Three Steps to AI-Ready Data: Results You Can Trust


Blog By: Tendü Yoğurtçu, PhD, Chief Technology Officer at Precisely

It’s no secret that artificial intelligence (AI) has been top of mind for businesses across industries and around the globe in recent years – and it’s easy to see why.

These technologies unlock tremendous opportunities for organisations needing to increase productivity and efficiency to keep up in a dynamic, and ever-evolving, technological landscape. When done right, AI enables:

  • valuable insights
  • better decision-making processes
  • greater innovation – faster

With outcomes like that, the possibilities seem endless – whether you want to leverage AI-powered assistants to improve customer experiences, deploy AI-powered workflows that streamline operations, and more. But to reap those benefits, your data must be up to the task.

That’s because when it comes to AI, it’s not just “garbage in, garbage out” – it’s “garbage in, garbage everywhere.” Unfortunately, that’s where we see so many organisations faltering in getting their AI initiatives off the ground. According to a 2023 report by Gartner, only 4% of organisations report that their data is “AI-ready.”

Trusted AI requires trusted data. When models are trained on inaccurate or biased datasets, the outputs will likewise be flawed and skewed – putting your business at risk.

The key to accurate, trustworthy, and ultimately successful AI initiatives is data integrity – data with maximum accuracy, consistency, and context. While the journey to trusted data can feel overwhelming, it doesn’t need to be. Here, we’ve outlined three key steps that will help you overcome data trust issues and drive AI-readiness.

Solving AI Challenges with Trusted Data

When you think about your own top AI use cases, what comes to mind? As we’ve mentioned, innovation and efficiency gains are priorities for many, but AI technologies don’t solve data management issues on their own.

Here are three crucial steps to take within your data management practices to ensure accurate, relevant, and contextualised data that fuels trustworthy AI outcomes.

  • Ensure Access to Critical and Relevant Datasets

A more complete dataset helps you realise the full potential of your AI. However, this is easier said than done as many companies lack a holistic view of all their datasets.

When your data is siloed across different systems and isn’t easily accessible in the cloud environment where AI is managed, the AI models don’t have a clear understanding of the big picture. The datasets may be partial to specific geographies or customer demographics, leading to biased and unreliable outputs.

To address this and help produce trustworthy results, you need to break down data silos and integrate critical data from all relevant sources across your landscape – on-premises, in the cloud, and in hybrid environments (including complex mainframe data).

  • Build Data Accuracy and Consistency

Alongside step number one, the data within your critical datasets needs to be timely and accurate. That’s accomplished with the implementation of robust data quality and data governance measures. Rigorous data quality measures will ensure your data is accurate, consistent, standardised, and duplicate-free. High-integrity data also needs to be timely and governed using a robust framework.

To take these measures even further, incorporate data observability tools into your strategy. Businesses that focus on data observability can use powerful machine learning techniques to gain a better understanding of data health, reduce risk, and proactively identify and solve data issues to prevent bigger downstream issues.

  • Unlock Greater Power from AI with Data Context

For more contextualised, nuanced, and relevant AI responses and recommendations, enrich your data with spatial insights and third-party information.

Context is a key element of data integrity, and datasets that provide insights into factors including points of interest, demographics, and risk, are critical to maximising the accuracy and relevance of AI outputs.

For instance, when using AI tools to output natural catastrophe insights or predictive modelling, you can add detailed geospatial points such as address information or environmental risk factors to uncover otherwise hidden patterns in your data and gain more informed results.

Ready to Start? Future-Proof your AI with Trusted Data

The AI discussion certainly isn’t expected to quiet down anytime soon. With new technologies constantly coming to market it seems clear that AI will continue its exponential rise, particularly as leaders continue to prioritise improved efficiency and productivity across their organisations.

For your AI success story, you first need to focus on building a foundation of data integrity. After all, there’s no trusted AI without trusted data. Accurate, consistent, and contextual data is your key to AI initiatives that are high-performance, reliable, and produce quality outputs that take your business to the next level.

Are you attending the Enterprise AI & Big Data event on June 19th? If so, don’t miss Sadat Ahmed, Senior Sales Engineer at Precisely, presenting “AI You Can Trust: Embracing Data Integrity Throughout the Development Lifecycle.”