Someone has to say it: the analytics capabilities of so many large enterprises are entirely substandard.
Despite vast investment in data collection and analysis, it’s now something of a dirty secret that many executives don’t even trust their own numbers.
This is a dangerous problem, yet few enterprises openly acknowledge it. Instead, they continue making decisions based on disjointed, incomplete, or misleading data, exposing themselves to strategic missteps and competitive threats.
Tech giants like Netflix and Facebook — digitally native and with scandalously deep pockets — have set the gold standard for data-driven decision-making. They operate with seamless, real-time, AI-enhanced analytics that provide a comprehensive understanding of customer behaviour.
Most other enterprises, however, are stuck in a different reality entirely. Their data is usually fragmented, locked in silos across different teams and systems. Inconsistent datasets make it impossible to achieve a single, complete version of the truth about customers, or anything else for that matter.
The irony is that the larger and better-resourced these organisations are, instead of miraculously transforming into a Netflix or Facebook, they find it harder to achieve a joined-up view of data. Separate teams — analytics, business intelligence, management information — tend to operate in isolation, using different systems and sources. Marketing teams, for example, may focus on web analytics, while operational teams rely on different performance metrics. Data doesn’t flow consistently across the business, and crucial insights are lost in translation.
Worse still, these businesses are only now realising how far behind they are. The rise of AI — which marks a generational shift in technology that will impact every business — has forced a bout of introspection. They now understand that AI’s transformative potential depends on clean, structured, and unified data. Without fixing these foundational issues, enterprises will struggle to compete with more agile, data-savvy rivals.
Poor analytics simply leads to poor decision-making. Whether it’s feeding AI, inventory management, customer retention strategies, or marketing spend allocation, businesses that rely on flawed data risk making costly mistakes. This goes for customers too. Enterprises that fail to personalise experiences based on accurate data will see engagement drop as competitors offer better, more relevant interactions. The challenge, therefore, isn’t just about collecting data, but using it effectively.
The strategic rethink
So how do businesses fix this? Quite clearly they must start with a complete rethink of their approach to data. Many enterprises still operate under outdated models — often hereditary — that prioritise third-party over first-party data. But the future belongs to businesses that build direct, trusted relationships with customers, capturing their precise, relevant and timely data ethically and leveraging it intelligently. This shift requires a new mindset that values data as a strategic asset rather than a byproduct of disparate operations.
This means breaking down silos is essential. It’s staggering how many businesses still fail to understand how much this holds them back. Separating IT and business functions and treating data analytics as an isolated technical problem rather than a core business priority remains, in my view, the single most important area to focus on.
A digital-first approach is essential, where teams across departments collaborate seamlessly. Modern API-driven platforms such as Customer Data Platforms (CDPs) and advanced CRM systems can help integrate and unify data, ensuring that insights are accessible and actionable in real time.
Of course, effective leadership is required to drive this transformation. Many executives assume their organisation is more data-driven than it really is, simply because they have invested in analytics tools. Other businesses have a hunch — not always shared with the senior leadership — that things aren’t great, but everyone is getting by. But having data is not the same as using it effectively.
Leaders must ask tough questions here: Do we trust the numbers we base decisions on? Are our data sources truly unified? Are we measuring the right things? External audits or fresh internal assessments can reveal uncomfortable truths, but only businesses willing to confront their shortcomings will improve.
Align with business objectives
Finally, a successful data strategy must also be measurable. Too often, enterprises launch data initiatives without clear KPIs or accountability. If a company spends millions on analytics but cannot quantify its impact, that investment is wasted.
The best organisations tie their data strategy directly to business objectives — whether that’s increasing revenue, improving customer retention, or enhancing operational efficiency. Every data-driven initiative should have a clear return on investment and a direct link to business goals.
If you’ve got this far and still think your analytics are in a good place, then congratulations. But if there’s any doubt, now is the time to act. The AI revolution is going to be the litmus test for future preparedness for most businesses. Fail it, and things will become very difficult indeed.
The good news, at least, is that fixing this does not require operating like a Silicon Valley tech giant. Incremental improvements and honest assessments can drive significant gains. It’s true that most things come out in the wash, and the dirty analytics secret so many enterprises harbour is due a good rinse.
About the Author
Neil Trickett is UK MD at global digital transformation business Apply Digital. Apply Digital is a global digital transformation partner for change agents. Leveraging expertise that spans Business Transformation Strategy, Product Design & Development, Commerce, Platform Engineering, Data Intelligence, Marketing Services, Change Management, and beyond, we enable our clients to modernize their organizations and deliver meaningful impact to their business and customers.
