The 5 Best Data Cleaning Techniques That Instantly Improve Output

Data makes the world go round, but just because you have a lot of information doesn’t mean it will produce real value. While tools like artificial intelligence (AI) can do a staggering amount with data, these records must be reliable for that work to be any good. That’s where data cleaning techniques come in.

Why Are Data Cleaning Techniques Important?

Data cleaning is the process of preparing records before analyzing them through AI. It’s a crucial practice because changes in as little as 1% of a model’s training dataset can cause it to not work correctly.

There’s a saying in the AI industry — “garbage in, garbage out.” Low-quality data will produce unreliable, inaccurate results, even with an otherwise sound machine learning model. While using good sources for your information helps avoid those outcomes, even minor issues can cause big problems, so it’s important to ensure everything’s in order before feeding datasets to AI.

Data cleaning doesn’t change what your information is, but it makes minor adjustments to ensure an AI model can understand it correctly. By making something easier to interpret, you can get more out of your AI analyses.

The Best Data Cleaning Techniques You Should Use Today

Data cleaning techniques come in many forms, each serving a unique purpose. Here are five of the most useful you should employ.

1. Removing Irrelevant Data

One of the most important data cleaning steps is to remove data points or values you don’t need. In theory, more information is good because it gives AI additional reference points to make informed decisions, but too much of the wrong kind of data can be distracting.

Studies show that irrelevant data can introduce bias and noise into AI models — both things you want to avoid. While deleting what you don’t need isn’t a complete fix on its own, it does make hallucination and bias less likely, as what the algorithm learns from is more focused.

Anything that doesn’t help your AI model do what it needs to do shouldn’t be in your dataset. That includes not just irrelevant information but also unneeded values like HTML tags or hyperlinks.

2. Deduplication

Deduplication is another critical step in data cleaning. As you can probably tell from the name, this involves removing duplicate copies of the same data points.

Duplicates can pop up in your datasets for many reasons. Simple human error when entering records is a common one. Recording the same information from two different sources as unique values is another. Whatever causes them, though, these repeats have a big impact on AI reliability.

At best, duplicates make your datasets larger than necessary, which leads to slow processing times and can create noise. At worst, they can skew your analysis’s accuracy by making one value seem more common than it really is. That’s especially impactful when analyzing demographics or using AI to inform big decisions, such as those in security or business intelligence.

3. Filling Missing Values

While some records may have multiple copies or too many unnecessary details, others have the opposite problem — they’re incomplete. Missing values are present in most real-world datasets because real-world information often doesn’t fit into neat boxes, especially when coming from multiple sources. 

Given how common this issue is, filling in absent information is an essential data cleaning technique. Start by determining what values you actually need. It’s better to delete entries that not every record has instead of filling them in if those data points don’t add any value to your AI application.

Some details, however, are essential. You can sometimes find or infer the missing entries if you have access to the original data source. In other cases, you may need to use regression techniques to estimate those values. Still, this may introduce errors, so you might want to adjust your algorithm to weigh such factors less.

4. Typo Correction

A simple typo can hinder your AI’s accuracy, too. Remember, AI lacks logic. While a human could see a word or number that doesn’t make sense and infer what it’s supposed to be, AI will take it at face value.

Running a spell check is one easy way to look for typos. You should also ensure everything uses consistent punctuation and capitalization, as subtle differences in these could cause an AI model to think similar records refer to separate things.

While reactive measures like this are important, it’s best to prevent typos from occurring in the first place. You can do so by taking greater care in the data entry phase, paying close attention to spelling, syntax and punctuation.

5. Reformatting

Reformatting is a similar but distinct data cleaning step. Even if all your records are correct, complete, relevant and error-free, AI can still struggle to interpret them if they lack consistent formatting.

Keeping everything in the same file type is a good first step. More complex models may need to analyze multiple kinds of data, but you can still use a consistent file type for each category. This consistency may seem small, but it will make the dataset easier for the AI to weigh each factor appropriately.

Similarly, make sure your language is consistent. Synonyms or different turns of phrase may all mean the same thing, but machines can interpret these differences as larger distinctions between information that would otherwise say the same thing. 

Data Cleaning Best Practices

Now you know how to clean data, at least in general, but you can go further. A few data cleaning best practices will make the process much easier and more effective.

As you may be able to tell, going through all these steps is time-consuming and highly detail-oriented. Consequently, it’s not a great fit for manual work. It’s often best to automate your data cleaning, and you can find plenty of off-the-shelf tools that do just that.

Similarly, cleaning works best when you pair it with enrichment. Enriching your data means adding values to create a robust learning environment for your AI model. You can do that by providing supplementary records for context or through synthetic data. 

Synthetic data is AI-generated text, which gives you a wider pool of usable records without the complications and cost of real-world data gathering. Even though it doesn’t necessarily reflect actual trends or people, it performs just as well as real data in studies using it to train predictive models.

Data Cleaning Techniques Make AI More Reliable

Once you know how to clean data, you can get more out of it. Following these steps and best practices will give your AI applications the best training or operating environment possible. All the lofty promises of machine learning become more realistic as a result.