How AI Data Cleansing, Data Cleaning & Data Scrubbing Decreases Costs

Did you know dirty data is not only harmful to your brand’s reputation but detrimental to your bottom line? In fact, a study by Gartner revealed organizations surveyed are losing an estimated $14.2 million annually. And, according to IBM research, bad data costs US companies a total of $3.1 trillion per year. Of course, we all knew bad data was…well, bad, but the bottom-line financials are beyond staggering.

Sadly, 94% of companies suspect they have inaccuracies in their data yet don’t pursue data cleansing for various reasons: costs, lack of skills/resources, or the absence of a sustainable data plan. The good news is there is a practical solution.

Data cleansing is an ideal use case application for AI. AI has not only been successful in reducing data cleansing costs but in catching bad or corrupt data before it enters your database. Let’s explore a few common data errors, the AI application for correcting the problems and best practices for data cleaning.

What Is Data Cleansing

Data cleansing, also referred to as data cleaning or data scrubbing, is the process of identifying and cleaning inaccuracies and inconsistencies in data. It is often used to improve the quality of data for analysis purposes.

The process can involve other methods, such as comparison with outside sources (e.g., compare the list of customers owning a particular product with other lists) or applying logic (e.g., identifying inconsistencies in dates). Some data cleaning tools use crowd sourcing to identify and correct inaccuracies.

Data cleansing work has always been one of the most time consuming tasks in the analysis workflow, and it is easy to lose data quality when working with a large number of tables. This makes having a data cleansing program an essential step for every analysis work flow.

Having a data cleansing tool aims at minimizing this tedious process by providing simple and dynamic tools to help you rapidly clean dirty data while exploring your quality data.

Duplicate Data

Duplicates in data are far too common and easily sneak into a dataset. For example, variations in spellings or address abbreviations during data collection, address changes, data coming in from multiple sources, and data syncing errors from one system to another can all result in duplicate records and data quality issues.

To give you an idea of the magnitude of the duplicate data problem:

Duplicate records are one of the biggest problems with data sets, resulting in high costs for the organization, reduced productivity for customer service personnel, wasted dollars when sending direct mail pieces, not to mention the negative impact on customer perceptions and confidence.

Structural Errors, Typos and Data Entry Inaccuracies

Inaccurate data is worse than no data at all. Structural errors and data inaccuracies include anything from typos to incorrect abbreviations to inconsistencies in dataset entry, particularly when entering city and state names to missing unit numbers or other incomplete fields. Invalid values/ranges (10%) and missing fields (8%) are the most common data quality issues, and research shows 10-25% of marketing databases contain critical data errors.

Many structural errors and inaccuracies stem from basic human mistakes, often due to employee tiredness, the pace of entering the data, or other distractions in the workplace. This makes having a data cleaning process backed by AI even more essential.

Improper Entry or Unorganized Data

Data entered into the incorrect field or data entered that your software can’t sort out are examples of unorganized data. As a result, data cannot be properly segmented. In order for data analysts to gain accurate and meaningful insights from the data, the data needs to be consistently entered in a format machines and systems can interpret. As an example, if city information is entered in “address2” vs. the “city” field, attempts to segment records by the city will be incomplete, as entries with the incorrect data placement will not be selected. Unorganized data is particularly problematic for postal sortation processes.

Obviously, errors and inaccuracies have to be fixed for an organization to effectively use its data.

Data Cleansing Process and Data Tuning

Data cleansing is two-part: cleaning the data followed by data tuning. Data cleaning removes duplicates, fixes errors, and adds missing data to records. Data tuning structures the data to be consistent. Once data has been cleaned up, it’s known as “technically accurate data.” Once it’s been tuned, it becomes “uniform data.”

Cleaning big data, especially, is a time-consuming process. A survey by Anaconda found that data scientists spend approximately 45% of their time cleaning and organizing data. Additionally, 57% of data scientists surveyed said data cleaning was their least favorite part of the job. Terms like “data janitor,” “data wrangling,” and “data munging” have been used to describe the “painful process of cleaning, parsing, and proofing data.” But, the data pros know that in order to have meaningful insights, data accuracy is essential. After all, bad data leads to incorrect analyses, predictions, and bad decisions. And this is where AI can significantly improve the data cleansing process and outcomes.

How AI Helps with Data Cleaning & Data Scrubbing

Whereas humans become tired or even ineffective in data cleansing, AI fits the bill quite nicely. AI can clean large volumes of data in significantly less time and with a higher degree of consistency and completeness. Additionally, organizations can automate data collection, validation, and cleansing, further streamlining the process. While AI doesn’t replace the need for data scientists, it does make their jobs easier (and more enjoyable!), not to mention more productive.

Data collection is everywhere, from your purchases at the grocery store to online search behavior to lifestyle habits and more. And data volume— including bad data— increases every year, too. Every time bad data is discovered, a data scientist must write code to tell the system what to do with it and how to process it. Unfortunately, as volume increases, the process becomes more complex, making it difficult for humans to keep up, resulting in the bad data problem worsening over time.

With AI, the data cleaning outlook is very different. AI can recognize patterns and anomalies in data that humans might not recognize more quickly. Additionally, as more data comes in, the incoming data feeds the AI algorithm, so the cleansing process is continually refined as volume increases. Of course, the key to effective AI data cleansing is training the machine on how it systematically analyzes, rates, and uses data so that it can become better at correcting, repairing, updating, and improving it. Overall, AI can handle large volumes of data cleaning tasks in less than a day, whereas it might take a data scientist weeks to perform.

The Importance of AI Cleaned Data

We now know the dangers of bad data, but what is the impact of having AI clean and tune your data? For starters, faster data cleansing provides more accurate views of your database. Consider, too, how other parts of your business are likely using your database:

  • Marketing: sending the right message to the right audience at the right time; list segmentation and filtering, creating marketing plans and building campaigns, identifying best prospects, discerning propensity to purchase signals.
  • Sales: reliant on a complete view of the customer; intent to purchase signals.
  • Legal and compliance: ensuring data collection, storage, and customer communication preferences are accurately recorded and in compliance with various privacy regulations.
  • Operations and the C-Suite: better data equates to better strategies and decisions.

Additionally, bad data will always produce unreliable predictions leading to false conclusions and inaccurate outcomes if you use other AI models in your business. As an example, suppose you are a medical provider using AI models to analyze and process patient symptoms. Imagine what could go horribly wrong if the patient’s base medical records or health history data has inaccuracies and errors; the outcome could be harmful to both the patient and the medical provider. Thus, the interdependence between good data and AI is obvious— and an area in which more organizations should invest.

The Bottom Line

Bad data costs businesses significant money every year. From lost sales opportunities to brand-damaging customer perceptions to legal implications, bad data is just bad business. Creating best practices for data management will provide you with quality results but may seem overwhelming. The good news is that AI is an efficient solution for data cleansing and data tuning, improving data collection and accuracy, and enabling data-driven decisions across your organization. After all, isn’t that the goal of all data, to drive more successful outcomes?

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