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Over recent years, awareness within the social housing sector of the importance of good data, and the risks associated with poor data has intensified. Regulatory judgements continually cite poor data management as a contributory factor to failings in areas such as inaccurate regulatory submissions, health and safety compliance, and poor decision making by boards. This increased awareness of the data quality agenda was also supported by the introduction of The Data Protection Act 2018, which refocussed all businesses on data protection law and the legal requirement to keep personal data accurate and up-to-date.

The risks of poor data management for social housing providers are numerous, including non-compliance with regulation and legislation, process inefficiencies, unnecessary costs, and flawed management information diminishing the effectiveness of decision making. Robust data management can however be an ongoing challenge for social housing providers as a result of the large volume of data they must deal with.

Issues that can lead to an under-developed data management framework are often similar across the sector. A common scenario is where an organisation has grown over time through stock transfers, mergers, acquisition and development. If robust data management arrangements were not in place throughout this time, it may well have led to a situation where there are inconsistencies in the data that has been collected, the formats in which data has been stored, and the systems and reporting tools being used to process that data. The size of an organisation and the diversity of its structure and operations is also therefore relevant to the potential scale of any such issues, with larger organisations often facing a much greater challenge.

An underdeveloped strategic approach to information technology is also a common issue leading to data integrity problems. Some organisations have legacy databases and systems holding key data, that do not integrate with each other or with the organisation’s core housing, asset, finance, and customer relationship management systems. High levels of spreadsheet usage can be an additional indicator of data management issues, contributing to a reduced transparency around data processing activities and data accuracy controls.

Furthermore, where organisations have seen turnover of staff during their lifetime, poor knowledge management can be an issue resulting in situations where handover procedures have been ineffective, and the provenance of data sets now being relied on for key tasks is not always understood.


If an organisation has identified a business need to focus on data it is generally appropriate to take a project management approach. The project should address all data quality issues and put in place a systems and data architecture that is fit for the future and supported by solid data governance processes. For those organisations on a journey to improve their data management some key criteria for success include:

  • Gaining buy-in from all relevant stakeholders.
  • Developing a business case that sets out the tangible outcomes and benefits of the data project.
  • Devising a clear and comprehensive data management strategy.
  • Making sufficient resources available to effectively manage the data project and complete project actions.
  • Putting in place a Project Steering Committee.
  • Allocating data ownership responsibilities, and data stewards.
  • Development of a Data Architecture Team.
  • Updating systems and processes to support good data management.
  • Putting in place ongoing assurance arrangements around data quality control.


Many social housing organisations already have data cleansing projects in place, however, these can sometimes be reactive and siloed, rather than proactive and part of an organisation-wide strategic approach. The latter option is the ideal way forward but is not without its challenges.

Crucial to success is the development of a business case that achieves buy-in from key stakeholders and secures the allocation of the financial and human resources required to run a data management project and ensure effective ongoing data management following this. Quantifying the benefits in terms of economy, efficiency and effectiveness to support such a business case can be difficult, and even when done well, some organisations may still struggle to make such resources available.

Other barriers may include issues such as negative perceptions from stakeholders based on previous data projects or a lack of in-house technical capabilities to support such an undertaking.


Despite the challenges of implementing an effective organisation-wide data management approach however, there are numerous high-value benefits that can be realised. These include:

  • Accurate data.
  • Better management information to support decision making.
  • Improved risk management.
  • Enhanced ability to comply with regulation and legislation.
  • An efficient and optimised data architecture.
  • Streamlined processes resulting in the achievement of efficiencies and operating cost reductions.
  • Removal of data duplication.
  • Better coordination of working practices (including joined up policies and procedures across the organisation).
  • Clarity around data ownership and responsibilities for data quality.


Our maturity assessment methodology provides your organisation with a health-check of data management within your organisation, giving you a snapshot of controls and culture, and identifying any areas of weakness.

Our approach is to review and understand your current data architecture and the framework of processes and technology that support data management. We then work with you to identify practical solutions to improve controls throughout your organisation and manage your data risk.

For a further discussion on how we can assist your organisation with improving its data management please contact us.

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