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Artificial intelligence (AI) is no longer just a buzzword; it’s a transformative force reshaping industry. We’re currently past the initial ‘hype’ phase of AI and are now in what Gartner calls the ‘trough of disillusionment.’ Essentially, this means that everyone is realising that successful AI implementation isn’t as easy as plug-and-play. Without effective data quality, setup and management, businesses will fall short when trying to successfully drive value from AI. To maximise the benefits of AI and adopt successfully in 2025, business leaders must first lay the groundwork – starting with data.
The crucial role of data in AI
Data is the cornerstone of AI. Without accurate, relevant, and well-structured data, AI systems can’t function effectively. The integrity and quality of the data processed by AI is paramount to delivering accurate insights that in turn drive better business decisions and outcomes. Imagine pouring hot chocolate into your car’s fuel tank and expecting it to run – that’s what it’s like to feed inaccurate data into your AI.
Bad data – the Grinch that ruined AI
Using AI with low-quality or poorly organised data can have significant negative impacts:
- Flawed AI outcomes: Incomplete and inconsistent data can lead to skewed results and irresponsible outcomes due to biased models. Poor data quality directly impacts the accuracy and reliability of AI-driven decisions, potentially misleading business operations and strategy.
- Legal and financial implications: Incorrect data can result in severe legal and financial repercussions. Inaccurate reporting and non-compliance with data governance regulations can lead to fines, legal actions, and damage to the organisation’s reputation. Ensuring data accuracy and completeness is vital for maintaining compliance and avoiding costly mistakes. This is particularly important in sectors that hold large amounts of sensitive data, such as Healthcare and Financial Services.
- Ethical considerations: Data privacy and security are critical in the AI landscape. Protecting against cyber-attacks and breaches is essential to maintain trust and integrity in AI systems. Ethical considerations also involve safeguarding personal and sensitive information, ensuring that AI practices align with data protection laws and standards.
- Missed business opportunities: Poor data quality and organisation can lead to mistrust in AI outputs and missed opportunities due to unreliable insights. Businesses may fail to capitalise on valuable trends and patterns if their data foundation is weak, ultimately impacting their competitive edge and growth potential.
Key steps to order your data and get AI ready
High-quality, well-organised data is essential for AI to deliver accurate and valuable insights. Laying a solid foundation for AI involves several key steps:
- Conduct a data audit: Start by auditing your existing data to ensure its accuracy, completeness, and relevance. This involves assessing data quality, cleaning up duplicates, and updating outdated information. A thorough audit will identify gaps and inconsistencies that need to be addressed. Good data hygiene practices, such as regular auditing, cleaning, and updating, are vital to maintain data integrity and reliability.
- Prioritise data management: Effective data management is essential for AI readiness. Centralising data to improve its quality and accuracy is a critical step. Eliminating silos and adopting efficient data management practices will streamline data access and processing, facilitating smoother AI integration.
- Leverage machine learning: Utilising historical data to make predictions and ease into AI adoption is a strategic approach. Assessing the entire IT environment, including databases, infrastructure, and analytics, ensures that the organisation is prepared to support AI initiatives. Leveraging machine learning can provide valuable insights into past trends, aiding in future decision-making.
- Modernise your data infrastructure: Modernising your data infrastructure is the first step towards embracing intelligent technologies. This process involves integrating external data for a comprehensive view, simplifying analysis and report compilation, enabling real-time analytics, and enhancing the use of intelligent technology. Modernisation saves time and improves flexibility and adaptability, making it easier to scale AI efforts.
Laying the foundations
Preparing your data is the first crucial step towards successful AI implementation. By conducting thorough data audits, prioritising data management, and modernising your data infrastructure, your business can lay a solid foundation for AI. You should see your data strategy as a natural extension to your overarching AI strategy.
Investing in this preparation will unlock AI’s full potential, driving substantial business value and positioning your organisation for long-term success in an AI-driven world.
Martin Brierly is data and AI practice lead at ANS, a digital transformation provider and Microsoft’s UK Services Partner of the Year 2024. Headquartered in Manchester, it offers public and private cloud, security, business applications, low code, and data services to thousands of customers, from enterprise to SMB and public sector organisations.
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