Guest blog (NTT Data): Without data trust, AI can’t be leveraged for growth and innovation

Learn more about why trust in data is essential for AI success

Data is the currency of competitive advantage. Yet, despite substantial investments, many businesses find themselves at a critical juncture, wondering why the true value of their data remains elusive.

The issue of trust in corporate data lies at the heart of this challenge. It's a conundrum that casts a long shadow over our journey towards data-driven excellence. Without trust, the remarkable capabilities of Artificial Intelligence (AI) can't fully address our most pressing business challenges.

After all, the AI revolution isn't just about capabilities; it's about the data that the AI consumes to generate these game-changing outcomes; and reliable data is heavily dependent upon the people and processes surrounding it.

People, processes and culture drive data success

Historically, organisations have poured resources into data-related initiatives, but the returns often fall short of expectations. We tend to pin our hopes on technology as the ultimate saviour, believing it can single-handedly extricate us from this predicament. The reality, though, is that technology alone cannot solve this puzzle. It demands the alignment of people, processes, and culture to become truly data-driven.

Effective data governance is the linchpin of the transformation towards data excellence. Data must be well understood, sourced from a common reliable origin, governed comprehensively, applied enterprise-wide, and driven by specific use cases if it is to truly be trusted. Enterprise-wide data trust is a journey, not a destination.

Trust in corporate data

Today, one of our most significant challenges is the erosion of trust in corporate data. AI, in all its forms, relies critically on the accuracy, completeness, consistency, conformity and integrity of the data. To harness the full potential of AI, we must first ensure trust in our data and its governance. This is the bedrock upon which we can optimise the inevitable shift driven by today's AI variants.

As we become data-driven organisations, we must address the challenges which continue to stand in the way of progress. Data skill gaps are pervasive, and investing in new projects without addressing these gaps is akin to treating symptoms rather than the root cause. What's needed is a cultural shift, that recognises the importance of data and AI, resonating from the top leadership to every corner of the organisation.

Becoming data-driven takes time and dedication. While businesses are eager to turn raw data into immediate insights, the reality is that transformation is a methodical journey with a series of steps. It demands strategy, skill refinement, literacy and the right tools. Instant gratification is tempting in the short-term, but data-driven success unfolds through sustained effort and continuous improvement.

How to prepare for AI: dispelling myths

The question ringing in boardrooms is, "Are we ready for AI?"

Success in the data and AI journey requires technological prowess and a holistic transformation. We must align the data strategy with business goals to bridge the gap between technological potential and organisational readiness. This alignment is the key to enabling organisations to truly harvest value from their data and act upon its insights.

AI isn’t a universal remedy. Successful AI implementation relies on pinpoint focus on relevant use cases, as data insights gain significance when woven into broader business objectives. The future will see AI orchestration platforms becoming commonplace.

According to research from Gartner, by 2025, 50% of enterprises will have devised artificial intelligence orchestration platforms to operationalise AI, up from fewer than 10% in 2020. As the AI tide rolls in, optimising AI to fit your business will be a key factor in success.

Commitment to data is one thing but translating it into actionable insights is another. The bridge between intention and action lies in fostering data literacy and enabling leaders to trust what the data reveals. Trust in AI applications is paramount and emanates from including human judgment in decision-making.

Establish data governance en route

When data is untrusted, we find multiple versions of the truth, manual report production, valuable data which isn’t used and ultimately increased operational costs that are often hidden. The starting point is making the case for change, assessing current data trust maturity, and setting baseline standards for data quality assessment.

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