You spend most of your workweek preparing and cleaning datasets. What if you could cut that time in half? With AI, you could streamline your workflows while improving accuracy and quality. How can you incorporate this technology into your process?
The benefits of using AI to clean datasets
If you’ve ever cleaned a dataset, you know how tedious it is. You spend 80% of your time cleaning and exploratory analysis, leaving little time for visualization, presentation, reporting or insight extraction. The longer you spend in this phase, the less time you have to generate value or uncover trends.
With a large language model (LLM), you don’t have to explicitly outline every possible edge case to ensure it functions without making mistakes. Moreover, you don’t need to retrain it for each new dataset. Since machine learning algorithms adapt as they process new information, they can dynamically adjust to unexpected irregularities within pre-defined parameters.
A machine learning model does all of this passively and with minimal intervention. Professionals often find autonomy to be one of its most beneficial features. Since 73% of data scientists are on teams with 10 or fewer people, automation is vital to lightening workloads and compensating for last-minute schedule changes.
Cleaning data with a machine learning model
Once you set parameters for your model and give it instructions, it completes tasks automatically. It has enough knowledge to make reasonable guesses or flag you when it encounters something it is unsure about. You can deploy it in several areas.
1. Remove duplicates
Since duplicate data is relatively common — having disparate storage systems or multiple similar data streams increases your chances of inadvertently cloning fields — utilizing AI this way is a good starting point. It can use optical character recognition, natural language processing or image recognition to flag copies for review and removal.
2. Fix formatting issues
Even if you have years of experience building, preparing and cleaning datasets you likely still experience formatting issues. Something as simple as entering a phone number with hyphens the first time and without another can skew your insights. Unlike humans, machine learning models don’t overlook these anomalies. They can quickly identify and standardize fields.
3. Update outdated fields
Some of your information may become outdated, rendering your insights inaccurate. Manually reviewing it to uncover dated values is a painstaking process. Luckily, AI can swiftly parse through datasets, using metadata, user-defined parameters and context clues to flag anything out of date.
4. Identify miscellaneous errors
Minor mistakes are inevitable whether you aggregate information automatically or process it manually. Although mistyping, misspelling, miscalculating and mismeasuring are common, recognizing them is challenging since they don’t fit into easily identifiable categories. Since AI can process massive datasets in moments, it can rapidly pinpoint such errors.
3 ways to improve data cleaning with AI
You may experience many of the same challenges as other data professionals when attempting to clean or scrub your information. Fortunately, AI automation can improve your process. It can resolve three of the most major cleaning-related pain points.
1. Improve data sourcing
AI can indirectly improve cleaning by continuously reviewing sources for relevancy, timeliness and accuracy, resulting in fewer errors downstream. Moreover, considering 52% of business leaders report their teams spend too much time manually collecting data, strategically leveraging automation here would free up most professionals’ workdays.
2. Enrich dataset values
Professionals can use a machine learning model to enrich data. It can fill in missing values by inferring the appropriate input from other fields or using context to generate relevant synthetic values. For example, it could determine a user’s zip code by searching for their city’s location. This approach results in higher accuracy and quality.
3. Process unstructured data
Manually transforming and standardizing unstructured and semi-structured information is tedious and difficult to manage. With AI, teams can accelerate this process to extract more valuable insights. Since approximately 80% of companies’ data is unstructured, this system could provide a more comprehensive overview of their information assets.
How to improve the model’s performance
Model selection has a massive impact on performance. Whether you select an LLM or a standard machine learning algorithm, remember to use one with an instruct suffix. This identifier signals that the AI is purpose-built and fine-tuned to follow instructions directly and output in a specific format rather than give conversational responses.
Since the training dataset has the most significant impact on model performance out of all other factors, you must ensure you properly clean and transform it. Taking time to get it right here improves your model’s operation and knowledge level, benefiting you later. Remember to review its output periodically to ensure it functions as intended.
Remember to keep an eye on your datasets
Even though AI is a powerful technology, it can still make mistakes. You should review its performance and make decisions yourself instead of letting it change data without oversight. At the very least, you should keep a human in the loop to monitor its output. This extra supervision can help you identify and resolve new pain points much faster.
