The use of machine learning and AI is the norm among businesses and enterprises everywhere. The recent improvements in computing power and available technologies have led to the creation of thousands of AI models designed to improve efficiency and performance.
However, most AI models never reach the market due to poor data quality. High-quality data is essential for every ML and AI platform, which is why you have to change your approach to data management. Let's take a closer look at the correlation between data and AI.
Machine Learning and Big Data
The link between data quality and AI is becoming more and more evident over time. All AI models use ML to identify patterns and propose solutions to the problems at hand. However, if the data input is too small or of poor quality, the model won't be able to provide any real business benefits.
As data inputs keep improving, ML models can learn faster and provide more accurate results. Of course, any ML model can work with small amounts of data, but it won't be able to find the right solutions. Data quality makes the most difference when it comes to AI models. Every powerful AI can scale up using multiple data pipelines without any data extraction.
That leads to improved ML data analytics, which leads to generating insights and efficient solutions. Once you optimize automated data entry, boost data quality at the source, fill in the gaps, and improve reporting, your AI solution will be able to generate more accurate insights. The process is long and complex, but it's a must if you want to ensure the success of your AI solution.
The Negative Effect of Bad Data on Good AI Models
No matter how good the ML model is, it won't provide expected results without high-quality information. Data quality and AI correlate on multiple levels. First, historical data is needed to develop a predictive model. Second, the way the model uses new data to make predictions.
The data used to train the ML model must fit the final purpose. The data sets have to be complete, accurate, and without double records. Using poor quality data will only provide inaccurate results, even if the new data input has the needed quality.
Historical data is the backbone of every successful ML model. However, sometimes poor data quality can make the entire learning process much more complicated. Here are some of the main challenges of managing data.
1. Data Quality Across Multiple Departments
One of the primary challenges of data quality management can be explained by data segmentation across multiple departments. Different departments look at data from different angles. For example, data engineers require high-quality data from individual records. On the other hand, consumers prefer data sets to make the right business decisions. They want to know more about the state of the business, its attributes, and overall goals to make accurate predictions.
2. Measuring Data Quality
Data quality depends on multiple attributes. Some of them are relevant to the content, so they don't impact data quality that much. Others are far more relative to the specific use cases you want to cover.
When it comes to data management, you have to limit your input to the most important dimensions that help you apply what you find in practice. Once you assign appropriate weights and generate the combined score, you'll get the final result about the data quality.
3. The Approach Matters
If you don't put in the effort to improve data quality, you can't expect to get any long-term benefits. The approach to data quality makes the most difference in the results. That's why you should align all available data pipelines across the enterprise to boost your data quality even further.
As you can see, data quality is not the only detail that makes a difference. Ensuring that you create a system that makes it easy to find specific datasets will provide better results in the long run. Pulling data from different levels of the supply chain can help you get the right data to the right consumer.
The Negative Effects of Bad Models on Good Data
It should be clear by this point that ensuring high data quality for AI is a must if you want to get good results. With that being said, if the ML model isn't on the same high-quality level, the results won't be as good as you might think. Poor models deliver inaccurate results, even if the data input is top-quality.
Most bad ML models derive from incomplete, inaccurate, and irrelevant historical data used during the learning process. Data scientists have to monitor training data constantly to ensure that the model learns the right details and works correctly in the future.
Predictive Data Quality
Predictive data quality uses ML to find explainable and adaptive rules allowing the system to keep learning from available data. Once the model gains access to data, it improves data quality rules and becomes more capable over time. After some time, the model can predict issues before they happen. Data scientists then monitor the data to identify patterns and changes to improve the accuracy of the ML model.
Ensuring that the available data has the right quality is an ever-going process since it can deteriorate over time. As the quantity of data keeps growing, many ML models struggle to keep up with the increased load from multiple sources. If a model can't cope with all available data, the results will undoubtedly lose quality.
That's why you have to invest time and effort into keeping the data quality on the highest possible level. That means that you need a team made up of data engineers and scientists, use business analytics, and hiring managers to keep everything moving in the right direction. Here are some of the best practices that impact predictive data quality:
- Use adaptive rules to simplify incoming data, reduce bottlenecks, prevent repetition, and improve the accuracy of the rules that impact overall data quality.
- Run a detailed data quality assessment to define single scoring.
- Constant monitoring to detect anomalies and improve quality.
- Metadata management for improved data quality processes.
- Constant collaboration between departments for better productivity and faster cycle times.
Trusted Results Rest on Good Data, Powerful Models, and Exceptional Collaboration
Now that you know how poor data and bad ML models negatively affect AI solutions, it's time to talk about what works. In a situation where data scientists get high-quality data within the ideal collaborative platform, the results can be truly incredible.
The key here is to ensure that all individual elements work in perfect synergy to cope with the data analytics needs of every data pipeline. In other words, without the right data management platform, it's almost impossible to track data quality and generate trusted results.
The platform has to be able to provide full data storage across all layers. The data also has to be divided by structure. With the use of advanced collaboration tools, everyone involved can monitor individual pipelines accurately.
Moreover, they can set up data quality rules to make accurate predictions and adapt to new data coming to the same place. The ideal platform also needs auto-scaling infrastructure to allow fast data management and improve performance.
Once you establish a stream of continuous high-quality data within a platform where all members collaborate, you can ensure that the results are as accurate as possible.
Conclusion
Data quality is what drives ML and AI systems to cope with demanding business needs among organizations everywhere. Finding the right solution that tracks and analyzes increasing data generated by multiple channels is not an easy task. However, without AI and ML most organizations couldn't follow trends and improve their operations on all levels.
There are available technologies that can improve the quality of the data and help you generate better results. However, data quality, the ML model, and the entire data management teams have to work together to create a powerful AI solution. Data quality and AI go hand in hand. You have to put in some effort and careful planning to create a powerful AI solution that can benefit the entire organization on all levels.
