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AI & Data Best Practices

10 Data Preparation Issues that can sideline AI Projects

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Data preparation is one of the most important steps in any artificial intelligence (AI) project. Unfortunately, it’s also one of the most overlooked. Many organizations dive headfirst into building models without properly preparing their data, only to hit roadblocks and delays later on. In this post, we’ll cover 10 data preparation Issues that can sideline AI Projects and what you can do to avoid them.

Lack of Data Quality: One of the biggest issues with data preparation is lack of data quality. This can include missing values, duplicate data, and inaccuracies. Before building a model, it’s important to clean and validate your data to ensure that it’s accurate and reliable.

In  our AI Today podcast episode on Data Preparation for AI, we go over some key considerations for data quality for AI projects.

Inadequate Data Volume: Another common issue is not having enough data. AI models require large amounts of data to be trained effectively. If there isn’t enough data, the model may not be able to make accurate predictions.

Inconsistent Data Formats: Inconsistent data formats can also cause problems. For example, if some of the data is in one format (such as CSV) and some is in another (such as JSON), it can be difficult to work with. Make sure to standardize the format of all the data before building a model.

Missing Data: Missing data can also be a problem. This can include missing values for certain fields or missing data for certain time periods. Before building a model, make sure to fill in any missing data or remove it from the dataset.

Check out the Data Preparation checklist for more things to consider before starting your AI projects.

Biased Data: Biased data can also create problems. Biased data can include data that is skewed towards a certain group, data that was collected at a certain time of the day or a certain location, or data that is not representative of the population. Before building a model, make sure to remove any bias from the dataset however this can not always be easy to identify and spot.

Outdated Data: Data that is out of date and no longer valid can also be a problem. For example, if the data is from a few years ago, it may not be relevant anymore. Ensuring you’re working with the most up-to-date data before you prepare that data is crucial. Data preparation is not free and there is a cost involved so you want to make sure you’re prepping only good quality data.

Unlabeled Data: Data that is unlabeled can be a big issue. After all, if it’s unlabeled how do you even know what it is? Could it be sensor data? Log data? PII data? If it’s not labeled then your guess is as good as mine. Before prepping your data and building a model, make sure to label the data or remove the data if you have no idea what it is.

Irrelevant Data: Irrelevant data can include data that is not pertinent to the task at hand. For example, if you need name and address data, but you also have birthday and gender data then you’ll want to consider removing the data you don’t need. Otherwise you’ll be spending effort prepping data that you won’t actually be using. 

Incorrect Data: Data that is not correct is a huge issue. After all, if you’re using this data to train a machine learning system and you feed it garbage incorrect data, then don’t be surprised when you get garbage incorrect results. Incorrect data can include data that is inaccurate or data that has been entered incorrectly. Before prepping your data you must make sure the data is accurate.

Not Enough Data: People often ask how much data is enough data for machine learning systems. And the answer is it depends. It depends on a number of factors such as  the algorithm you’re using, the problem you’re solving, and how much access to additional third party data you have. However, not enough data can also be an issue. If you don’t have enough data that means you won’t be prepping a sufficient amount of data for your model and this may lead to accuracy issues.

Data preparation is a crucial step in any AI project. By avoiding these common data preparation issues, you can ensure that your model is built on a solid foundation of high-quality, accurate data. Remember to clean and validate your data, remove bias, and standardize formats. Also, make sure to have enough data, label the data and remove any irrelevant or outdated data. With the right data preparation, you can set your AI project up for success.Our Infographic on Data Preparation and Labeling for AI highlights some key metrics and considerations around data preparation.

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