When it comes to physical assets such as homes and offices, ensuring they are known, well-maintained and fit for purpose is at the core of every housing provider’s operations, so why do so many not treat data in the same way?
Whether they realise it or not, data is the lifeblood of any housing provider. It drives their efficiency, their income and their customer experience, yet it’s often considered more of a necessary evil than a valuable asset.
The best housing providers leverage their assets to fuel decision-making at all levels and to better understand the people they serve. By treating data strategically, forward-thinking organisations can give both themselves and their tenants better outcomes.
For housing providers which haven’t yet reached that level, now is the time to plan the journey from being data-rich yet information-poor to being wholly data-driven. Data-quality management is a specialism but one that’s rooted in common sense. The goal is to get accurate and reliable information to the right people at the right time, no more, no less.
This could be providing potential customers with an up-to-date view of the available stock, recognising and accommodating a customer’s vulnerability, providing a maintenance engineer with the correct address and job details or providing the senior leadership team with accurate financial information. At every level, in every operation, better quality data adds tangible value.
Step one – Get the right team
Many housing providers choose to operate with a chief data/information officer, others opt for a head of data role and some manage change from the COO’s office. Whichever the case, having senior sponsorship of transformational data activity is vital.
From there, experts from both the technical and operational sides of the organisation need to be brought together. Data-quality transformation should be a business-led activity, supported by technologists along the way. By bringing together both sides early in the initiative, buy-in can be assured and this is essential to success.
Tackling complex data-quality issues can be as much about building a consensus among peers as it is about technical investigation, so the leader of any data-quality activity should have strong negotiation and communication skills. Resources for root-cause analysis and data cleansing will need a solid understanding of the business operations and good technical competence to ensure they can see issues from all sides, leading to more robust fixes.
Step two – Think about potential data-quality partners
The right partner will act as an accelerator and provide the specialist (albeit temporary) skills needed to get a data-quality initiative started. From planning and design through to execution, an experienced partner with knowledge of the housing sector should provide best-practice advice and help avoid the common pitfalls.
They can also provide expertise on the software available to support data-quality management and how that software can be leveraged to maximise its value. Finally, where resources are scarce, many partners will be able to deliver ‘turnkey’ solutions to get the ball-rolling while the internal resources are being established.
Step three – Decide your strategy
One of the secrets to a successful data-quality exercise is focusing on where invention is truly necessary. While the idea of measuring the quality of all data sources may sound appealing, the reality is starkly different. Identify key business processes and regulatory requirements (such as NROSH) and start with the data items that feed those. In most instances, this will reduce the project scope to something that is manageable and (usually) well understood.
Another key piece of advice is to take the lead from software development processes and deliver ‘little and often’. Choose one area to make a start and work it through before starting on the next key area. This will build momentum, allow lessons to be learned and spread the load on all stakeholders over a longer period, allowing parallel activities to continue unaffected while still providing value and confidence in the data-quality project’s potential.
Step four – Build momentum & prove the concept
That word ‘momentum’ is hugely important in data-quality projects. Build momentum by quickly choosing and implementing a software package to support data-quality measurement and starting to prove the value in doing the work. Here is where a partner will add the most value, in terms of getting rule-sets developed quickly and accurately.
Bring people on the journey by starting a regular data-quality forum with representation from all key areas, even those not initially directly involved. This will encourage people to contribute to how data quality could be improved in their area, and quickly highlight any overlaps which might increase the priority of certain issues.
Step five – Prioritise the results
Prioritising the issues found is so important. Many data-quality projects fail because teams become over-faced with the volume of issues and lose sight of the value of issues. Low-volume issues in critical data-fields almost always represent more valuable cleanses than high-volume issues in less critical areas. For example, do you really need a four-line address for your processes to be successful or will just the first line and postcode be enough for them to work?
Step six – Cleanse and fix
Cleansing data could be a whole article on its own! However, as a starting point it’s important to create stretching yet achievable data-quality targets. Aim for 97-98 per cent quality in key fields; 100 per cent is neither likely nor cost-effective. Use a prioritisation matrix to identify the short-, medium- and long-term wins. Disregard any which cost more to fix than work around but do look at their root causes to prevent more being created.
Root-cause analysis is also a vital facet of these projects; this is what prevents cleansing becoming ‘regret spend’. Many organisations overlook this part and enjoy clean data only for a short time after investing in cleansing. Prevention is preferable to the cure so look at your end-to-end processes from all angles, from technology issues to training, incentivisation, resource levels and process efficiency; you will find gains to be made.
Step seven – Expand and repeat
Once a successful pilot has been completed, expand its coverage to more key areas and processes. Keep the quality forum alive, keep communicating your progress and results and encourage people to highlight any work-arounds that could be removed or any other persistent issues which are harming your customer experience.
Finally, enjoy the benefits of high-quality data. Better decisions made faster; better customer experience, consistently; and a more efficient business, delivering real value.
David Bamford is head of deployment at IntoZetta.
