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Image to illustrate no data can mean no voice and no idea

No Data? No Voice, No Idea – The Importance of Data

The collection and analysis of data must never be allowed to fall by the wayside – it’s a founding stone, not a ‘nice to have’.

Of course, I would say that, wouldn’t I? But here are six concrete reasons why data is important, drawing on Get the Data’s recent work with the Advice Services Alliance (ASA) as a case study.

  1. Data Is the Great Persuader

There is no more powerful tool for influencing stakeholders than data, as I explained in more detail in this recent blog post on using data to influence stakeholders.

ASA works with its members – various associations who provide advice services – to capture and analyse data from the front-line. This gives weight to conversations with all of those who have an interest in ASA’s direction of travel, reassuring them that strategic decisions are being made in response to changing needs, and reinforcing the professionalism that underlies the work they and their partners do.

 

  1. Data Means Funding

In particular, data is invaluable in drawing in new sources of funding, and persuading potential funding providers. Faced with a choice of projects or programmes in which they might invest, with ever-tighter budgets, funding bodies will regard convincing data as a good reason to choose your work over others. ASA will use data to add weight to its funding applications for this reason.

 

  1. Data Gives You the Power to Lead the Debate

ASA seeks to lead thought, representing its members in national discussions by highlighting issues affecting the people who use advice services. The body of data and analysis to which ASA will refer confers authority and allows the organisation to direct the debate and steer collective thinking around youth issues.

 

  1. Data Defines Good (and Bad) Practice

Using data provided by its members ASA will be able to identify areas for improvement in front line practice and also to pinpoint what is working especially well so that good practice can be shared across the community. It informs training programmes, service improvements and helps determine how resources should be employed for maximum impact.

 

  1. Better data and measurement development 

Good data leads to better data. Use of data means we learn its limitations and how to outcome those. We also learn how to measure the right outcomes, better; particularly in how to measure informal outcomes along said formal attainment.

 

  1. Data Is Cheaper and Easier Than Ever

Cloud-based databases are cheap and easy to implement compared to the cumbersome systems of the past. They make it easier for people to enter data and share it. So there’s really no excuse for failing to collect and analyse data in this day and age.

If you would like to find out more about our cutting-edge approach to data capture and analysis please get in touch.

Databse word cloud

Choosing a Database? Just Keep It Simple

When choosing a database for your project or programme, made to measure software or flashy online tools aren’t necessarily the right choice.

If you’re going to gather and analyse data in a serious way of course you need a database of some sort and, having spent much of my academic and professional career buried in them, I’m very much an advocate. But I too often see evidence of the database itself being regarded as the end rather than the means – as the solution to the challenge at hand rather than a tool for addressing it.

Perhaps that’s because we all so often feel under pressure, either external or self-imposed, to demonstrate what we have delivered in concrete terms. A specially commissioned database, perhaps with a catchy name and fancy interface, is something a project manager can point to and say, ‘I made this.’

The problem is that it takes huge amounts of time, resources and testing to create excellent software from scratch or to implement a powerful online tool, meaning that in practice these products all too often become clunky, creaky and frustrating. And products that don’t work well don’t get used.

As always, the answer is to focus on what you want to achieve. That will help you understand what kind of data you need to collect, who will be collecting and managing it and, therefore, what kind of system you need to house it.

Sometimes, of course, a custom-designed database or powerful online application are absolutely the right choice but, in my view, those occasions are actually very rare. More often than not the humble, rather plain Microsoft Access, or a similar tried and tested generalist professional product, is not only cheaper but also better suited to the task. Remember, these programmes have been worked on over the course of not only years but decades, and have huge amounts of resources behind their development and customer support programmes. Standard software also makes sharing, archiving and moving between systems faster and easier in most cases.

It is also possible to customise Access databases to a fairly high degree, either in-house using the software’s in-built features, using third-party software, or by hiring developers to create a bespoke front end interface without getting bogged down in the complicated underlying machinery.

And cloud data storage has made all this easier and cheaper than ever.

In conclusion, I’d like people to recognise that the real deliverable isn’t necessarily software, and that there’s no shame in off-the-peg. After all, a database is only as good as the information it holds and a clean set of useful, appropriate data is what people should really be proud of.

A lightbulb of cogs to illustrate service innovation

Service Innovation – Segment and Conquer

Supermarkets use data to sell us more of the things we want, and even things we don’t yet know we want – a real world example of service innovation through segmentation that we can learn from.

In social policy, we all know that there is no one programme or service that will work equally well for everyone in the target cohort. Even if it is having an impact across the board there will be some people for whom it works better than others and that’s where extra value can be squeezed out.

We might roll our eyes at the buzz-phrase ‘customer segmentation’, and of course there’s a difference between tailoring public services and selling sausages, but both require a similar approach to gathering data, analysing it, and in a sense letting it lead the way.

In the case of Tesco it’s about working out what shoppers want and selling it to them – a far easier job than convincing them to buy things in which they have no interest, a win for both parties. With public services it’s a matter of thinking in broad terms where we want people to end up – or not end up, as the case may be – and then letting what bubbles up from the data determine the most efficient route, and even the specific end point.

For example, working with one client that specialises in tackling youth offending, our data analysis found that though their intervention was effective overall, it was less effective at reducing offending among 12 to 13-year-olds than among young people of 15 and 16. By treating these two segments differently the overall impact of the intervention can be improved and more young people can be set on the right path at the right moment in their lives.

This approach challenges current orthodoxy which would have us determine our theory of change and set out clearly how we will achieve a given outcome before starting work. This can lead people to impose an analysis on the data after the fact, forcing it to fit the predetermined course. It also implies that all service users need more or less the same thing and we know very well that they don’t. The orthodox approach has its place, of course, once data has been collected and analysed, when we can start to make predictions based on prior knowledge.

Equally, it’s not efficient to design a bespoke service for every single end user, but there is a sweet spot in which we can identify sub-groups and thus wring out more value from programmes with relatively little additional time, manpower or funding. I’ll finish with another example: we have been designing approaches to impact management with a number of providers of universal services for young people and adult disability services. These agencies work with different sorts of people, with varying needs, and for whom different outcomes are desirable. Advanced statistical analysis can help us identify groups within that complex body and lead to service innovation which is both tailored and general.

Influence through Data

“Yeah, Says Who?” – Influence Through Data

You know you’ve achieved results – the data tells you so – but how do you influence sceptics to believe it?

It can be a rude awakening to take the findings of a study outside your own team or organisation, where trust and mutual support are more or less a given. In front of a wider audience of funding providers or other stakeholders, you will inevitably in my experience find yourself being challenged hard.

This is as it should be – scrutiny is a key part of a healthy system – but, at the same time, it’s always a shame to see an impactful project or programme struggle purely because its operators fail to sell it effectively.

Fortunately, while there are no black-and-white rules, there are some things you can do to improve your chances.

Confidence = Influence

When I present findings I do so with a confidence that comes with experience and from really understanding the underlying mechanics. But if you’re not a specialist and don’t have that experience there are things you can do to make yourself feel more confident and thus inspire greater confidence in your audience.

First, make sure you have thought through and recorded a data management policy. Are you clear how often data should be entered? If information is missing, what will you do to fill the gaps? What are your processes for cleaning and regularising data? Is there information you don’t need to record? A professional, formalised approach to keeping timely and accurate data sends all the right signals about your competence and the underlying foundations of your work.

Secondly, use the data as often as possible, and share the analysis with those who enter your data so that they can understand its purpose, and own it. Demonstrating that your data is valued and has dedicated, accountable managers hugely increases its (and your) credibility.

Thirdly, take the initiative in checking the reliability and validity of your own tools. If you use well-being questionnaires, for example, take the time to check whether they are really measuring what you want to measure in most instances. In other words, try to find fault with your own approach before your stakeholders so that when they find a weak point you have an answer ready that not only reassures them but also underlines the objectivity with which you approach your work.

Own Your Data’s Imperfections

Finally, and this might feel counterintuitive, you should identify the weaknesses in your own data and analysis and be honest about them. All data and analysis has limitations and being clear about those, and the compromises made to work around them demonstrates objectivity which, again, reinforces credibility.

In conclusion, the better you understand your own data and analysis, flaws and all, the more comfortable and confident you will feel when it, in turn, comes under scrutiny.

Image of data being analysed

There’s no Magic Way of Measuring Impact

Wouldn’t it be great if there was a way of measuring your social impact across multiple projects using a single dependable statistic? Well, I’ve got some bad news, and some good.

I was recently talking to a charity who wanted to know how if they could go about measuring and reporting the overall impact of the organisation on children and families. With multiple strands each aiming to achieve different things, they asked if a single outcome measure – one accurate, reliable number – to sum up the impact of the whole organisation was either possible or desirable.

First, here’s the bad news: it’s very unlikely – I might even be so bold as to say impossible – that any such thing exists. You might think you’ve found one that works but when you put in front of a critic (or a nitpicking critical friend, like me) it will probably get ripped apart in seconds.

Of course, if there is a measure that works across multiple projects, even if not all of them, you should use it, but don’t be tempted to shoehorn other projects into that same framework.

It’s true that measuring impact requires compromise but an arbitrary measure, or one that doesn’t stand up to scrutiny, is the wrong compromise to make.

The Good News

There is, however, a compromise that can work, and that is having the confidence to aggregate upwards knowing your project level data are sound. You might say, for example, that together your projects improved outcomes for 10,000 families, and then give a single example from an individual project that improved service access or well-being to support the claim. In most situations that will be more meaningful than any contrived, supposedly universal measure of impact.

Confidence is the key, though: for this to work you need to find a reliable way of measuring and expressing the success of each individual project, and have ready in reserve information robust enough to hold up to scrutiny.

Measuring Means Data

In conclusion, the underlying solution to the challenge of measuring impact, and communicating it, is a foundation of good project level data. That will also make it easier to improve performance and give you more room to manoeuvre. Placing your faith in a single measure, even if you can decide upon one, could leave you vulnerable in a shifting landscape.

 

Images showing analysis, in a light bulb to illustrate project evalution

You Might Be Winning but Not Know It

Have you ever eagerly awaited the results of a project impact study or external evaluation only to be disappointed to be told you had no impact? ‘How can this be?’ you might ask. ‘The users liked it, the staff saw the difference being made, and the funding provider was ecstatic!’ The fact is, if you’re trying to gauge the final success of a project without having analysed your data throughout its life, proving you made a difference is bound to be difficult.

Of course we would all like to know before we invest in a project whether it’s going to work. As that’s practically impossible (sorry) the next best thing is to know as soon as we can whether it is on a path to success or, after the fact, whether it has been successful. But even that, in my view, isn’t always quite the right question: more often we should be asking instead what it has achieved, and for whom.

In most cases – rugby matches and elections aside – success isn’t binary, it’s complex, but good data analysed intelligently can reduce the noise and help to make sense of what is really going on.

A service might in practice work brilliantly for one cohort but have negligible impact on another, skewing anecdotal results. Changes might, for example, boost achievement among girls but do next to nothing for boys, leading to the erroneous conclusion that it has failed outright. Or perhaps across the entire group, attainment is stubbornly unmoving but attendance is improving – a significant success, just not the one anyone expected. Dispassionate, unprejudiced data can reveal that your project is achieving more than you’d hoped for.

Equally, if the goalposts are set in concrete, consistently mining that data can give you the insight you need to learn, improve and change tack to achieve the impact you want while the project is underway. Or, at least, to check that you’re collecting and reviewing the right data – if the answer to any of your questions is a baffled shrug or an anecdote (and it too often is, in my experience) then you have a problem.

I’ll be circling back for a detailed look at some of the case studies hinted at above, as well as several others covering various fields, in later posts in this series.

In the meantime, consider the project that keeps you awake at night – where are its dark corners, and what good news might be lurking there?

Impact Management Programme Logo

GtD Approved Provider for Impact Management Programme

Get the Data are pleased to announce that we are an approved provider to the Access Foundation’s Impact Management Programme.

The Impact Management Programme aims to build the capacity of charities and social enterprises to manage their impact. This will help them to increase their social impact and diversify income.

Get the Data will support organisations to build impact measurement tools, develop impact plans, report performance, manage data, analyse data and design a theory of change. Please contact jack.cattell@getthedata.co.uk to learn how to take advantage of the fund.

Training is being held at locations across the UK for organisations who wish to participate in the programme:

  • London 9th February
  • Liverpool 23rd February
  • Birmingham 1st March
  • Bristol 23rd March

Visit http://accessimpact.org/events/ for further information or to book onto a training session.