Melissa AU Team | Australia, Data Cleansing | checklist, Data Cleaning, Machine Learning, practices

Machine Learning (ML) is helping businesses across industries solve problems and deliver tangible benefits. The chatbots customers interact with, to get more information on customized product recommendations on websites – these are examples of how ML helps businesses improve their customer service and simultaneously increase conversion rates. You can build ML models for many real-world applications but its usefulness hinges on one key factor – the quality of data being analysed.

The Need For Clean Data

When it comes to data, simply having a lot of it is not enough. Machine Learning involves the use of statistical techniques on data to train computers in logical thinking. If an ML model uses incorrect or outdated data, the results will be misleading. The financial impact of using poor quality data in 2017 resulted in losses of about $15 million!

Here’s a simple example –Let’s say the woman in a junior position and earning relatively well applies for a credit card but the fintech company’s ML model takes data from 6 years ago when she was a student with minimal income – it would give her a low credit score which would result in the loss of a potential customer.

The only way to avoid such mishaps is by maintaining a clean, high-quality database. This can be quite a challenge given the vast amounts of data being collected, the different sources it is collected from and the human factor.

Data Cleaning Checklist

Before data can be analysed and used for decision-making by ML algorithms, it must meet the basic quality control criteria. To be classified as clean, data must be accurate, valid, complete, consistent and unique. Here are a few tips on how you can clean data to prepare it for ML models.

• Remove Duplicate Data

With data being gathered from multiple sources, there is a high risk of records being duplicated in the database. This may also happen when data is siloed in different departments. Duplicates serve no purpose and can skew analytics. It also increases the storage space required and, in turn, the cost of storing data. Every organization needs a single customer view across departments.

When it comes to data deduplication, businesses must be careful that they do not lose any important information in the process. There are many different ways data can be deduplicated. While some businesses may choose to maintain the most recent record, others may save the first record.

• Remove Irrelevant Data

Businesses capture data at various points but not all of this data is useful. A 3-year-old product inquiry email from a customer is no longer serving any purpose. According to a survey, 33% of global respondents said that 75% or more of their organization’s data is dark. While this data may not be used actively, it can skew ML results. Hence, it must be removed.

The first step to doing so is defining qualifying criteria for specific problems. Data that does not meet these criteria can then be removed from the dataset being used.

In some cases, an entire row of data may need to be removed. Three factors should be kept in mind in such instances:

• Data should be removed only if it is known to be wrong
  • Questionable data should be removed only if the sample size will not be majorly affected
  • Data should be removed only if it can later be recollected.

• Standardize Syntax Formats

The easiest way to understand the importance of standardizing syntax is by imagining the confusion it could cause if an order date was saved at 05/04/2022 (DD/MM/YYYY) in one column and 04/05/2022 (MM/DD/YYYYY) in another. There are many other grammatical and syntactic errors that could occur.

In most cases, syntax errors can be fixed quite easily by structuring the format in which data is entered. Setting strict boundaries such as insisting on country codes for phone numbers can be beneficial. Similarly, using an address auto-complete tool can help ensure that address data is complete and in the correct format while simultaneously improving the customer input experience.

• Validate Data Accuracy

Once data has been deduplicated and put into the correct format, it must be validated to ensure accuracy. This can be difficult to gauge and is possible only when it can be referenced against predefined reliable datasets. Doing so manually is next to impossible. Not only must incoming data be checked for accuracy, but existing data in the database must also be checked from time to time to see that it is still valid. A study found that 58% of organizations are making decisions based on outdated data.

What you need is software that can compare your data against more reliable datasets to ensure its validity and accuracy. For example, customer details can be compared with details available from the driving license records to validate addresses.

• Fill In The Gaps

Just as unwanted data can affect ML outcomes, so can incomplete data. If a customer’s address simply ends with the city as Paris, how would you know if the customer lives in France or the USA? Missing data must be identified and handled as soon as possible. Many ML algorithms will not accept data with missing fields.

Some fields can be filled up quite easily. In the above case, having the customer’s area pin code could help. In other cases, data scientists may need to make calculated guesses to fill in the missing information.

In Conclusion

Data cleansing is a vital step for organizations that use ML algorithms for any kind of business decision-making or customer servicing. Ensuring that only clean data is used will reward the organization with high-quality predictions to keep customers happy with their experience, ensure compliance and boost profits.

For efficient data cleaning, you need to ensure that you have the right software. It should be able to provide real-time validation against trustworthy third-party databases. With the right data cleaning software tool, you too can uncover the full potential of Machine Learning for your business.