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How to Improve Data Quality for Use in AI/ML

The advent of Artificial Intelligence (AI) and Machine Learning (ML) involved has given us enormous opportunity. With the use of AI, you can make quick, informed decisions on marketing, advertising, company strategy, and more. If your AI is properly trained, then your business will be ahead of the game in making sophisticated generalizations about your industry, market conditions, and target audiences.


If, of course, your AI is properly trained.


This is the crux of the matter: AI must be trained on quality data. If it isn’t, then you will be receiving flawed evaluations, wrong generalizations, inaccurate trends and tendencies, and your marketing and advertising campaigns will suffer. The secret, therefore, in having powerful AI assisting rather than hampering your business, is to get top quality data to train it on. Here are surefire ways to improve data quality to properly train AI:


Enhance your data collection protocols

How you categorize, label, and sort your data is of major importance. To properly train AI on data, the same quality must be maintained across the board. Data points must follow the same parameters and standards.


You should also take care to avoid duplicates in your dataset, especially if having duplicates creates a risk of altering the overall results in algorithms making calculations on the dataset.


The accuracy of the data collected must be as high as possible. The adage of “garbage in, garbage out” when it comes to processing data holds for AI/ML as well: the support you will get from AI is going to be as good as the data you train it on.


Use data governance

The best way to boost your data quality is to adopt a high quality data governance framework. Data governance is different than data management in that data governance applies to your business’ strategy in using and handling your data. Data management is the process of ensuring your data is properly controlled and organized. Both occur after data collection, but data management is what will help you make the most of AI/ML.


Through data governance, you will be able to get the right data when you need it, properly assess the data quality, accuracy, and security, and be able to act on the data when it’s necessary to do so.


Invest in data quality

Once you have collected your data, you should clean it. Data cleaning is necessary the bigger data and huge datasets are used for key components of business strategy and decision making. To do the data cleaning that your business requires, you must implement the correct data cleaning tools. This depends on the size of your business, the nature of your business, and how you will be using your data. There are different data cleaning tools for different needs.


Work with data providers

The use of big data and third-party data is a staple of procuring powerful datasets for your business. But that can also mean that some of the datasets will include data that you can’t control for quality as efficiently as with your own data collection protocols. That means that there is a risk that lower quality data can be included in your data pool.


The most efficient way to minimize this risk is to work closely with data providers. Share protocols of data collection and keep working with them to continually enhance data collection practices across the board. The more you are collecting data in the same ways, labelling them the same way, the higher the quality of the data that will go to train your AI/ML.


An AI/ML that is trained on top quality data is the most powerful weapon for key insights in your industry, from marketing to advertising to risk assessments and more.


Keep your finger on the pulse

Data quality needs to be constantly monitored. The more you oversee it and keep enhancing your protocols, the better results you will have. Once you have properly trained AI/ML, the AI will help you boost the quality of your data, including data collection. But to get there, you must first start with the best data you can procure to get your AI ally. If you keep scrutinizing your metrics and keep looking out for potential issues in data collection that are bound to occur, then you will catch problems before they infect the AI.

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