Housing Technology interviewed data management specialists from CGI, Civica, FLS – Fast Lean Smart, Insite Energy, Jaywing, Manifest Consulting and NEC Software Solutions on the role of data management in social housing, how to move from bad to good data, cultural and behavioural changes, data metrics and the pitfalls to avoid when implementing data management programmes.
Ben Nduva, director of consulting services at CGI UK, said, “Data is now flowing into housing providers via myriad sources such as social media, tenant apps, call centres, face-to-face surveys and IoT sensors, to name just a few. Ensuring the business is systematically sweating these data assets for all they’re worth can provide incredible benefits. The key message is that data management, data maturity and data quality aren’t just technical problems; the entire organisation has a part to play in embedding the right processes and culture to continually focus on data management.”
Jeremy Squire, UK managing director of FLS – Fast Lean Smart, said, “From a business perspective, data is king and gives housing providers, each of which holds vast datasets, an edge in terms of predictive analytics and reliable insights. More and more housing providers are becoming completely data-driven in order to proactively manage their assets and tenants.
“Housing providers appreciate the value of their business-critical core data, in the form of a golden thread of data which acts as a central source of truth. This golden thread is a singular dataset running through the business, providing a data backbone for people, buildings and operations.”
Inderjit Mund, data management practice director at Jaywing, said, “Data management isn’t important to housing providers – it’s critical. Housing providers need to improve outcomes for tenants, regulators and internal stakeholders, but many housing providers are currently tackling these challenges without strong data management practices. As their business requirements become more detailed and complex, relying on inefficient manual processes will put them at risk of failing to meet both their external and internal obligations.”
Peter Salisbury, director of Manifest Consulting, said, “Of course, no business can function without data, even if that’s just a few customer records on a Rolodex (remember them?). However, housing providers deliver multi-faceted services, and therefore have a significant degree of intrinsic complication that most other businesses don’t have to deal with.
“Consider all of the different internal and external housing functions, each with specific and very varied data requirements – we could run each of these functions in its own silo and just deal with their data needs separately (unfortunately, it feels as if we often do exactly that), but that isn’t the right way to run an effective housing provider. It’s clear that a well-run housing provider is entirely dependent on its diverse datasets being properly maintained in a way that’s accessible and useful to all operational staff.”
Data management and IT
Trevor Hampton, director of housing solutions at NEC Software Solutions UK, said, “There’s no point having good IT systems if your data is poor. When we design IT systems, we start with the data. That involves working out what the database will look like in terms of logic, then implementing controls and checks before putting in the processes and the visualisation. Data must be at the very heart of systems design.”
Richard Shreeve, technical director at Civica, said, “Although data is contained within IT systems, such as databases, file servers and applications, it’s important that data is also viewed as a business asset. It must be owned by the business and not just left with IT to manage, with business owners and data stewards named and empowered to make decisions about that data.
“Typically, data custodians will ensure that data is managed within the IT systems that store, process and exploit it. IT certainly has a significant part to play in good data management, enforcing usage and retention policies, compliance with data protection legislation, profiling data quality and mastering data, but IT can’t be the only invested party within the organisation.”
Anthony Coates-Smith, managing director of Insite Energy, said, “GDPR is crucial here; housing providers must have decent processes to protect themselves and their customers from erroneous uses of their data. It also comes down to how the data is gathered and from there, how it’s made useful to the housing provider.
“For example, will they run assessments using the raw data itself (e.g. via spreadsheets) or will they use a dedicated IT platform to view and digest the data in a way that is more meaningful to them? Whatever route is taken, housing providers should consider how their data will be harnessed into something that can be used, understood, displayed and presented to others.”
Jaywing’s Mund said, “Strong data management should be a core competency of all housing providers. In more data-savvy sectors, such as financial services, data management used to be seen as the domain of IT. While IT clearly has a significant role to play in data management, sector-leading organisations all recognise that the true power of data management lies in the partnership between IT and business functions.”
Data management vs. everything else
FLS’s Squire said, “Data management is hugely important; only front-line services which might affect the health, safety and wellbeing of residents could be considered more important. Data protection, data management and data processing could be sidelined in order to respond to pressing operational issues or emergencies, but the data that housing providers rely on every day is of fundamental strategic importance and operational necessity.”
CGI’s Nduva said, “Taking a holistic, organisation-wide view is the single most important aspect to improving data quality and ultimately demonstrating the value-adding nature of data management.
“Removing silos and ‘shadow IT’ to generate a single view of the truth is the key outcome from getting data management right. The challenge is ensuring that the associated data strategy and cultural change to support it has a suitable sponsor with enough seniority that all service lines of an organisation buy in and support the processes to govern data.”
Manifest Consulting’s Salisbury said, “A well-designed infrastructure is the most important IT aspect because without that, we wouldn’t even have the systems in which to manage our data. Equally, effective governance and business operations are more important than data management per se, because without them we would lack the objectives, strategy, staff and processes that put the data there in the first place.”
NEC’s Hampton said, “If you ask a housing provider what their number one risk is, they would probably say health and safety above data management. The problem with that is how do you know health and safety is your main area of risk if you don’t have the data to prove that’s the case? Data management should be top of the list because without it, you don’t have any insights into your other business areas.”
Benefits of good data management
Jaywing’s Mund said, “Good data management can be a game-changer. It takes raw data and transforms it into accurate, timely information that recipients can trust. The key here is data provenance, which means knowing the origin, lineage and quality of the data. Trusted data allows housing providers to robustly evidence their decision-making and opens the doors to powerful techniques such as predictive modelling. Without good data, the rule of ‘rubbish in, rubbish out’ applies.”
CGI’s Nduva said, “Perhaps ask yourself how often (at home or at work) is the data you need available to you either immediately or at the first time of asking? For most people, the answer to that is probably ‘rarely’. With that in mind, it’s been interesting watching the cycle of hype over AI (in particular the rise of ChatGPT) over the past year because this technology appears to magically present information, found on the public internet, in a more immediately useable way.
“Most housing providers are some way off leveraging machine learning in their data operations, but by getting data management right along with the careful choice of tools such as process automation, housing departments such as repairs and maintenance, rents and arrears, and benefits management can reap immediate rewards from better internal data management. Furthermore, proactive, personal interactions with tenants and colleagues are achievable; you can see this already in the clever ways that some housing providers are using chatbots integrated correctly with their back-office systems.”
Civica’s Shreeve said, “Better data means better insights, which lead to better decisions at the right time. That might be an improved customer experience because queries are answered correctly at the first point of contact or a better matching of housing needs to applicants based on a live view of all housing stock.”
NEC’s Hampton said, “Good data management is tremendously important. Housing providers might be able to see how they are performing at a strategic, KPI-based level, but you need good data management to really go beyond that and understand the problems, identify their underlying causes and decide what actions to take.
“With good historical data, you can even predict what’s likely to happen in the future, such as who is at risk of falling into arrears, which assets are deteriorating and where you need to do preventative maintenance.”
From bad data to good data
Civica’s Shreeve said, “There first needs to be a definition of ‘good’ and then you need to measure ‘where we are now?’ versus ‘where we want to be’, alongside asking questions such as: is all data equal, what do we mean by ‘fit for purpose’, and where are the biggest gains likely to be made?
“This can be part of a data-maturity assessment and gives a baseline from which to gauge progress and to see where your strengths and opportunities lie. This data-quality approach requires data-governance structures to be established in order to formalise the decision-making framework for an organisation’s data, thereby laying the foundations to improve data quality, usage and insights.”
FLS’s Squire said, “First of all, housing providers need an overarching data strategy in order to gain the most from their metadata, including a data-quality mindset where the benefits of better data quality can be demonstrated to all areas of the business.
“In order to clean bad data streams, irrelevant data must be removed, with duplicated data taken out and structural errors fixed. The accuracy of data needs to be validated, along with the completion of any missing data. Above all, moving to a stronger data model demands a cultural shift that focuses on people, processes, technology and executive support.”
Insite Energy’s Coates-Smith said, “In our experience, there are four stages of moving from bad data to good data: understand your ‘as is’ situation; identify the root causes of the bad data; training; and reviewing and re-jigging the processes if needed.”
Manifest Consulting’s Salisbury said, “Don’t make it a project; good data management needs to be embedded in your day-to-day operations. In our experience, data management projects tend to create a lot of activity but often achieve little in terms of long-term, sustainable good practice. Data quality therefore needs to be an intrinsic part of everyone’s role, with all staff empowered and encouraged to correct bad data where they can and report it for checking where they can’t.
“There are some key tasks that housing providers should complete to ensure that they’re keeping their data accurate and useful: agree the location of your master data; make sure your data sources can be aligned easily; ask your staff (they’re your eyes and ears) and report and act on their findings; deal with bad data when you find it; appoint data champions and make data quality a part of everyone’s job; agree a process for reporting bad data; and brief your senior teams to make sure they understand the importance of data quality.”
CGI’s Nduva said, “A key metric we use is defining the value of data, or the service impact from missing data. Highlighting the missed value helps an organisation quickly prioritise its initiatives to drive improvements.
“This should be supported by ongoing data-quality assessments, measured according to the data dimensions of completeness, uniqueness, consistency, timeliness, validity and accuracy, presented as a clear view of the trade-offs associated with making the data available.”
Civica’s Shreeve said, “At a macro level, a standard data-maturity assessment, such as recently standardised by CDDO, will provide a yardstick on data maturity. And at a micro level (people), data-literacy skills are essential, with regular training programmes having dramatic effects. Finally, at the data level, data-quality indicators can be used as part of a framework to track data quality over time and its positive impact on housing operations.”
Manifest Consulting’s Salisbury said, “Once you’ve set up automated reporting tools to look for inconsistencies across data sets, start reporting on the volume of unresolved records that you’re finding in your regular housekeeping reports, while also making those reports available to your executive team in order to raise the profile of data management. However, avoid the temptation to create targets or performance indicators from such metrics since those tend to drive behaviours that can mask the real picture.”
NEC’s Hampton said, “Measure your data quality by seeing how many duplicate or missing records you have. You might know which person lives in a property but do you know who they live with, or how their family is composed? Check how old your data is and develop a cleansing strategy to eliminate duplicates.
“We’ve built a set of data-quality dashboards with well-defined rules around the data to highlight errors. For example, you could search for any tenants over the age of 110; more than a handful would indicate inaccurate data. Similarly, if a tenant is recorded as being under 18 but they have a child aged 21, an error has crept in somewhere. Creating rules like these will check the data and improve data quality.”
Quick wins and slow burns
CGI’s Nduva said, “Comprehensive change relating to how an organisation manages its data isn’t a quick process but within 8-12 weeks, you can write an achievable implementation plan. Within that plan, you will have some priority areas and some ‘quick wins’ but unless you have joined-up support from your leadership team, you’ll end up with siloed, unsustainable change and over time, your data creators and data users will revert to their old ways.”
Civica’s Shreeve said, “Building a common, organisation-wide understanding of the data is a good starting point. This enables data owners to be identified and governance structures to be set up, meaning a housing provider can begin to exert formal decision-making and control over its data assets.”
Jaywing’s Mund said, “It starts with understanding your current data landscape. A logical data model and data dictionary (using common business terminologies) are the first steps towards identifying common data attributes across your disparate systems. This will also flush out any issues of non-alignment, such as what your repairs and finance teams respectively view as a ‘customer’. Overall, housing providers should be aiming for a ‘single version of the truth’ data infrastructure; approached correctly, this can deliver value fast and doesn’t need to be a long-term project.”
Pitfalls to avoid
Civica’s Shreeve said, “First of all, don’t try to do everything at once; think big but start small. Tactical data-management programmes with short-term, positive impacts can then be used to incrementally foster best practice across the rest of your organisation.
“Don’t be swayed by the shiniest new analytics tools; a lot can be achieved using mainstream and established technologies, many of which you probably already have. Find a definitive problem to be solved, build a proof of value, test the model and then scale up, thereby building data management into your organisational fabric.”
Insite Energy’s Coates-Smith said, “The greatest pitfall when it comes to data management is humans. Whatever policies, processes or systems you have, they will all have an element of human interaction and, despite our best intentions, all of us are fallible. Human fallibility is a recurring problem, so it’s important to be aware of it, accept it and have policies to deal with incidents when they occur.”
Jaywing’s Mund said, “The common pitfalls for housing providers with low levels of data maturity include not having a clear scope of what want they want to improve, putting too much into the initial scope so that delivery is unachievable within reasonable timescales, not using skilled practitioners in delivery, and a lack of senior-level sponsorship.”
NEC’s Hampton said, “When implementing a new system, it is often tempting to make unwise decisions to save time, particularly if a project is in danger of overrunning. Deciding to cut corners by not cleaning the data properly, reducing the validation checks or working offline from separate spreadsheets can compromise the data, resulting in poor quality data being loaded into the new system.”
Manifest Consulting’s Salisbury said, “The right levels of executive support are rare. However, what’s actually needed from housing providers’ boards and executive teams is their support for the operational staff who know their subject and know how to do things well. The role of senior executives should therefore be to remove any obstacles in the way, as well as not adding any new ones.”
FLS’s Squire said, “Data management must be prioritised by senior teams, and housing partners, contractors and consultants should also be challenged to ensure they meet the highest possible standards of data management. At the same time, boards need to challenge the information they see and ask how confident the management team is regarding the accuracy of the data presented.”
CGI’s Nduva said, “CIO or CDIO board-level roles are now common, with these roles making information governance a key board-level priority, with associated accountability. Having stakeholders and support at this level helps align the entire organisation to the importance of managing and governing information.”
Examples of exemplary data management
FLS’s Squire said, “Our partnership with Your Housing Group is enhancing its data management capabilities. By using real-time optimisation and not just finding ‘white space’ to fill in its repairs schedule, Your Housing’s tenants are given am/pm appointment options for repairs and maintenance. Its capabilities have been transformed; operatives are more cost-effective and punctual, achieving 30 per cent more jobs per day and completing more first-time fixes.”
Manifest Consulting’s Salisbury said, “We worked with Optivo (prior to its merger with Southern Housing) to develop a brilliant property handover solution – staging development data and creating relevant and useful structures for physical and financial information, resulting in the automated creation of property records in Optivo’s housing system.
“The interface of data between development and operational teams has been a long-term problem for housing providers and we thought Optivo came up with a very effective plan and set of tools for tackling this.”
NEC’s Hampton said, “I would highlight PA Housing, Wolverhampton Homes and Guinness Partnership who are each using enterprise data and statistical modelling to identify key predictors for rent arrears and damp and mould.”
Jaywing’s Mund said, “Anchor is an exemplar for how housing providers should approach data governance and data management. Over a three-month period, Anchor’s internal subject matter experts, with the help of data-management consultants, defined a logical enterprise-entity model and a data dictionary that documented the accepted terminologies and definitions for all entities in the model.
“Alongside this, Anchor now has suitable data-governance processes, ensuring its data-management framework aligns with its evolving business requirements. Crucially, the entire project had executive-level support via Anchor’s chief technology officer.”
Housing Technology would like to thank Ben Nduva (CGI UK), Richard Shreeve (Civica), Jeremy Squire (FLS – Fast Lean Smart), Anthony Coates-Smith (Insite Energy), Inderjit Mund (Jaywing), Peter Salisbury (Manifest Consulting) and Trevor Hampton (NEC Software Solutions UK) for their comments and editorial contributions to this article.