INDUSTRY VIEW enterprise data that everyone, from executives to frontline staff, can trust. Like a chef who knows exactly what’ s in their pantry and how fresh it is, with data you can trust, you can cook up something extraordinary, without unpleasant surprises.
One foundation, many use cases
Let’ s explore this in practice by taking the example of an innovative bank. Its marketing team is keen to leverage AI to offer hyperpersonalised financial products. They want to know when a customer might be buying a home, starting a business or sending their child to university so they can provide timely, tailored offers. Meanwhile, their fraud detection team wants to use AI to spot suspicious patterns in real time, saving customers the need to self-report.
Two vastly different use cases. But they share one foundational need: up-to-date, accurate behavioural and transactional data. The marketing team can’ t personalise without knowing the customer. The fraud team can’ t protect without understanding what‘ normal’ looks like. If each team tries to build its own data pipeline, you end up with duplication, inefficiency and inconsistent insights.
Instead, by investing in a common, trusted data layer – like we at Informatica call turning chaos into business value – you empower every department across the organisation to innovate, scale and advance. According to McKinsey, as much as 70 % of the effort in developing AI solutions is spent on wrangling and harmonising data.
Solve that once, and your entire organisation becomes more agile, more aligned and more AI-ready.
Getting your data AI-ready
So how do you get there? It starts with acknowledging that data is not just an IT problem, it’ s a strategic asset belonging to the entire organisation. Preparing your data for AI involves more than cleansing spreadsheets. It requires a robust data governance framework that ensures integrity, compliance and accessibility. It requires scalable infrastructure, ideally cloud-based, that can grow with your ambitions. It requires investment in data
Poor data quality destroys business value.
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