The ‘black box’ nature of many AI models is a topic of significant concern for many businesses and researchers. Complex AI algorithms use deep learning models like neural networks, which can turn out to be incredibly intricate, so much so that even their creators can’t be certain how the AI arrived at specific conclusions or predictions.
While AI technology is advancing rapidly, the ability for humans to fully comprehend complex AI models and the rationale behind every prediction is an evolving area of study and development in the field of Artificial Intelligence.
But why does this happen?
In the most crudest sense, Artificial Intelligence tries to simulate human intelligence in machines that are programmed to think and learn like humans. They rely on algorithms to process huge amounts of data, recognize patterns, and make decisions or predictions based on that data.
AI will learn from the data, but the mysterious ways in which it does that is beyond human comprehension so far.
But before you succumb to doomsday scenarios, there is still one area where you retain control. Data is the foundation of AI. Without sufficient and relevant data, AI systems cannot learn or make accurate predictions.
The quality, quantity, and diversity of the data directly influence the performance of AI models. Moreover, the continuous feedback loop, where AI systems learn from new data, is crucial for their evolution and improvement.
Data is not only the fuel that drives your AI models, but also one area where you have a modicum of control over the results. What you need is vast amounts of high-quality data. Where can you possibly get it from, you ask? The internet, of course!
The Rise of Big Data: A Game Changer
Unlike us humans, AI doesn’t tire. Neither does it need free pizza to go the extra mile. It can run swiftly through millions of data points, learning a whole lot in a relatively short amount of time.
Even though the concept of Artificial Intelligence has been around for quite some time now, without the processing power to comb through massive data points and the subsequent infrastructure to enable the operation, the technology was good as wasted.
Big Data, which refers to the massive volume of structured and unstructured data that inundates people and businesses, gained popularity in the mid-2000s. As information became democratized, people all over the world have only continued to produce more and more data every day. Statista predicts that by the year 2025, global data creation will grow to more than 180 zettabytes.
Big Data is marked by four key factors: Volume, Variety, Velocity, and Veracity.
Volume refers to the exponential growth in data creation, while velocity pertains to the astonishing speed at which this data is generated. The vast increase in web data every day provides a fertile ground for exploring volume and variety using Big Data technology.
And herein lies the advantage you are looking for: large volumes of data to train your machine learning models to acquire and perfect a skill. When analyzed the right way, web data can provide you with the insights you are after, the genesis of the reason you started with AI in the first place.
But there’s a caveat. Data in the wild is hard to tame. Despite the abundance of data, ensuring its accuracy and establishing its quality has become paramount. Analyzing data without ensuring its accuracy can result in blind spots, leaving you ill-equipped to tackle uncertainties.
Data Quality and AI
At Grepsr, where I serve as a Content Specialist, we employ a straightforward framework to highlight the detrimental impact of poor data on businesses: the 1:10:100 rule in data quality, conceptualized by George Labovitz and Yu Sang in 1992. This rule provides valuable insight into the costs associated with bad data.
When you address bad data proactively, the cost is minimal—just $1 for prevention. However, if flawed data goes unnoticed and requires correction later, the cost escalates significantly to $10. In the worst-case scenario, if bad data is left unattended, the cost skyrockets to $100. Given the unstructured nature of web data, a crucial initial step is transforming this data into a structured format, whether it’s JSON, CSV, or a simple spreadsheet.
Before delving into the methods for achieving data quality, it’s essential to understand what constitutes data quality.
Accuracy
The precision of data indicates how faithfully it represents real-world conditions, ensuring it isn’t misleading. Relying on insights derived from inaccurate data almost always leads to ineffective outcomes.
Inaccurate data poses substantial risks to enterprises, carrying severe consequences. Components of inaccurate data sets include outdated information, typographical errors, and redundancies.
For instance, imagine a retail company using outdated sales data to plan their inventory for the upcoming holiday season. If the data does not accurately reflect current market demands, the company might overstock certain products and understock others, leading to financial loss and disappointed customers.
Completeness
A dataset is deemed complete when it precisely aligns with an organization’s requirements, lacking any empty or incomplete fields. Complete data fields provide a comprehensive view necessary for accurate analyses and informed decision-making.
Insufficient or incomplete data can lead to flawed insights, negatively affecting businesses and squandering resources. For instance, in survey data analysis, if respondents omit their age, marketers cannot accurately target the desired demographic, leading to ineffective marketing efforts and suboptimal outcomes.
Validity
A dataset’s validity pertains to the collection process rather than the content of the data. It is deemed valid when the data points are in the appropriate format, possess the correct data type, and fall within specified value ranges.
Datasets not meeting these validation criteria pose challenges in organization and analysis, necessitating additional efforts to integrate them seamlessly into the database. When a dataset is invalid and requires manual intervention, the extraction process and the data source are typically the main cause, rather than the data itself.
Consistency
When managing multiple datasets or various versions of the same dataset over different time periods, it’s crucial for corresponding data points to be uniform in terms of data type, format, and content. Inconsistencies in data can lead to disparate answers for identical queries, creating confusion within teams.
An illustration of this challenge is the diverse formats of postal addresses worldwide, making it arduous to standardize this information. Similarly, implementing corporate-level cost-reduction initiatives becomes problematic when faced with inconsistent data. In such cases, manual inspection and correction of data become necessary, adding complexity and potential errors to the process.
Timeliness
In high-quality datasets, timely data collection right after an event is essential. As time passes, datasets become less accurate, reliable, and relevant, transitioning from reflecting the present reality to representing past occurrences. Therefore, the freshness and relevance of data are pivotal for effective decision-making and analysis.
Staying abreast of the latest trends and opportunities is crucial for businesses aiming to make informed decisions and capitalize on emerging market dynamics. Fresh and relevant data ensures that strategic decisions are aligned with current market conditions, enabling organizations to proactively respond to changing trends and gain a competitive edge.
‘Quality – you know what it is, yet you don’t know what it is. But that’s self-contradictory. But some things are better than others, that is, they have more quality. But when you try to say what that quality is, apart from the things that have it, it all goes poof!”
- Robert Pirsig, Zen and the Art of Motorcycle Maintenance
Zen and the Art of Motorcycle Maintenance has to be one of the most influential books I have ever read. Rober M. Pirsig explores the concept of quality and its subjective nature.
He delves into the idea that quality is not an objective and fixed measure but is rather dependent on individual perception and context.
I can’t help but draw parallels between quality in general, which Pirsig elaborates on, and quality in data.
Data quality, more often than not, is subjective and context-dependent. What constitutes high-quality data can vary based on the specific requirements of a given analysis, the goals of the given organization, and the context in which the data is being used.
That’s why, at Grepsr, we measure data quality on a case-by-case basis. Although we have developed the QA black box testing framework, the human touch is never completely weaned off.
A major challenge in the development of AI is to ensure that it abides by human ethical standards, and the best way of going about it is to probably have a human-in-the-loop. Afterall, there’s only so much that can be automated.
In essence, just as Pirsig challenges our understanding of quality, one must recognize the intricate, multifaceted nature of data quality, ensuring a thoughtful and nuanced approach in the pursuit of accurate, meaningful insights.
To conclude
Data, the lifeblood of AI models, holds incredible power, yet its value is intricately tied to its quality. We have explored the multifaceted dimensions of data quality: accuracy, completeness, validity, consistency, and timeliness, each playing a pivotal role in shaping meaningful insights.
The journey through the intricacies of data quality mirrors Pirsig’s exploration of quality in the broader sense — it’s not a fixed measure but a perception deeply rooted in individual understanding and the specific context of its use.
At Grepsr, we recognize this complexity and approach data quality with a case-by-case lens, combining advanced frameworks with the indispensable human touch. As the digital landscape advances, the challenge persists: ensuring AI aligns with human ethical standards. The integration of human judgment, the ‘human-in-the-loop,’ becomes paramount.
Automation has its limits, and the nuanced, thoughtful approach to data quality echoes the essence of Pirsig’s philosophical inquiry.
We must acknowledge the intricate and multifaceted nature of data quality. It calls for a nuanced, adaptable, and human-centered approach, ensuring that the insights derived are not just accurate but deeply meaningful, empowering businesses and researchers to navigate the complexities of our data-driven world with confidence and clarity.
Ruchir Dahal, Content Specialist at Grepsr.
