The Importance of Data Quality in Machine Learning
Data is an invaluable resource in a competitive business environment. According to Forbes, 59% of enterprises use data analytics to make informed choices and remain competitive. The good news is that machine learning has made data collection and analytics efficient since machines do not get tired like humans.
However, machine learning requires quality data for AI tools to produce correct inferences. For this reason, one should be focused on applying quality data in machine learning. The data collection team and AI engineers should ensure the facts on the AI tools are accurate, updated, and reliable.
In this article, we state what good data means and why it is crucial in machine learning. Also, we discuss the impacts of poor-quality data in ML. Lastly, we offer ideas on how to ensure data quality. Let’s get started.
What Is Good Data?
Good data is data that can satisfy a set system, technical, and business requirements of a company. The data one is feeding to an AI tool should be updated, relevant, and accurate to serve the intended purpose. For example, good data for a cancer diagnostic machine shows mammograms of other cancer patients. The user can be assured of getting quality outcomes from the ML model if the data used on a machine meets the criteria of good data.
Benefits of High-Quality Data in Machine Learning
The data employed in machine learning determines whether an ML model can deliver the desired results and how much it can develop. The truth is that no AI tool can grow beyond its data quality. Here are the reasons why high-quality data is required in machine learning.
The data used for starting marketing campaigns or improving customer experience is sensitive. According to the California Consumer Privacy Act, anyone who breaches data protection laws is liable to a fine of $100-$50,000 per violation. AI tools using high-quality data are not prone to non-compliance because they can mask sensitive personal information.
Accurate and complete information on AI tools helps businesses to avoid recollecting data or spending time analyzing errors. The time and resources can be used for other income-generating projects for the firm. For example, a business could utilize reprinting money to offer rewards to customers to increase sales. Also, good data helps companies evade fines associated with non-compliance.
The inferences an AI application or software makes depend on data quality. If the machine has quality data, it will make valuable conclusions. The top management can use the inferences from the AI tool to make beneficial decisions for the company. For example, an accurate diagnostic tool may help a doctor prescribe the correct medication for a patient.
Impacts of Poor Data in Machine Learning
Bad data refers to facts or figures collected through erroneous or substandard collection approaches, sampling techniques, and study designs. For example, customer data with typos is poor data since it may cause a machine-learning algorithm to give inaccurate results. Using poor data in machine learning can affect the machine’s functioning and reduce productivity. Read this section to see the effects of poor data in machine learning.
Utilizing substandard data in machine learning leads to an erroneous analysis and lost income for businesses. For example, customer relationship management efforts based on flawed data may lead to client dissatisfaction. The dissatisfied customers will choose to buy from competitors, which is a loss of revenue for your business.
Moreover, flawed data management practices may lead your business to legal challenges. For example, feeding a fraud detection system with low-class data can cause it not to detect fraudsters. The authorities will fine your company for failing to report fraudulent activities. For this reason, you may lose revenues if you use poor data for your machine learning process.
According to a study by the HFS research, around 95% of C-level executives doubt their data. The bad thing about poor data is that it may force organizational leaders to make poor decisions or miss deadlines. For instance, an AI marketing tool with incorrect data may cause a business to launch an ineffective advertising campaign.
More so, employee productivity may decrease if the AI tool is using poor data. IT engineers could take days or weeks trying to figure out why an AI tool is ineffective. The time such professionals waste can be used to perform other crucial tasks at the firm.
The image customers have for your business determines its success. Poor data could be the source of reputational degradation as it may expose a company to criticism. For example, an AI machine that does not adhere to moral standards could hurt a firm’s image.
Ways to Ensure Data Quality
Collecting and maintaining quality data for machine learning requires focus and dedication. Here are some ways to ensure you have quality data:
Utilize effective data quality tools. There are tools in the market to help you capture and manage high-quality data. Look for a data quality tool to help you to migrate, cleanse, or standardize data accurately. The best option is to read customer comments about different AI tools to determine the most effective.
Adopt a data-cleaning habit. Focus on updating customer information and reading customer comments to adjust the data in your AI tool. A regular data cleaning routine prevents you from using inaccurate analytics to make decisions.
Assign a particular individual or team the task of ensuring data quality. The person should focus on checking inconsistencies in data and correcting them before they affect machine learning. Detecting the errors early saves your business the challenges associated with poor data.
Data quality is an essential ingredient in business success. Using high-quality data in machine learning increases revenue ensures compliance, and improves decision-making. On the other hand, low-quality data can lead to the collapse of a company because of reduced revenues, reputational damage, and high inefficiency.
As a business owner or leader, you must invest in quality data in machine learning for high returns. Pixta AI understands the importance of the ethical and legal use of data in the development of AI models. All of our data is fully licensed and compliant with all relevant regulations, giving you peace of mind and a competitive edge in the responsible AI space. If you do not know how to ensure good data for your AI software or application, don’t hesitate to contact us for more advice.