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

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.

Table of Contents

1. What Is Data Cleaning
2. How To Clean Incoming Data
3. AI and Its role in data cleaning
4. What Are The Benefits Of Data Cleaning

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.

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. 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