AI For Data Cleaning: How AI can Clean Your Data and Save Your Man Hours and Money

Dirty data is the bane of the analytics industry. Almost every organization that deals with data have had to deal with some degree of unreliability in its numbers.

Studies indicate that enterprises spend as little as 20% of their time analyzing data. The rest of that time is spent cleaning the dataset.

Unfortunately, poor data leads to poor insights. Assessments based on faulty data are inconsistent and often lead to failure to meet goals, increased operational cost, and customer dissatisfaction.

AI data cleaning is essential to clean your data and save you man-hours and money. Want to know more about Express Analytics AI data cleaning, speak to our experts to get a lowdown on how data cleansing can help you.

What is Data Cleaning?

Data cleaning is the final stage of data entry. This stage involves cleaning data according to specific rules. The source of the data entry error is different for each data cleaning job. Data correction is used to correct errors in data entry. These errors can be due to:

1. bad data entry
2. source of data
3. mismatch of source and destination
4. sample rate mismatch
5. invalid calculation

Cleaning data refers to the way of deleting wrong, corrupted, wrongly formatted, duplicate information, or incomplete information from a dataset.

The possibility of duplicating or mislabeling data increases when two or more data sources are combined.

Data errors can make outcomes and algorithms unreliable, even if they appear to be correct.

Cleaning up bad data will help you eliminate poor-quality results from your study, so it’s vital that this step be completed before moving on to modeling and analysis.

The best way to clean up bad data is to take the time to examine each row of data for typos, missing values, spelling errors, etc.

In this way, you can eliminate data rows that are clearly not good enough for analysis.

Eliminating these types of data will also eliminate the possibility of generating spurious results.

The term “bad data” is vague, but you can look for a few key red flags:

1. Duplicate data: Bad data tend to have multiple copies of the same event recorded in the dataset
2. Missing data: Bad Data entries might have values missing from important fields
3. Invalid data: the data entered might be old or incorrect
4. Inconsistent formatting: This means all kinds of formatting problems, including spelling errors, old code conventions, etc.

Most bad data comes from human error. Ensuring data quality is time-consuming, and negligence will lead to bad data.

So ironically, you need to analyze your data before you can do data analysis. This is to understand the type of irregularities and errors that have crept in, and which are serious enough to need to be removed. This is why best practices need to be used at every point in the chain.

How to Clean Incoming Data?

How to clean data (datasets) for machine learning? The first step in cleaning up bad data is examining it and identifying where there are problems with your analysis and model building.

You can start this process by selecting all rows with particular values in the target field.

Once you have these values, it is important to select them individually and examine each row of data.

Review each row and decide if any of the values should be excluded from your analysis.

Duplicate values: Sometimes data will contain duplicate values, but it is usually possible to select only one of the duplicates (e.g., the data might state that the age of a student was 18 or 19 years old, and only one value was recorded).

If multiple records appear to have identical records, then those records may be removed from your dataset as well.

It is important to review all of the information available in your dataset before deciding whether to remove particular rows.

While reviewing the data, you should take into account the size of your data file and the amount of computation required to build a good model.

Try to avoid using more than two factors for modeling unless there is a compelling reason for doing so.

Instead, simplify your data by dropping factors that have a negligible impact on the data analysis.

Finding outliers: This is another important task when inputting data.

Although your dataset may be relatively clean, it may still contain values that are significantly different from the average value.

These differences indicate an anomaly in the dataset and can help us spot anomalies or unusual patterns in other datasets as well.

Validate data:

It is important to make sure that the values you input into your dataset are indeed correct.

Make sure that your new graph doesn’t look skewed or that its graph points match a fitted curve very well.

AI and its Role in Data Cleaning

The first step in the data analytics process is to identify bad data.

The second involves taking corrective action. An example of this corrective action is replacing bad data with good data from another sample of the dataset.

Before the advent of artificial intelligence (AI) and its subset of machine learning (ML), data analytics companies had to use traditional data cleansing solutions to do the job.

These methods don’t work at scale or when working with ’empty-calorie data’. The traditional methods simply can’t keep up with large inflows of new data, of varying degrees of usefulness.

The entry of AI now means data cleansing experts can use data cleansing and augmentation solutions based on machine learning.

Machine learning and deep learning allow the analysis of the collected data, making estimates, to learn and change as per the precision of the estimates. As more information is analyzed, so also the estimates progress.

So How Does it Really Work?

Since data flows in from numerous sources, any program using ML needs to get data into a stable arrangement to simplify it and ensure consistent patterns across all points of data collection.

Factors may force you to transform the data for use. At this point itself, the suitability of the transformation activity and the definitions must be analyzed.

Once this is done, the bad data must be substituted with good data in the primary source.

This is a very important step as it means all data across the enterprise is refreshed, permeating throughout all the divisions, removing any need for removals in the future.

“Human error is found to be the main reason in critical areas of data collection so any AI based model uses ML to replace humans in identifying bad data and refreshing the models as and when needed”.

ML algorithms can determine flaws in a data analytics model’s logic.

The more information an ML algorithm can work with, the better its predictions. This means contrary to manual cleansing systems, the ML-based algorithm gets better with scale.

As the ML-based software improves over time due to deep learning, the cleaning of data gets faster, even as it is flowing in, which speeds up the entire data delivery process.

Automation also guarantees:

1. Clean data
2. Standardized data
3. Reduced time spent coding and correcting faulty data at the source
4. Allows customers to integrate their 3rd party apps easily

ML-based programs generally use the Cloud. When combined with on-premise delivery, models can provide customizable data solutions.

In other words, any enterprise across verticals like marketing or healthcare can deploy it. This implementation also offers better metadata management abilities to provide better data governance.

Does Data Cleaning Require Manual Intervention, or can it be Automated?

Automation of data cleaning processes can be done through AI and ML algorithms.

Still, manual intervention is usually needed for higher accuracy and to manage complicated data issues that automation single-handedly can’t resolve.

Let’s discuss this further:

Automation in AI data cleaning: AI and ML algorithms can handle bulk data and perform tasks like filling in missing values and correcting inconsistencies.

This automation is specifically essential for structured data. 

Limitations of automation: Automated processes may not always identify context-oriented information and nuances.

They may face difficulties in handling unstructured data, including complicated datasets, images, or text where domain-based knowledge is essential.

Manual intervention’s role: Manual intervention is crucial to predict and verify the automated cleansing process.

It contains tasks such as implementing domain-oriented knowledge and validating the accuracy of automated modifications to make sure that the data cleansing process relates to the actual context of the data.

How Does AI Improve the Data Cleaning Process?

Identifying and handling missing data

AI points out gaps in datasets and recommends smart ways to fill them either through external data sources or predictive modeling. 

Eliminating duplicates

Modern algorithms identify duplicate entries even if they have unstable formatting, minor variations in names.

AI combines or eliminates duplicates perfectly to keep the dataset clean.

Systematizing data formats

AI automatically turns data into a stable format—bettering data formats, systematizing addresses, and integrating measurement or currency units across datasets. 

Reforming instabilities and errors

AI can identify outliers, typos, or differing entries by verifying various data points to recommend corrections.

Verifying and enriching data

AI enriches data by fetching more details from trusted origins, making the data precious for analytics.

Best Practices of Data Cleaning using AI

Before you initiate the data cleaning process, you need to have clarity on what you want to achieve and how you will do it. Implement the following best practices:

Take a bird’s eye view of your data: Ensue that the person who is conducting analysis is not relying on the results from the data.   

Expand controls on inputs: Ensure that the system contains cleanest data.

Find and resolve poor data: Stop poor data before it leads to incorrect results by using tools that have that feature.

Restrict the sample size: With vast datasets, vast samples needlessly extend prep time and reduce performance.

Conduct spot checks: Identify errors before they can be repeated throughout the data.

How AI is Changing Data Cleaning: Real-World Examples

Below are a few real-world examples where AI has remarkably increased data-cleaning operations:

Retail: Merging customer data from various origins

An international retail store gathers customer data from a variety of sources, resulting in inconsistent entries, i.e., misspelled city names, product names, etc.

Example: The retail store uses AI/ML-based clustering algorithms to categorize similar entries and use past behavior of customers to fill gaps in customer profiles.

This reduced manual efforts by 40% and better-personalized marketing efforts.

Restaurants: Simplifying operations

Example: An international fast-casual restaurant chain encountered issues with unstructured data from online orders across different platforms such as its app, Uber Eats, etc.

Variations in item names, missing customer information, and pricing typos resulted in delivery errors.

The company used NLP to validate pricing and standardize menu item details. This resulted in a 50% increase in daily sales reports.

Consumer goods: Reducing product returns

A reputed skincare company noticed a rise in returns due to “worthless product” complaints.

Manual inspection of return forms wasn’t fast, and unorganized customer feedback wasn’t considered seriously.

The company used AI tools to clean and organize return data, adding batch numbers to customer sentiments received from reviews. The result was an 18% reduction in returns.

Use Cases of AI in Data Cleaning

It’s difficult to manage large datasets as the process demands data integration and automation. You can do this using AI-enabled data cleaning solutions.

1. AI-based data cleaning in eCommerce (e.g., customer databases)

eCommerce platforms deal with tons of customer and transaction data. 

Let’s see how using AI for data cleaning turns out to be a game-changer for eCommerce businesses:

a) Remove duplicate customer profiles

eCommerce stores usually collect duplicate records due to several sign-ups, invalid data entries, or guest checkouts.

AI-based deduplication algorithms inspect data trends and combine unnecessary records, ensuring a unified customer view.

b) Fixing irregular data formats

AI-based models organize data by identifying and correcting variabilities in fields such as email formats, addresses, and phone numbers.

c) Managing incomplete or missing data

AI for data cleansing uses predictive analytics to fill missing values according to past trends.

This is done to ensure partial customer profiles don’t impact sales and marketing efforts.

2. AI-based data cleaning in CRM and marketing

AI-enabled data cleaning in machine learning allows marketers to work with updated information, ensuring outstanding results from marketing campaigns.

a) Increasing email deliverability

Bad data quality results in low engagement rates and email bounces.

AI-enabled cleaning ensures formatted and authentic email addresses, enhancing the success of email campaigns and lowering spam complaints.

b) Automated lead scoring

AI excludes duplicate, outdated, and incomplete leads, refining lead-scoring models and increasing conversion rates for marketing efforts.

c) Improved ad targeting

AI-based data cleansing filters out audience data to help your ads reach a suitable target group.

What are the Benefits of Data Cleaning?

For any business that is data-centric, data cleaning is an extremely crucial step. As a result, businesses can remain agile by adapting to changing business scenarios.

Your data cleaning choices should be aligned with your data management strategy for a successful data cleaning strategy.

Ultimately, data cleaning contributes to a very high level of data quality within your enterprise data management system.

1. Removes anomalies and noise: As we discussed above, the way we prepare and clean data doesn’t always work in everyone’s favor. This means that some rows may have some special cases (special characters, color data, incorrect formatting, etc.) that can’t be incorporated into analytics projects.

2. Improves data quality: Data scientists prefer clean data with no missing values, and “typical” business transactions are processed by standard SQL queries without any additional manipulations.

Data should be cleaned properly and stripped of the unusual data.

The benefits also include:

1. an increase in the precision of data used for prediction
2. an increase in the speed of data analysis
3. the storing of data in tables instead of spreadsheets, which can be done without any change in application.
4. reduction in data errors and changes in data which can negatively affect the data model and later data modeling

By cleaning data, an enterprise can minimize the risk of data entry errors by employees and systems.

Effects of Data Cleaning on AI and ML Reliability

Businesses use AI data cleansing techniques to improve the trustworthiness of AI and ML systems.

These techniques play a crucial role in removing instabilities, and missing entries from the datasets.

Data cleaning removes errors and irregularities in data to make it accessible for AI and ML algorithms. 

This will eventually lead to correct recommendations, predictions, and classifications. 

AI data cleaning simplifies the phases of learning and increases the speed of AI and ML systems.

The Future of AI and Data Cleansing

AI and data cleansing are changing analysis and data management.

According to McKinsey research, AI has increased sales and marketing ROI by 5% for businesses investing in top-quality analysis and data management for excellent customer insights. 

Self-service AI models: AI-based data cleansing tools will consistently learn from historical corrections and errors, making them perfect over a period of time. 

Combination with cloud and big data: AI will consistently combine with big data platforms, ensuring top-quality data across platforms.

Data scientists and the data warehouse personnel deal with a huge amount of information and need to be highly selective and methodical in what they deliver to business users.

Additionally, data cleaning enables you to migrate to newer systems and to merge two or more data streams.

References:

How to clean dirty data