Andy Baillie, VP of UK and Ireland at Semarchy, and Callum MacBurnie, Chief Revenue Officer at Amplifi, explore some key use cases where AI’s potential can be unlocked through robust data management practices.

AI and data management combine like yin and yang – two forces that elevate each other’s potential. AI is immensely powerful, but only as insightful as the data it learns from. Strong data practices, on the other hand, allow organisations to truly maximise AI’s capabilities.

Yet, for most, there’s a big gap here. According to the International Data Corporation (IDC), only 14% of employees say the data insights surfaced in their daily work are actionable. How can organisations bridge this divide? By striking a harmonious balance between disruptive AI use cases and solid data foundations.

It’s a virtuous cycle – AI enhances data processes while high-quality data fuels more impactful AI insights. But what does this symbiotic relationship look like on the ground?

Data quality monitoring

Data quality is the bedrock on which AI systems are built. After all, you wouldn’t want an AI making critical decisions based on flawed or inconsistent data. By integrating AI into data quality monitoring, organisations can automate the detection of anomalies, errors, and inconsistencies across their data assets.

Here’s how it works: AI models are trained to learn the patterns and rules that define proper data quality for your organisation. Once deployed, these models continuously monitor data flows, flagging any violations or anomalies for review. It’s like having an extra set of eyes on your data 24/7.

This advantage reduces manual effort for data stewards while catching issues early before they spread downstream. No more tiresome manual checks – the AI handles the heavy lifting, while human experts can focus on remediation. The result? Higher-quality data pipelines you can trust to fuel reliable AI insights.

Intelligent data mapping and integration

Data integration projects often involve a ton of monotonous mapping work as you wrangle data from various sources into a unified view. But what if AI could make that process smarter and faster? By applying machine learning, organisations can accelerate data mapping and integration.

AI models are trained on your data architecture, metadata, and existing mappings. When new data sources are introduced, the models intelligently suggest mapping rules and transformations. They can automate aspects of the mapping process by understanding the semantic relationships between data fields across sources.

This gives you another pair of hands to help with the process while still putting human experts in the driver’s seat. This intelligent automation augments your team’s expertise to streamline integration projects, meaning you can rapidly incorporate new data sources while reducing manual mapping efforts.

Automated data classification

Having a solid handle on your data landscape is critical for effective data governance and AI enablement. But manually cataloguing and classifying all your data assets is a herculean task. This is where AI can lend a hand through automated data classification.

AI models can be trained to understand your data taxonomy, metadata conventions, and business glossaries. Once deployed, these models will automatically examine new data sources, classify them based on type, content, and relationships, and apply the appropriate metadata tags, meaning less manual classification work for your data stewards.

AI-driven data classification gives you a more comprehensive and up-to-date view of your data estate. This enhanced data governance provides the solid foundation needed to build and deploy AI use cases confidently.

Extracting insights from unstructured data

Most organisations’ data resides in unstructured forms like documents, emails, images and more. But these untapped sources contain a wealth of insights that could drive smarter decision-making if you could only make sense of the noise.

This is where AI’s unstructured data processing capabilities can pay handsome dividends. With techniques like natural language processing and computer vision, AI models can automatically parse through these unstructured data stores to identify important entities, topics, sentiments and other relevant metadata locked within the content.

This bridges the gap between your structured analytics systems and the unstructured data lakes often neglected due to complexity. With AI’s help, you can finally incorporate those untapped data sources into your analysis and decision flows.

Monitoring for compliance violations

Data privacy and ethical AI practices are top of mind for any responsible organisation today. With AI’s automated monitoring capabilities, you can ensure your data management and AI model behaviours stay compliant and unbiased.

AI can continuously monitor data flows to detect sensitive personal data and ensure it is handled per regulations like GDPR. For AI models, techniques like AI explainability allow you to peer into the ‘black box’ to identify potential sources of bias, discrimination, or other ethical risks.

It’s a proactive approach to catching issues before they escalate into larger problems. By keeping a vigilant AI eye on your data practices and model behaviours, you can confidently ensure compliance violations won’t slip through the cracks.

Intelligent data preparation for cloud migrations

Cloud migrations are becoming inevitable for most organisations. Yet, preparing and refactoring your data for the cloud is often where these initiatives get bogged down in manual toil and errors. AI can help accelerate and streamline this data preparation process.

AI models can be trained to intelligently profile, map, and transform your data for cloud migration. This includes automating tasks like schema conversions, encoding changes, data mapping, and other transformations. The AI handles the tedious grunt work, reducing manual effort.

By employing AI for this data prep work, organisations can complete complex migration projects that would otherwise be labour-intensive and risk data quality issues. This means fewer cloud migration headaches.

Time to harmonise?

AI and data management share an interdependent, yin-yang dynamic. This powerful combination creates a virtuous cycle that continuously elevates both forces when harmonised through responsible practices and governance.

On one hand, AI augments data management processes – automating tasks, surfacing new insights, and enhancing data quality. On the other, robust data foundations empower AI systems to reach their full potential.
The path forward is clear for organisations looking to maximise their AI investments. Embrace this symbiotic relationship by prioritising strong data management practices that put AI’s capabilities in a position to truly shine.