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Aareon Smart World

Big Data for Housing Associations

More and more organisations are gathering, processing, storing and deriving value from all forms and sources of data. The number and size of systems that support significant volumes of both structured and unstructured data will rise inexorably too.

Big data drivers are not IT but business departments

To many, this momentum is powered by the belief that the effective management of big data is the driving force behind sound and responsible best practice. However, it is not always the IT stalwarts that are the greatest protagonists of big data’s impact. For example, Aberdeen Group recently reported that, “organisations with big data are 70 per cent more likely than others to have business intelligence projects that are driven primarily by business users, not by IT.”

Reasons for the business interest

By studying the benefits of big data technology, we can understand why this business-led prerogative is in the ascendancy. Some of the main pay-offs are that big data can:

These are all good business motivations. As Geoffrey Moore, author of ‘Crossing the Chasm’ and ‘Inside the Tornado’ said recently, “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”

Challenge: The sheer mass of data

The clear implication here is that C-level management needs big data to drive strategic direction effectively. However, it is the sheer scale of the data sources that concern them most, or rather how best to use the data that they have gathered or could gather.

It’s a valid concern as the use of the technology continues to expand to hitherto unimaginable levels. And you can forget the principals of data warehouses or mountains and interrogating the data with pre-defined questions. Now, it’s all about ‘data lakes’ and finding the answer even before you knew what the question was. And this volume of data will continue to expand; where there were once terabytes, there are now zetabytes.

Look into the future with big data

Certainly, in the mainstream business world, I can see more and more examples of the benefits that big data delivers when managed successfully. For example, Experian can identify those most likely to need financial support in the future. This has led to a more supportive and caring attitude to credit.

Potential of big data for the housing industry

While such data analysis can be used effectively across all market sectors, social housing is one of the areas where I feel passionately that the potential of the ‘art of big data’ has not been fully realised. Yet it is perhaps one of those areas where big data offers the greatest potential. It’s certainly fascinating to see the data from different sources combine and to contemplate what this might tell us.

Data is available

For any single property, there’s data about the property itself, data about the type of tenancy, the background of the tenant and historic data from across the different housing providers’ departments including housing, income, repairs, care and estate management. There’s probably a whole load of tangential data too, such as nearby traffic management, ASBO issues or even policing statistics. To this external data, one could add data about regional demographics, cultural characteristics, education and health.

Additional data from sensors

In the future, IoT data from inside the property will also count – all provided by sensors that detect effective heating levels or water usage and which can even optimise boiler replacement times.

Helpful correlations

In fact, it’s quite mind-blowing that you can find correlations in the data that you didn’t know were there. Recently, I came across some research proving a close correlation between cancelled gas certificate inspections and late rent payments. This kind of information gives housing providers the ability to potentially tackle issues before they become problematic.

Practical use of existing data

Indeed, by using big data management, one can already deliver all the relevant data in a refined and accessible format (ref. both the tenant and a property) to the smartphone or tablet of a visiting operative. For example, within 1st Touch, field workers and mobile operatives now have comprehensive 360-degree access to all relevant data available online, in a refined format, through their devices.

Efficient on-site appointments

This allows them to handle all outstanding issues in a single visit. Thus, a housing officer can take a rent payment and book a repair or gas inspection. They can also discuss care issues or the need for a discretionary housing payment. By the same token, a gas engineer servicing a boiler could also report an ASBO issue or report on estate conditions. By being able to solve multiple cross-function issues in a single visit, it frequently means that the right outcomes are delivered far faster to an even happier customer. It also means that the costly requirement for a second or third, fourth or fifth visit is eliminated.

Satisfied customers and cost reduction

This is a wholesale change in the customer interface and the satisfaction ratings will leap as a result. However, there is also a real chance here to dramatically slash costs; by solving all the issues in just one multi-function visit, a housing provider can do more with less. This potentially means that they can reduce the recruitment bill for the same function. And if there are fewer staff involved, then there’s every chance that other fixed costs such as office rental costs could also be reduced.

Housing companies recognize the potential

This ‘do more for less’ opportunity explains why the driver in social housing is more a business issue than an IT one. It also explains why housing providers’ senior management are keen to get into the data to see exactly what’s in there, as opposed to looking for pre-defined answers.

As a consequence, one can see that it is the holy grail of ‘predictive big data analytics’ that will continue to drive future development. We have already found that many of the more customer-focused and efficient housing providers are keen to work with us to exploit our 2020 vision of the market. In doing so, they aim to identify the trends within the data they have collected and to understand ways in which they can translate these into positive actions. While they are aware that both the data and the task ahead are big, they are motivated by the fact that the opportunities for their customers are even bigger.

Vision 2020: Big Data helps to increase the efficiency of building management

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Background information: Rescue in the data jungle

Read about how digitalisation, big data and data mining influence your customer relationships in the following article:

Social data revolution, big data, our data

Besides the introduction and establishment of PCs and the Internet, the revolution of user data is the third major development stage of digitalisation. It is revolutionary because this development is expected to change society, politics and the economy just as fundamentally as the transportation of energy through electricity and the transfer of information via the Internet. The social data revolution was made possible through disappearing technological barriers in the generation of data and interaction with other people on the Internet.

According to Wikipedia, 94 percent of the global technological information capacity was already digital in 2007 – compared to only three percent in 1993. It is presumed that 2002 was the first year that mankind was able to store more information digitally than in analogue form, marking the beginning of the “digital age”. Dr. Andreas Weigend, Internet pioneer and former Chief Scientist at Amazon: “What’s even more significant is that the total volume of data is growing exponentially: It doubles every one and a half years, which means it increases by a factor of 100 per decade!

Where and how is data generated?

Never before has it been so easy to query, gather, analyse, evaluate and use information or data. We all reveal a lot about ourselves through an increasing number of online interactions and transactions over the computer or smartphone – whether indirectly, for example via our intention behaviour (e.g. Google: search) or our shopping behaviour (e.g. Amazon: “Customers who clicked on this cable then bought...”), or directly via our behaviour on social platforms (followers, likes, etc.). The consequence is a massive change in our information and consumer behaviour. Ask yourself: What has changed over the past ten years? How do I get information? How important has the Internet become for my purchasing decisions? At what price are you prepared to disclose personal data? How much is the confidentiality of “your” data worth to you? What does “our data” mean?

Nappies and beer

The advantages of gathering and evaluating data are obvious: Companies become more familiar with their target groups and are able to earn customer loyalty by making tailored offers. They can use the incoming data as a basis for improving their products and services and thus harmonise their own goals with those of their customers. Meanwhile customers are satisfied because they find what they are looking for much more quickly and also discover things they didn’t even know they were interested in (“delightful serendipity”). When they buy something, they feel safe because it was recommended by other consumers or even by friends or acquaintances. People prefer to be guided by other people rather than by companies or statistics.

While the volume of information from and for consumers is increasing, the level of attention remains unchanged. Companies therefore face the challenge of filtering relevant customer information from the growing flood of data and using this newly acquired customer knowledge intelligently and productively.

An early example from the days we had to get by without the help of the Internet: A major U.S. supermarket examined receipts for unexpected correlations. One correlation it found was that nappies and beer were often bought at the same time in the late afternoon. Interpretation: Young fathers who are asked to bring home nappies after work also buy beer for themselves to drink in the evening as a “reward”. The supermarket reacted by placing nappies and beer next to each other in selected supermarkets – and the amount of beer purchased rose significantly.

Data mining

The example of the nappies and beer shows that large datasets alone are not much of an asset. Data only unfolds its worth when it is linked with other data. For this purpose, it has to be structured and intelligently analysed – a practice known as data mining. With the help of the Internet, behavioural data can be gathered on a wider scale, at a greater level of detail, in a technologically simpler manner and above all in real time. Information about decision-making processes, consumer behaviour, social behaviour, thoughts, feelings and the like – i.e. information that is otherwise only in each individual’s head – can thus be digitalised and rendered analysable. It is therefore not surprising that, on this basis, companies can already predict customer behaviour, often even better than the customers themselves. Credit card companies even know who will get a divorce in the next five years – a vast playing field for the determination of innovation potential.

Using big data

If a company – no matter what size – wants to use big data to its advantage, it must first formulate relevant questions, then develop a data strategy and finally offer its customers a platform on which they can provide input and receive output. What is important here is how the customer data is handled: Without transparency and a tangible benefit for the customer, the strategy will not prove to be successful.

In conclusion: Today, data-based marketing still provides companies with a competitive edge. In ten years’ time, however, it will presumably have become a hygiene factor.

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