Many LinkedIn posts focus on the outcomes of AI tests that enjoyed access to structured data, or focus on the algorithmic aspects of AI.
As such, these examples of AI in action can be misleading.
One often underestimated issue with the actual deployment of AI within a company is the importance of data quality and accessibility. Data quality and accessibility is crucial because without high-quality, relevant, and accessible data, even the most advanced algorithms will struggle to deliver meaningful results.
We've been in meetings where this issue has been met with a genuine question - "Can't AI just sort my data?".
The temptation is to reply "No."
But it's better to explore 5 key learnings in answer to this question.
Garbage In, Garbage Out (GIGO): This adage holds particularly true for AI. If the data fed into the system is inaccurate, biased, or incomplete, the AI outputs will be similarly flawed.
Data Accessibility: AI algorithms need access to a wide variety of data to learn and make accurate predictions or decisions. Often, data within organisations is siloed or poorly organised, making it difficult for AI systems to access the necessary information.
Data Bias: Biases present in data can lead to biased outcomes, perpetuating and even exacerbating existing inequalities. Data that is inherently biased requires careful curation and preprocessing.
Data Security and Privacy: With the increasing emphasis on data protection and privacy regulations, there's a need to ensure data systems operate within legal and ethical boundaries.
Data Governance: Clear governance structures for data management are required to ensure that data is collected, stored, and used responsibly and ethically.
If your data is of less quality and accessibility than you'd like, don't panic.
Being has multiple methodologies that will help you attain the quality and accessibility to make you data-ready for AI deployment.
