A futuristic concept that has its roots dating back to the early 60s has been waiting for that one game-changing moment to become not just mainstream but inevitable as well. Yes, we are talking about the rise of Big Data and how this has made it possible for a highly complex concept like Artificial Intelligence (AI) to become a global phenomenon.
This very fact should give us the hint that AI is incomplete or rather impossible without data and the ways to generate, store and manage it. And like all principles are universal, this is true in the AI space as well. For an AI model to function seamlessly and deliver accurate, timely, and relevant results, it has to be trained with high-quality data.
However, this defining condition is what companies of all sizes and scales find it difficult to battle. While there is no dearth of ideas and solutions to real-world problems that could be solved by AI, most of them have existed (or are existing) on paper. When it comes to the practicality of their implementation, the availability of data and the good quality of it becomes a primary barrier.
So, if you’re new to the AI space and wondering how data quality affects AI outcomes and the performance of solutions, here’s a comprehensive write-up. But before that, let’s quickly understand why quality data is important for optimal AI performance.
Role Of Quality Data In AI Performance
- Good quality data ensures outcomes or results are accurate and that they solve a purpose or a real-world problem.
- The lack of good quality data could fetch undesirable legal and financial consequences to business owners.
- High-quality data can consistently optimize the learning process of AI models.
- For the development of predictive models, high-quality data is inevitable.
5 Ways Data Quality Can Impact Your AI Solution
Now, bad data is an umbrella term that can be used to describe datasets that are incomplete, irrelevant, or inaccurately labeled. The cropping up of any or all of these eventually spoil AI models. Data hygiene is a crucial factor in the AI training spectrum and the more you feed your AI models with bad data, the more you’re making them futile.
To give you a quick idea of the impact of bad data, understand that several large organizations couldn’t leverage AI models to their complete potential despite having possessed decades of customer and business data. The reason – most of it was bad data.
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Apart from bad data and its sub concepts, there exists another plaguing concern called bias. This is something that companies and businesses around the world are struggling to tackle and fix. In simple words, data bias is the natural inclination of datasets towards a particular belief, ideology, segment, demographics, or other abstract concepts.
Data bias is hazardous to your AI project and ultimately business in a lot of ways. AI models trained with biased data could spew results that are favorable or unfavorable to certain elements, entities, or strata of the society.
Also, data bias is mostly involuntary, stemming from innate human beliefs, ideologies, inclinations, and understanding. Due to this, data bias could seep into any phase of AI training such as data collection, algorithm development, model training, and more. Having a dedicated expert or recruiting a team of quality assurance professionals could help you mitigate data bias from your system.
There are two aspects to this:
- Having massive volumes of data
- And having very little data
Both affect the quality of your AI model. While it might appear that having massive volumes of data is a good thing, it turns out that it isn’t. When you generate bulk volumes of data, most of it ends up being insignificant, irrelevant, or incomplete – bad data. On the other hand, having very little data makes the AI training process ineffective as unsupervised learning models cannot function properly with very few datasets.
Statistics reveal that though 75% of the businesses around the world aim at developing and deploying AI models for their business, only 15% of them manage to do so because of the lack of availability of the right type and volume of data. So, the most ideal way to ensure the optimum volume of data for your AI projects is to outsource the sourcing process.
Data Present In Silos
So, if I have an adequate volume of data, is my problem solved?
Well, the answer is, it depends and that’s why this is the perfect time to bring to light what is called data silos. Data present in isolated places or authorities are as bad as no data. Meaning, your AI training data has to be easily accessible by all your stakeholders. The lack of interoperability or access to datasets results in poor quality of results or worse, inadequate volume to kickstart the training process.
Data Annotation Concerns
Data annotation is that phase in AI model development that dictates machines and their powering algorithms to make sense of what is fed to them. A machine is a box regardless of whether it is on or off. To instill a functionality similar to the brain, algorithms are developed and deployed. But for these algorithms to function properly, neurons in the form of meta-information through data annotation, need to be triggered and transmitted to the algorithms. That is exactly when machines begin to understand what they have to see, access and process and what they have to do in the first place.
Poorly annotated datasets can make machines deviate from what is true and push them to deliver skewed results. Wrong data labeling models also make all the previous processes such as data collection, cleaning, and compiling irrelevant by forcing machines to process datasets wrongly. So, optimum care has to be taken to ensure data is annotated by experts or SMEs, who know what they are doing.
We cannot reiterate the importance of good quality data for the smooth functioning of your AI model. So, if you’re developing an AI-powered solution, take the required time out to work on eliminating these instances from your operations. Work with data vendors, experts and do whatever it takes to ensure your AI models only get trained by high-quality data.