By Katie Inder
Most organisations in Guernsey don't have a data problem. They have a data use problem.
The spreadsheets exist. The reports get generated. The figures come in every week. But somewhere between collection and decision, most of that information quietly loses its value - not because it's bad data (although data quality can be an issue), but because the people working with it every day haven't been given the tools to do anything meaningful with it.
That's the data literacy gap. The inability to look at datasets you work with and ask: what is this data actually telling me, and what could I do with it? How could it bring value and opportunity to the business, and to me? What action is this indicating I should take?
It's already in your hands
Think about the data that passes through a typical professional's week. Customer interactions. Sales figures. Project timelines. Supplier costs. Error rates. Staff hours. Response times.
Most of it gets logged, filed, or reported upwards - and that's where it stops. Very few people are taught to look at that data as a source of leverage. To notice patterns. To spot when something's quietly going wrong before it becomes a crisis. To go further than the task at hand and identify where the low-hanging fruit actually is.
This isn't a failure of intelligence or ambition. It's a skills gap. And it's one that's almost entirely fixable.
What data literacy actually means in practice
It's not about learning to code. It's not about becoming an analyst. It's about developing the habit of asking better questions of the information you're already working with.
Why is this number higher this month than last month, and does that matter? Are we measuring the right thing here, or just the easy thing because that’s what we’ve always done? If I looked at this differently, would I reach the same conclusion? What decision could I make better if I understood this more clearly?
It's a cultural shift when teams develop the confidence to challenge a dashboard, to spot a suspicious trend, to ask "what are we actually measuring here?" and “what are we doing about it?”
These are critical thinking questions that require a mindset - one that treats data as a working tool rather than a reporting obligation. The insight organisations need is usually already there, sitting in spreadsheets that aren't being read critically, in reports that nobody questions, in data that's collected dutifully but never interrogated. Tools like Power BI exist precisely to change that: to make it practical for everyday professionals and not just analysts to visualise, explore and act on the data they already hold.
Why this matters more now
AI tools are accelerating all of this - for better and for worse. The better: genuinely useful automation, faster analysis, patterns that would have taken weeks to spot now visible in minutes. The worse: it becomes even easier to generate confident-looking outputs from poorly understood inputs. AI can produce a beautifully formatted chart, a compelling summary, a confident-sounding conclusion - in seconds. What it can't do is tell you whether the underlying data was any good to begin with. It can't flag that the sample was too small, or that the time period was cherry-picked, or that a key variable was left out entirely.
That judgement still belongs to humans. And if we're building faster, smarter tools on top of a weak data foundation, we're not moving forward - we're just making mistakes more efficiently.
The skill of knowing what questions to ask of your data - knowing whether to trust what you're seeing - is becoming ever more important.
The GTA course
This is what the Data Literacy and Power BI courses I'm running with the GTA are built around - the practical skills of working more effectively with the data that's already part of your job.
Data Literacy is for professionals who sense there's more value in what they're already collecting but aren't sure how to unlock it. Power BI takes that further: giving you the hands-on capability to connect, visualise and share data in ways that actually drive decisions.
Together, they cover the full picture - the mindset and the method. Visit the GTA website to find out more.
Foundations of Data Literacy | GTA – 21st May
Introduction to Power BI | GTA – 15th June & 22nd June (2x half days)