With AI being so popular, it is crucial for companies to have a plan in place to work with data. After all, your AI initiatives will not be successful if the data is bad. Good data is a key pillar of great AI projects, which is why data quality is now being emphasized so much. How do you quantify good or bad data? That is a massive question you have to find the answer to.
Poor data quality is now a major problem for organizations. On top of that, you also have the fact that most organizations have to contend with the massive variety of people that touch the data they work with. Because of this fact, you have many users showing why poor data can mess with how they work. The problem with data is that it is so variable. Not only do you have different types of data to work with, but you also have different data sources that can make or break your project. Making the wrong choice often leads to project failure. The data quality you have to work with is often crucial to building a good model.
Choosing the Right Data to Capture
One of the leading causes of poor data quality is not making the right decisions at the start. What data do you capture? The decisions you make here have drastic outcomes for your machine learning and artificial intelligence models. How do you update this data? This has an effect on your model drift and consistency.
All of these problems only get worse as the data get worse. Is your data set in a database or inan online storage platform like S3?All of these things make a difference, and you also have to think about the legal implications of choosing the right data set. When you put all these things together, it makes a massive difference in how good your data will be. Highquality data isn’t easy to get, but it decides whether your project will be successful or not. How successful will your AI projects be without the right sorts of data?
Data Quality and Making Sense of Things
The biggest thing that organizations can do is to focus on whatever AI projects they have in terms of the data needed. Poor data quality causes products to fail, meaning that you have to invest a lot of time and effort to get that data ready. Once you have accomplished that, you can start focusing on choosing the correct AI algorithms. One thing you can do to streamline this process is to start by asking the right questions. These questions will determine whether the data you have a sufficient or not. However, if you don’t get things done correctly, you might not be in the right position.
Choosing the Right Tools to Work with Your Data
Another area where working with data is crucial is choosing the right tools to use. Not every project will need to start over fresh every time. Some AI projects can leverage existing data to great effect. However, poor data quality will almost always cause this to be a major problem. By leveraging our project tool like xpresso.ai, you can understand what is needed to create high quality data can build projects that work.