Worldwide, corporations and governments place a high focus on AI. A crucial feature of AI that has been disregarded is poor data quality.
To deliver the best outcomes, AI algorithms are built on solid data. However, the results might be disastrous if the data is lacking, inaccurate, or insufficient.
Poor data quality might negatively affect AI systems that diagnose patients’ disorders. These systems tend to make incorrect diagnoses and forecasts, which might result in delayed or incorrect treatment. The University of Cambridge examined more than 400 methods for diagnosing Covid-19, and it was discovered that the erroneous data in AI-generated reports rendered them utterly useless.
This implies that your AI initiatives will have negative real-world effects if your data is insufficient.
What Exactly Is “Good Enough Data”?
Data that is deemed “good enough” is a contentious concept. Some contend that there is insufficient data. Some claim that it is not required to have high-quality data. Poor data, according to HBR, can lead to analytical paralysis. Tools for machine learning are worthless if the data is inaccurate.
WinPure defines good enough data as true, comprehensive, and accurate information that can be used with assurance in business operations without posing a manageable risk.
Many businesses face more issues than they realise with data governance and quality. They are under intense pressure to deploy AI ideas to maintain competitiveness, heightening the stress. Therefore, issues like contaminated data are not discussed in boardrooms until they result in a project’s failure.
Poor Data: What Does it Mean for AI Systems?
Data quality problems may occur when the algorithm utilises training data to identify patterns. Unfiltered social media data might cause an AI machine, like Microsoft’s AIbot, to make abusive, racial, and misogynistic statements. Recently, incomplete data was blamed for AI’s failure to distinguish humans with dark skin.
What relevance does this have to data quality?
Bad data governance, a lack of understanding of quality, and solitary perspectives of the data (where there may have been a gender gap) can all lead to bad outcomes.
What Should I Do?
Realising that their data quality is subpar, businesses scramble to find solutions. Hiring engineers, analysts, and consultants unthinkingly is the usual practice to solve data quality issues. The issue isn’t going away even if the corporation spends millions on the appropriate hires. Jumping to conclusions to address a data quality issue is not useful.
True change starts at the grass-roots level. These are the three most crucial actions you must take if you want your AI/ML project to proceed successfully.
Identifying and Increasing Awareness of Data Quality Problems
You must first assess the accuracy and reliability of your data. A well-known expert in the field, Bill Schmarzo, advises using design thinking to establish a company culture that is easy for everyone to comprehend and supports its data goals.
In today’s corporate world, data management and quality are no longer just the domain of IT teams or IT departments. Business users need to be aware of concerns like data quality and corruption.
Making data quality training an organisational priority and giving teams the authority to recognise problematic data qualities should be your first order of business.
You may use this checklist to discuss the calibre of your data.
Create a plan to meet the quality metrics.
Many companies commit the fallacy of downplaying data quality problems. They employ data analysts to clean up the data rather than focusing on strategy and planning. Without a strategy, many firms utilise data management tools to clean, de-dupe, and consolidate data. Problems cannot be solved alone with one’s skills and tools. To guarantee the quality of the data, a plan would be beneficial.
The approach has to consider data collecting, labelling, processing, and compatibility with the AI/ML project. The data used to train AI programmes was inadequate, biased, and erroneous if they only chose men for technical roles. The actual goal of the AI project did not apply to this data.
Data quality encompasses more than simply basic correction and cleanup procedures. Before you begin a project, setting governance rules and data integrity is crucial. This stops your project from subsequently becoming bankrupt.
Setting Expectations and Posing the Correct Questions
‘Good enough data’ or data quality are not subject to any uniform criteria. Everything depends on your company’s information management system, the policies for data governance (or the lack thereof), and your team’s knowledge and objectives, among other things. Here are some queries you may put to your team before the project starts:
What is the information’s source?
Do those in charge understand the significance of data quality?
What problems exist with data collecting that can influence favourable results?
What information does the data offer? Are the data following the requirements for data quality?
What are the duties and obligations? Who is in charge of data cleaning? Who is in charge of making master records?
Are the data suitable for their intended use?
Make the appropriate inquiries and role assignments. Assist your team in solving issues before they get worse!Conclusion
Fixing typos and mistakes does not constitute good data quality. It ensures that AI systems are accurate, impartial, and devoid of bias. Before starting an AI project, finding and addressing any data quality problems is crucial. Make a programme for data literacy available to the entire organisation to help teams stay focused on the big picture.