"How do we get one version of the truth"

Data Quality v Data Condition v Data Quantity

At ARK, among many of our client focused asset management support services, we undertake stock condition surveys, data health checks and asset management modelling through our ASAP (ARK’s Strategic Asset Performance) tool. Our clients usually volunteer that their data isn’t perfect, but this isn’t unique, 98% of all organisations believe they have inaccurate data. So why does this remain a stumbling block across the sector...?

Data quality is often treated as an IT project that sits in a system owned by IT. Data is meant to be used by the business, so ‘organisational data’ needs to be owned by everyone as all data has a value, but with a different perspective depending on who uses it.   For example, the compliance team records a tenanted property has had its gas capped at the meter.  The value to the compliance team is to make sure the property is safe and record it, however the value to the wider teams is far greater - the ability to support their customers to live in a healthy home (affordable, warm, safe and secure). The true value to this data is the ability to identify and intervene to prevent the associated costs with not only immediate health issues at home, but the longer term social and economic impacts such as increased usage on public services, (NHS), not forgetting the impact on the organisation’s financial performance and reputation with the Regulator.  So, in this one example, the value of the data recording a gas cap is actually exponentially far greater than initial face value.

There’s typically no link back from the quality of the data into service department performance. ARK recently completed a stock condition survey for a client - our access rate was 47%. Why so low? – the client knew the contact details were out of date but had not linked this back to performance. If ARK, experienced access issues, what was the true cost to all the other service areas i.e. gas servicing, electrical inspections etc. The corrective cost would be much less than the abortive cost and would provide the organisation with data that would add value back to other services and its customers. 

Remediation - focusing on data quality often leads to fixing issues just to fix them — without considering what actions have the most value or will have the biggest impact on business performance. 

Root cause analysis - data quality helps you identify technical issues, but not what’s causing them in the first place. Often the root cause involves the people, processes, and technologies around the data. 

Technical Standards – to ensure data quality, we should be assessing the data against a set of agreed technical standards. This may be completeness or age but somehow we need to define what ‘good looks like’ and measure against that. 

We also often hear clients tell us that they have a high amount of stock condition surveys or no cloned data. This is all really positive news however the use for data is endless within housing, and stock data or customer data shouldn’t be a race to the finish line…. it’s a never-ending marathon. We often talk to clients about quality of data rather than quantity – collecting stock condition data 10x times faster with inexperienced surveyors will only cost you more in the long term whilst placing you at the mercy of the Regulator. At a time when the Regulator is pushing for ‘professionalising’ the sector, a good starting point is making sure the foundation blocks (stock condition data is exactly that) are afforded, competent, well-trained, customer focused staff that have the experience to address all issues presented in front of them. 

So why is “one version of the truth” important? 

It is claimed that 80% of professionals that use data spend up to 80% of their time cleansing, organising and preparing data before it can be used for decision making. 90% of the data we hold is ‘dark data’ – stored data that is never used. Do we ever utilise repair data to measure the performance of individual components? Our financial plans are based on standard lifecycles but what happens if they underperform or over perform, do we have the sound knowledge to re-calibrate? 

From a property perspective, we have seen some fundamental challenges over the years, all of which require some form of transformation to ensure delivery such as the introduction of Decent Homes, the impact of the Pandemic, regulatory and legislative changes including the Building Safety Act and probably the biggest, decarbonisation. 

Let’s focus in on decarbonisation. A lot of money is being put into that now (rightly so) however the data for which we are placing large scale transformation of our stock and investment plans is inadequate. 70% of transformations fail because of lack of investment in data at the front end. 

A report undertaken and presented to the UK Parliament suggests that 30% of properties that are assumed SAP band C would become a SAP band D on the second assessment. If we look at the volume of grant that has been handed back for decarbonisation work or talk to the main contractors who have had to submit change requests because the properties originally included within the bid are no longer feasible / suitable it shows there is a huge cost associated with this. Not just a direct cost but an indirect cost to the supply chain, suppliers and customers. 

The solution? Recognise that data is an investment and an asset. Suppose I sat in front of a contractor without knowing my stock profile or able to give any confidence in my data that produced the programme. In that case, I will likely pay a premium for the work to be done due to the amount of risk the contractor would have to build in and sadly this is a common theme we see across the sector. If I recognise my data as an asset and not as a standalone data element, it will help me to put a value on the data and demonstrate a return on the investment. The unavoidable costs, removal of the contractor risk cost, programme delivery certainty, longer-term engagement with the supply chain, customer experience and reputation are some of the areas of tangible value. But what about Boards knowing they are making the right decisions? Staff confidence that we are doing the right thing and can plan for implementation? Customer engagement levels increasing as we are more informed and ultimately less chance of regulatory scrutiny? This is the bit we also need to value. That way we can start to understand the value of the data we hold, whether it is fit for purpose, and whether it helps to portray ‘one version of the truth’. 

So, to get to one version of the truth we need to:

1. Realise the Value of Data. If we start to identify which data assets are most important to achieving organisational strategic goals, we can prioritise investment into data that underpins and provides confidence in decision-making. For decarbonisation, should we base a large level of investment on an EPC?  If we were to value the impact of not having the right data assets, we can see the ROI and areas to invest in stock data. If we invested in building data assets, would this be more attractive to the contractors and funding market?
2. Plan for the right data asset by understanding what makes up the various component data sets. For decarbonisation, would a data set (a collection of data elements) for customers’ running costs, relative humidity, health considerations, occupation, property usage/interaction add more value when pulling together a decarbonisation bid if coupled with asset/energy data elements to form a data asset? If the value of holding this data outweighs the cost of collection and maintenance of the data, and demonstrates a return on investment should we not invest first in data?
3. Professionalise our approach to data – as we move forward, there will be more importance placed on data. This requires a shift in ownership from ‘someone else’s data’ held in an IT system to the data held in our business system where business decisions are made, asset or non-asset related. Remember, each data element is linked to a data set which is linked to a data asset
4. If we could ‘monetise’ the value of data, it could help demonstrate the true value of it to the business and highlight areas of investment required. To be able to do this we need to change our mindsets and start to understand what data is, its use, and its value. You never know, one day we may start to recognise data as an asset and see it on the balance sheet!

ARK’s Strategic Asset Performance Model helps clients to understand their housing stock. We take data elements and turn them into data sets and this allows housing providers to visualise and identify the performance of their housing stock and understand relationships between the many aspects of asset management including resident and staff feedback.

Our asset evaluation and grading platform provides clients with evidence-based data to help their organisation make well-informed strategic decisions for retention, stock rationalisation, future investment, re-modeling, and determining the future of their assets. We are driving forward our ongoing development on the value of data for the sector, helping to empower clients to join us on that journey to an intellectual, informed and robust ‘one version of the truth’… 

Luke Beard is an assistant director at Ark Consultancy