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It’s not how ‘Big’ your data is – it’s what you do with it that counts.

By Brent Collyer, Pareto Fundraising’s new Head of Analytics, Insights and Data.

I’ve come from a strictly ‘for-profit’ background, where I’ve been wrangling data for Big Business and using it to drive change in the pursuit of the bottom line. So it’s been eye-opening, confronting and exciting for me to come to a sector where there’s so much more at stake.

In my first months at Pareto, I’ve had a chance to be on-site at charities where I can see the impact of fundraising, sometimes in the next room. I’ve been struck by how close people in this sector are to the problems they are trying to solve, and I’m seeing why it’s a passion – not just a job – for so many people who work in fundraising.

So, my challenge is to take everything I know about data and analytics from my corporate experience, and help charities turn that into money that solves problems. Here’s my take on how the fundraising sector can use the opportunities presented by ‘Big Data’.

If you build in rigour and integrity, you’ll do better, faster. 

Everyone’s talking about data, and these days almost everyone is collecting a lot of it. That volume is one of the ‘four Vs’ which make up ‘Big Data’. The other three are variety, velocity and veracity.

But, the speed and rate at which it’s collected brings some challenges. I’ve see many businesses struggle with unnecessary and unreliable data, poor quality data and just plain incorrect data. They come unstuck if they make decisions based on that data, or have to spend hours and dollars trying to clean it, migrate it, and untangle the mess it can make.

The opportunity cost of all that cleaning up is the insights they could be getting from the right, clean data.

The takeout: Invest in the fundamentals of hygiene, integrity, and a good structure you can build on. Standardise your data capture at the front-end, have disciplined processes, a well-trained team and regular audits. These things may not be the most immediately exciting part of ‘Big Data’, but they will set you up for success.

 Your ‘Useful Data’ is more important than ‘Big Data’

Once you have access to a lot of data, the temptation is to do everything you possibly can with it. Something that I understand completely.  But trying to analyse every bit of data is just going to spread your resources too thinly. Even if you did have unlimited human resources and budget, you’d burn them on work that would not necessarily return any value.

So one of the most important decisions a data analyst can make is, “What’s the right data to analyse for meaningful insight?”. Often this means making hard calls on what you choose to ignore or put to the side for the moment.

One very good way to make those decisions is by focusing on the metrics, rather than the data itself.  No matter what the size or complexity of your business, there are probably only five or six metrics which really reveal what you need to know. (Other metrics may be interesting – but that doesn’t mean they’re useful).

Metrics let you track, monitor and assess the performance of a particular program or process. And metrics help you identify further opportunities.

The takeout: Don’t try to do everything with your data. Instead, choose the most useful metrics and focus on those.  Over time as your data expands and your understanding of this data evolves so will the range of metrics and the insights they can provide.

Data analytics and predictive modelling can help you make better decisions about your fundraising.

Ten years or so ago, analytics and business intelligence (BI) began to change the way traditional reporting was being used to help businesses understand their environment, investments, customers and efficiency. Analysing data gives you insights you can really use.

Then came the evolution from ‘descriptive analytics’ to ‘predictive analytics’. Once businesses realised the potential of analytics, they started to develop and use predictive modelling.

Simply, predictive modelling is a way to predict the future using data from the past.  Data analysts uncover data patterns and use algorithms to forecast outcomes and create models that predict which actions or audiences have a high likelihood to succeed, and which have a high probability to fail.

Modelling tools can help you make investment decisions: “If we invest this much in one acquisition channel, and this much in another, what are our likely returns in the short term, long term, etc.”? It’s a lot cheaper, faster, and less risky than actually making those investments and watching what happens.

Once we have an understanding of the range of likely forecast scenarios we can then build predictive into prescriptive analytics.  This means providing advise as to what a company or client should do given a prediction.  Prescriptive analytics attempts to quantify the impact of future outcomes and help determine an appropriate range of commercial strategies.

From many years of data analytics and insights, I can see so many applications for advanced predictive and prescriptive analytics in the charity sector. With the power of cloud-based computing and growth in data collection, the sky is the limit for:

  • Donor intelligence – predicting and understanding donor behaviours.
  • Donor profiles – using predictive analytics to develop profiles of the best donors.
  • Qualifying and prioritising leads.
  • Product development and channel integration.
  • Targeting the right donors, at the right time, with the right content.
  • Reducing donor attrition.

The takeout: Predictive and Prescriptive modelling and analytics have superb utility. BUT – read everything above this section, and everything below – to really get the maximum value from them.   

The competitive advantage lies in data agility and enrichment.

Your data is a reflection of the real world, which is a living, breathing, constantly-changing entity.  The increasing velocity of data means that there is an ever-growing need to continually refresh our data and recalibrate our models but given that data is created as part of the human experience we need to continually evolve our understanding of what our data means.

The ever-changing face-to-face market, for example, means that Pareto has a highly skilled and professional team that continuously verify and update the assumptions and algorithms contained in our Regular Giving Forecasting and Life Time Value models.  The increase in the average life span of men and women over the past 12 years of Bequest Modelling has led to a complete redevelopment of that model to keep it as robust and accurate as possible.

As a general rule, the more good data you can feed into your ever-evolving data pool, the more usable insights you’ll get from it. And when I say ‘good data’, I don’t just mean your own data. Data enrichment allows you to get an understanding of what your data and customers look like to another business or industry or even country.  Pareto Fundraising Benchmarking is a case in point, and I’m looking forward to enriching that wonderful cache of information with data and insights from several new sources.

Finally, the most important aspect of enriching data comes from utilising those insights alongside a strong foundation of industry knowledge. As much as I believe in analytics and respect data, understanding the environmental factors that surround it are just as valuable as the data itself.

So, after working in the commercial sector for almost 20 years, specialising in Analytics and Insights,  I’m very much looking forward to tapping into the deep knowledge and experience in the not-for-profit sector – and mixing it up with the data insights that will help us all make more of a difference.


Brent Collyer, Head of Analytics, Insights and Data for Pareto Fundraising.

Brent has over 20 years’ experience across a broad range of domestic and international energy and financial markets.  Developing strategic insights that drive commercial outcomes through the use of data, analytics and technology.

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