In part four of this short introductory series Jim Vine delves into the ‘why’ of Housing Big Data.
In the earlier parts of this series, I explained that Big Data are hard, more or less by definition. Not only are the data hard to extract value from, due to their volume, velocity or variety , but they also require a variety of skill sets, typically take some to-ing and fro-ing before you get to a result you can use, and present the analyst with a wide array of techniques that might or might not be useful in any particular situation.
Given that assemblage of complexities, it might be surprising that Big Data has become a popular and relatively mainstream buzz phrase. Of course, the only reason something with such inherent challenges has become so important across the business, government and social sectors is that it can have great value.
When I say that Big Data approaches can have great value, I tend to think of the value in terms of driving either (or both) effectiveness and efficiency. Does it help you to deliver services that genuinely meet the needs of individuals and communities? And does it help you to minimise misplaced effort (which of course frees up resources for further productive deployment)?
From all sides the housing sector faces pressures that are motivating a need for more efficiency and effectiveness: resources that are already constrained will be squeezed further for several years to come. Yet these pressures housing providers will come at the same time as pressures on the people they house and the communities they work in, so the impetus to deliver on landlords’ social missions will be as strong as ever. Across their operations, whether in core housing activity or in community investment, housing providers will be ever more focused on ensuring that everything they do is achieving its intended outcome and does so as cost effectively as possible.
Alongside efficiency and effectiveness I would also highlight evidence as the other key driver of the adoption of Big Data in the housing sector. Evidence is a necessary partner for understanding efficiency and effectiveness – drawing upon, or generating, decent evidence is how we can get to the bottom of whether something is efficient or effective.
The Housing Big Data project is assembling a far larger volume of data than the housing sector has ever brought together before. In doing so, we are tackling head on the challenges of coping with the resulting variety. We are doing all this to generate new insights about the relationships between people and properties – insights that it would not be possible to extract from the data of any one housing provider acting alone. Those insights will give housing providers information that will allow them to drive efficiency and effectiveness in a variety of areas of their businesses.
As well as the benefits inherent in assembling a large dataset, another major advantage of the shared analytics approach is that some of the complexity (and hence skills requirements) will be centralised. Each housing provider will need to provide data, be able to describe it, and be able to use the information given back to it; but it will not need to build up huge new expertise in coding, statistics and the range of algorithms that are available for analysis. The shared analytics also represents a pooling of resources and hence greatly increases the number of questions that can be answered.
I will be posting more blogs in the future as the Housing Big Data project progresses. In the meantime, do please feel free to get in touch if you would like to be considered for inclusion when we open the project up to wider participation, click here to email us.
HACT’s Housing Big Data project has been generously supported by the Nominet Trust, the UK’s only dedicated Tech for Good funder that invests in the use of technology to transform the way we address social challenges.