woods said, “The main barriers to integration are usually organisational. Inconsistent data definitions across systems can create confusion and misalignment, while fragmented technology landscapes lead to duplication and conflicting data.
“Furthermore, poor data identifiers make matching records difficult and vendors’ limitations, such as formats and cost, can restrict integration. Other common barriers include the lack of end-to-end ownership of data across business processes, an inability to demonstrate data performance and security, and data-sharing concerns that hinder progress.”
Stronger data governance
Lashan Digital’s Gogineni said, “Data governance is mainly about clarity and accountability, not tools. The foundation is knowing what data the organisation holds, who owns each major domain and who has decision rights when definitions conflict. Once you have that structure, tools such as data catalogues and quality dashboards become useful, but without that structure, they just result in documentation that no one follows.
“Wherever possible, run your data governance through the existing forums where strategic decisions are already being made, such as the board, executive team or operational committees. A separate data-governance group, sitting apart from those forums and without the authority to make decisions stick, usually ends up generating reports and minutes that nobody acts on. Keep it practical: catalogue your important data, assign owners and define your core policies.”
IoTSG’s Mahy said, “Assign clear ownership of specific data domains, not just systems. Your governance also needs to extend beyond your organisational boundaries: if repairs are outsourced, your governance framework should define when contractor data becomes available to you, in what format and who is accountable if it doesn’t arrive.”
Everon’s Oksa said, “Effective data governance is built on clarity, accountability and trust. This includes defining ownership, establishing consistent standards and embedding governance into daily processes rather than treating it as a separate function.”
Asprey Solutions’ Buragas said, “Data governance is often approached as a policy exercise, but it only becomes effective when it’s supported by clear ownership and accountability across the organisation.
“Stronger organisations embed data quality into routine performance management rather than treating it as a separate or periodic activity. Governance also has more effect when it’s directly linked to key risks, such as compliance, tenant safety or service delivery, rather than being viewed as an administrative requirement.”
Getting your end-users on board
IRT Surveys’ Woods said, “When end-users understand how data impacts their work and resident outcomes, engagement follows naturally. This can be achieved by linking data quality to real operational issues, such as missed appointments or repeat calls, as well as by simplifying systems and reducing the need for unnecessary data entry.
“Other ways to improve engagement include clearly defining accountability for data within specific job roles and providing feedback on data quality and its impact on performance. Ultimately, implementing stronger data management means focusing on clear ownership, consistent standards, strong governance and user engagement.”
Everon’s Oksa said, “End-user engagement improves when data is clearly linked to real-world impacts. Front-line teams are more likely to take ownership when they can see how data contributes to earlier interventions, improved outcomes and more efficient ways of working.
“Providing intuitive tools, reducing data duplication and feeding insights back to users all help reinforce that sense of ownership. When data isn’t just collected but actively used to support decision-making, engagement tends to follow naturally.”
Mobysoft’s Gill said, “End-user engagement improves when people can see how data helps them in their role. Providing relevant insights and easy-to-use tools makes a big difference. When people trust the data and understand its value, they’re more likely to take responsibility for it.”
Plentific’s Shaw said, “You don’t get engagement by asking people to ‘care more’ about data. You get it by designing systems where good data is the default, poor data is difficult to enter and the impact of data is visible in people’s day-to-day work.
“Most importantly, data ownership needs to be explicit. Data improves when people know it’s their responsibility and when the organisation treats it that way. The sector doesn’t have a data problem, it has an operating-model problem. Fix that and the data will follow.”
Housing Technology would like to thank Marius Buragas (Asprey Solutions), Pasi Oksa (Everon Group), Emma Mahy (IoT Solutions Group), Chris Woods (IRT Surveys), Viswa Gogineni (Lashan Digital), Jon Gill (Mobysoft) and Emily Shaw (Plentific) for their editorial contributions to this article.
