By Chris Turner, Sales Director at Go Inspire
Businesses are swimming in data.
Every customer interaction, ad click, website visit…
It all generates a digital footprint, contributing to a growing mountain of information. In theory, this empowers brands to make smarter, more precise decisions.
But it’s time for a reality check.
Many organisations find themselves paralysed by lengthy internal processes and the sheer volume of data. Organisations are struggling to extract meaningful insights, quickly. Decision-making remains slow, proving ROI is an ongoing battle, while fragmented data create an incomplete view of marketing performance.
The illusion of data-driven decision making
Marketers are often told that data is the key to success. The more data you have, the better your decisions will be.
But this assumption is flawed.
Marketers can’t look for straightforward answers to questions such as “last year’s best-selling product” or “a region’s top-performing campaign”.
You might check your e-commerce platform for online sales, then a separate retail system for in-store purchases.
But what about returns? Are they accounted for? What if sales data is stored differently in different regions?
If you have a centralised source of truth, it’s likely you need to go through another department to access it, but not without creating a brief, submitting it and waiting in a queue.
Organisations get trapped in a cycle of data requests. Analysts spend their time building and running queries to pull specific numbers and then building interpretations of these numbers to contextualise the answer.
When they have responded with their answers, we all know the next action is “thanks, but I have another question.
Lo and behold, we’re back to square one.
This approach dramatically slows down decision-making. Reacting to challenges and spotting areas of immediate return are almost impossible. Moreover, this also wastes valuable resources. Highly skilled data professionals are often bogged down answering basic queries instead of working on more strategic initiatives.
More data, more confusion
One of the biggest challenges marketers face is differentiating between data volume and data quality. Having five million customer records sounds impressive, but what if those records only contain a handful of relevant data points?
As the quantity of data grows, so does the potential for errors. When data is spread across multiple systems, discrepancies creep in, fields are formatted differently, some datasets are more up to date than others, and critical insights get lost in translation.
Even if an organisation manages to bring all its data together, interpretation remains a challenge. Two analysts working with the same dataset can produce different results simply based on how they query. This subjectivity makes it difficult for businesses to get a, consistent picture of what’s happening.
Data overwhelm can cause confusion, with organisations sometimes becoming mistrustful of data.
Fragmentation: the hidden cost of disconnected data
Data silos are another major roadblock to effective marketing decisions. Most businesses store information across multiple platforms: CRM systems, social media analytics etc. Each of these operates in isolation, making it tricky to form a complete view of customer behaviour.
The result?
Inconsistent reporting, inefficiencies, and a lack of clear attribution.
Imagine a direct mail campaign whose success is measured purely on the number of calls received. It might look like a failure. But what if organic search traffic surged on the same days the mail was delivered? Without connecting these dots, brands risk underestimating the true impact of their marketing.
The solution is most likely data-based and scientific in approach. But you can’t truly harness the power of data if your team is too bogged down in the ‘basic’ queries and overloaded with general business requests.
The AI-powered solution
So, how can marketers escape the data trap?
The answer lies in AI-driven intelligence. AI-powered tools can automatically surface insights, identify trends, and optimise budgets in real time.
Marketers must implement these systems, instead of running SQL queries or chasing down reports. They can simply ask a question for internal data.
“How did our email campaign impact sales last week?”
Instead of waiting days for a report, they get an instant, actionable answer.
AI also helps mitigate the issue of human bias. By standardising data processing and analysis, AI ensures that decision-makers receive consistent insights rather than subjective interpretations.
This doesn’t call for the cull of data and insight specialists. Far from it. They play a crucial role in reviewing the data and addressing the wider challenges, without being weighed down by minutiae. The integration of AI fulfils their roles to a greater extent.
Moving beyond data overload
For marketers, the goal shouldn’t be to collect as much data as possible but to make the right data work harder and faster. That means:
- Prioritising quality over quantity – focus on the most relevant data points that drive business impact.
- Breaking down silos – unify data sources to create a single, accessible version of the truth.
- Leveraging automation – use AI-powered tools to process and analyse data efficiently and elevate the strain on resource
- Focusing on actionability – ensure that insights are reliable and swift and lead to measurable marketing decisions.
Ultimately, data should empower decision-making, not complicate it. Marketers don’t need more numbers; they need the right intelligence and the right timelines. With a revised approach, brands can move from being data-rich but insight-poor to data-driven, driving new initiatives and identifying not just marginal gain but measurable positive business impacts.
