A Bite of the Elephant: Data & AI Enablement vs Governance. Is There a Difference?

When we know that accuracy in enterprise data is critical for driving AI success, how can businesses build resilient, adaptable data infrastructure that supports this AI-driven growth?

Surely a whopping data governance project is the silver bullet?

Data governance programmes – outdated?

The problem is that the situation with data governance projects hasn’t changed.

Organisations still aren’t sure exactly where to begin, they often face an extremely costly project and are struggling to secure buy-in for what might morph into a multi-year commitment. Not only this, but data governance has a connotation of someone saying ‘no, no, no’ repeatedly. And that sounds like someone trying to stall progress, not enable it.

So what can be done?

Here’s a better approach

The answer lies in taking a “bite by bite of the elephant” approach. You need a plan that’s phased, budget-friendly and outcome driven.

It’s not about data governance; it’s about data enablement.

But again, the question crops up – where to begin?

This is where Data and AI Enablement as a Service is ideal for organisations – from SMBs to enterprises.

By tailoring your path towards better data and adopting a “bite-by-bite" strategy based on your priorities, you'll achieve faster results with clear goals and outcomes. This, in turn, enables you to adopt and implement AI initiatives in phases - backed by clean, well-managed data.

This service is structured in phases, each with distinct goals and pre-determined costs. This ensures you maintain the benefits of a standard governance programme - such as expert guidance and up-to-date insights - but it’s a far more achievable and sustainable approach to improving data.

Flexibility is another advantage. Unlike some rigid data governance programmes, this service isn’t a one size-fits-all. You can make agile changes, addressing specific business priorities to build a truly data-driven business.

GenAI can be a game-changer, boosting efficiencies and productivity. But we also need to ensure these systems work within set guardrails, consistently produce reliable and accurate outputs, and remain as transparent as possible. This can only be done when your data is in top-notch condition.

How are you approaching data improvements in your organisation? What will it take to gain an AI-driven competitive advantage?

Let's talk about it.