It is a cliché to say that we live in a data-driven world. Almost every digital move we make leaves a footprint about who we are and what we seek, which – to any savvy business and decision-making power – is a potential gold mine.

For the research community and our clients, a rich set of data offers untapped opportunities for insight, analysis and an evidence base for future change. But can there be too much data? Is the seemingly endless pursuit of ever more granular data taking precedence over the pursuit of meaningful answers?

The modern research industry is dependent on the need for good data, intelligent analysis and an evidence-based springboard for change. Strange as it might sound, for researchers it also remains important to ensure the questions – which are at least as important as the answers – keep coming.

This is where good researchers come in.  We can help organisations gather and make sense of all that data.

How do we do this well and what are the pitfalls to avoid?

Good planning and design

As researchers, we can often find ourselves up against tight time constraints. This can create a paradox – a need for robustness, precision and data (lots of it), but little time for collaborative design and a report often due before the research has had much time to get off the ground. Managing expectations here is essential.

Common research design challenges in my own specialist field of evaluation include programme monitoring data being rolled out before finalising the theory of change (leading to a potential mismatch between the two); the monitoring data burden on programme participants being disproportionate to the resources they had hitherto committed; and/or a misconception that UK GDPR means that no data can be shared with anyone – ever – under any circumstances.

For researchers, these issues mean we can’t always access the data we need, work with it, or trust it – which is where those all-important reporting caveats come in.

Proportionate data collection

If we really put our minds to it, we could probably glean sufficient answers to inform meaningful change by examining and synthesising existing data, then introducing fewer primary research questions than we sometimes tend to ask. Admittedly, that’s easier said than done when there are multiple stakeholder contributors at design stage, which can lead to lengthy primary research scripts.

In these circumstances, it’s important to consider the “essential” versus “nice to have” questions and not be afraid to make necessary cuts. Anything that feels unwieldy or overwritten risks losing the most important person in the room – the research participant.

We are all bombarded with data requests every day, be it to rate the cardboard box that our home deliveries arrived in, or how satisfied we are with the satisfaction survey. We’re all at saturation point – hence the need to be selective in what we ask of research participants, proportionate in how much we ask and fair in what ethical incentives we are willing to offer.

Crafting the story

Once we have skilfully amassed all the data we need, statistical and content analysis techniques can help us to find patterns, associations, meaning and any red herrings. The more data, the more red herrings. No problems there – spotting those is what we’re good at.

A researcher then comes into their own by crafting the story and the narrative to answer the crucial research questions. A vitally important skill here is maintaining that broader view of the wood beyond the trees – understanding what the data are really telling us and skilfully unmasking the answer to the biggest question of all, the “so what?”.

As a research professional, making a tangible difference in some way is arguably the ultimate reward, but this can be thrown off kilter if the data are unreliable or priorities change. If plans are paused, findings gather dust and the data risk becoming obsolete. Repeating research and keeping it up to date is important, but it’s always good when the findings feed into meaningful change.

Where do we go from here?

As we find ourselves grappling with the many and varied data challenges we face, we should ensure we always understand the intended use of the evidence, identify the questions that are important and realistic, research the answers in a proportionate way, and encourage participants to answer truthfully.

The timeless BBC sitcom Yes, Minister reminds us that our ability to control the endless quest for data may ultimately be out of our hands:

Jim Hacker: “We need to do a time-and-motion study to see who we can get rid of.”

Sir Humphrey Appleby: “Ah, well, we did one of those last year.”

Hacker: “And what were the results?”

Sir Humphrey: “It turned out that we needed another 500 people.”

Adrain Smith is senior research director at Pye Tait Consulting.