Garbage In, Garbage Out: Verifying High-Quality Data for Your AI Models
Do you remember the sci-fi movies from the late 80s?
Self-driving cars that won't need you steering the wheels, smart assistants who can find you from recipes to business insights, and robots that can replicate human behavior.?
The fiction of yesteryears is a reality now, thanks to Artificial Intelligence.?
AI is everywhere, and it is impacting our lives tremendously. From minute daily activities to bigger functionalities in a business, the impact of AI is tremendous.
But, as they say, "With great power comes great responsibility," the case of AI isn't isolated.?
Remember when Uber's self-driving test car killed a woman crossing the street?
Or when Amazon's AI-powered hiring tool favored men over women with similar qualifications??
These incidents highlight a crucial yet often overlooked aspect of AI development: data quality. Your AI models can only produce high-quality output if fed with good-grade data.?
How do you ensure the flow of quality data in AI models?
Leveraging the power of AI in businesses can yield significant positive results. Bing, operating in a market dominated by Google, became profitable by doubling its share in the search engine market, all through the help of AI.
Here are a few ways to ensure good data fuel your AI models:
Define your data quality standards
Before you measure the quality of your data, set the criteria. Based on your need, you need to set specific criteria for different aspects of data quality, like completeness, accuracy, consistency, relevancy, etc.
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Focus on the data source
Whether your data is valuable or crooked depends greatly on the source and the collection methods. By checking for errors, biases, or gaps in the data, you can ensure its quality in the pipeline.
Refine your data before feeding
Cleaning your data before feeding it to your AI models is a healthy and vital step. Data cleaning includes removing outliers, samples with high missing data points, computation of missing data, and reviewing flagged data for optimum outputs from AI models.
Keep on Tracking
Checking the quality of your data isn't a one-time thing but a constant process. Use trackers for key metrics and identifying trends. Based on your AI needs and goals, update and refine your data quality criteria, sources, methods, and techniques.
According to Statista, the global market for AI will reach approximately 2 trillion dollars by 2030, growing twentyfold from current times.?
Food and drinks giant Nestle, for instance, uses AI fed with quality real-time weather data to optimize its supply chain, significantly reducing food waste and carbon footprint.
Similarly, global MNCs like Pfizer and BMW are also leveraging the power of AI based on good data to boost their efficiency and productivity.
While using AI in business is advantageous, remember this always:
Garbage in = Garbage out.
Low-quality data breeds biased models, inaccurate predictions, and, ultimately, business disasters. So, invest in data hygiene, verify its quality rigorously, and guide your business to the pinnacle in the competitive market.?
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