Why Data Preparation Is Critical for AI Success
As AI adoption continues to grow, the importance of data preparation and governance cannot be overlooked. Today’s article explores why clean, organised and well-governed data is the foundation of any successful AI project.
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Artificial intelligence is no longer a buzzword, it has proved to be a transformative force that is changing how businesses operate across the globe. From automating routine tasks to predicting customer behaviour, AI enables businesses to work smarter, faster and more efficiently. According to Storm’s 2024 Modern Workplace Report in association with TechCentral, 63% of IT leaders expect spend on AI to increase in 2024 and 59% believe their company needs to adopt AI to be competitive over the next 3 years. As businesses strive to increase investment in AI, it is vital to understand one crucial element of AI adoption – AI is only as strong as the quality of data it works with. Data preparation before implementing AI is essential to ensure your business can unlock the full potential of its AI investment.
Why is Data Preparation Crucial?
While AI may appear revolutionary, it is not magic and it relies entirely on the data it is fed to function properly. If your business has inaccurate, incomplete and unorganised data, your AI project is more likely to fail. In a Workday survey, 77% of participants were concerned that organisational data was neither reliable nor timely enough to use with AI or machine learning.
Before adopting AI businesses need to allocate time to ensure data preparation is complete. Data preparation involves ensuring data is clean and complete – identifying and removing duplicates, filling in missing values, resolving errors and standardising formats across datasets. The next step for businesses is organising data and making sure it is accessible. Often, businesses store their data in silos, separated by systems across different business functions and departments. It can be useful to consolidate data from various sources to ensure AI has a comprehensive view of the business. This helps ensure AI has a variety of data to be trained on too.
Data cleaning and organising can be a tedious task, particularly for businesses that do not have efficient data management processes in place already. There are a number of tools and platforms that can help businesses streamline data preparation as well as skilled technology partners who can offer expert advice.
What are the Implications of Poor Data Preparation?
Failing to prepare data can lead to significant problems. When AI is trained on incomplete, biased, or poorly organised data, its outputs will be equally flawed. For example, using an AI marketing tool trained on incomplete data may wrongly predict customer preferences leading to failed campaigns and wasted sources. Further, operational inefficiencies can occur. AI relying on poor-quality data may lead to incorrect automation resulting in system errors. Rather than streamlining processes, AI can introduce new bottlenecks or create additional work for employees. There is also a risk of financial loss, investing without ensuring data is ready can result in wasted time and money. Businesses may find themselves having to rework data management across the entire organisation or in extreme cases abandon AI projects due to data quality issues.
The Importance of Data Governance
While data preparation is critical, so too is data governance. In the same Storm 2024 Modern Workplace report, 77% of Irish It leaders say their organisation is concerned about how to govern AI. Data governance refers to the policies, procedures and standards that ensure data is managed properly across the organisation. Good data governance ensures businesses comply with relevant regulations that govern the use of personal data. Having a solid governance framework allows companies to track where their data is coming from, how it is being used and who has access to it – reducing the risks of data breaches or misuse. Data governance along with data preparation is an essential part of any AI strategy.
If businesses implement AI without proper data governance in place, they may encounter several issues. One major risk is data security. AI relies on vast amounts of data, including sensitive data and personal information. Without strict governance protocols, this data is vulnerable to internal misuse and data breaches. This is supported by a 2024 KPMG study that found that 63% of consumers were concerned regarding the potential of GenAI to compromise an individual’s privacy by exposing personal data to breaches or through other forms of unauthorised access or misuse. This can even lead to potential fines or legal action as well as significantly eroding trust within the organisation. Similar to data preparation, data governance can be a challenging task for organisations to manage alone. This is where governance tools and expert advice from a trusted technology partner can help set your organisation up for success.
In the race to adopt AI and stay competitive, data preparation and governance are often overlooked, but they are essential components of a successful AI strategy and help provide a strong foundation for any AI initiative. Clean, organised and well-governed data ensures that AI systems function at their full potential, providing accurate insights and driving business value and impact. If you would like to learn more about preparing your business for AI, watch Storm’s on-demand webinar series ‘Becoming an AI-Driven Organisation’.
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