Social housing providers in the UK are feeling the pressure of a changing business, policy and technology environment. The level of change in the political external environment has been unprecedented and providers are having to adapt and respond to a series of existential threats and opportunities. The timing of this change, coupled with the data and digital revolution, has meant that business intelligence solutions, where decisions are informed and underpinned with a strong evidence base, are now playing a key role in the strategic and operational planning process within an organisation.
From a technology perspective, businesses are experiencing constant disruption as rapid innovation brings about change which challenges existing operating models. As computing power becomes more accessible and digital transformation programmes are implemented across key business areas, the amount of data generated increases exponentially, opening a number of opportunities for rich analysis and insight but also creating frustration when the promise of a data-driven future does not materialise.
A useful lens on which to view the challenge and opportunity facing providers is to consider where organisations are in terms of the data value chain.
Figure 1: Data value chain
The data value chain describes the path many organisations follow, starting with data then moving to reporting (which is generally backwards looking) onto analysis, informing decisions which in turn trigger the appropriate action, ultimately providing value in the form of efficiency, higher profitability, social value and customer satisfaction.
Our own evidence from talking to the sector suggests the vast majority of organisations are at the lower end of the chain, somewhere between reporting and analysis and in many cases, where analysis is present, there are other cultural issues which prevent that analysis informing decisions.
This is further reinforced by the findings from our Executive Matters survey which canvassed the opinions of CEOs from across the sector about their confidence across key data areas within their organisation – quality, completeness, access, presentation and control.
The overwhelming view is that CEOs do not have a high level of confidence in data and do not see this changing significantly in the next 12 months.
We know that there is a real appetite to improve this situation and an acknowledgement that this is not a straightforward task that can be resolved with a PID and a project plan (sorry to all you PRINCE 2 lovers out there!).
At HouseMark we have embarked on our own data journey and have continued to work in products and services which support members to move up the data value chain, make better decisions based on evidence and increase their overall data maturity.
We think that a data maturity model which is simple to articulate and visualise is a good tool to engage in discussions around data and evidence. This model builds on the concept of the data value chain and looks at the path to becoming a data optimised organisation through three domains: people, process and technology. Looking across these three domains it is possible to get a better view of the current position and aim to make progress in a coordinated way. These three domains are all interconnected and interdependent and there are different strategies depending on the specific circumstances of each organisation.
Figure 2: Data maturity model
There are a number of ways organisations will develop their maturity across these domains
Figure 3: Even maturity across domains
Figure 4: Some domains with a higher level of maturity than others
Figure 5: Non-linear maturity stages across domains
Within this framework it is worth considering the type of data roles that are key to increasing data maturity
Taking the importance of the data leader a bit further, a key area that has to be explored is the importance of culture and the existence of the HiPPO.
The issue of ensuring an effective culture was highlighted in our Executive Matters survey as a key critical success factor in achieving efficiency and improving effectiveness. Given the important role that data can play in business transformation, it is therefore no surprise that building a data driven culture is so important.
Have you ever been in a meeting where you’ve had strong evidence based on data to support a specific course of action, only to be overruled by a senior person in the room without alternative evidence? Have they said that they have years of experience or a strong intuition about which way they should go? Well, you’ve been struck by a HiPPO!
Figure 6: The HiPPO
The HiPPO (Highest Paid Person’s Opinion) is a dangerous beast in the meeting room. It will kill off discussion and discourage people from being data driven. If you have such creatures in your organisation, this needs to be understood and managed as no technology, system or process is likely to be a match when to comes to the HiPPO’s influence. You must be careful not to blame everything on the HiPPO, as they are created by the environment they work in. As Jim Barksdale said in his time as CEO of Netscape: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
Ensuring there is a high level of data literacy across the organisation is another vital ingredient. This should not be mistaken for a focus only on technical skills, but a much broader set of skills that ensure staff are comfortable to read, understand, create and communicate data.
A key element within this is the ability of staff to engage in data storytelling. We know that people engage with stories and act on evidence and both are needed in a data driven culture. This combination is often missing and there can often be a gap between those involved in the creation and analysis of data and those receiving the information and reliant on data to make critical decisions.
A culture that embraces experimentation is one that is much more likely achieve a high level of data maturity. Many of the household name organisations that have transformed industries and everyday lives, be it Amazon, Netflix and Uber, are built on experimentation and rapid testing. Thankfully, we are beginning to see some providers embrace this approach, but it still very much the minority. Experimentation and testing are a massive opportunity to unlock creativity within the organisation and allow staff to try things and share the lessons when they go wrong.
What we do still see a lot of providers launching data led projects with excitement and grand objectives about how they will use the latest thinking – be it big data, machine learning or predictive algorithms – to transform a part of the organisation. Whilst this comes from a positive place about realising the potential of data, too often these projects are big on enthusiasm and low on maturity as to the reality of how the potential of the data revolution applies to their organisational context.
This brings me onto my final point – the importance of sharing. We have lots of conversations with our members and it is clear many are keen to seize the opportunity that techniques such as predictive analysis and machine learning seem to offer. However, this can lead to organisations going off on their own and missing the benefit from sharing their journey with others, which not only reduces the risk and cost, but can also give a much greater chance of success.
So, let’s keep talking…
To find out how we’re helping the sector maximise the use of their data, get in touch. Contact us at firstname.lastname@example.org, let us know what you’re interested in and we’ll happily help.
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