Data Scrubbing

The accuracy of a machine learning model depends significantly on the quality of its input data. However, the raw data businesses collect for use in model training comes with imperfections that can muddle the results. Data scrubbing, or data cleansing, is the practice of fixing errors and resolving issues in the raw data before feeding it to a machine learning model.

What Do Data Scrubbing Services Do?

Whether it’s customer survey results, sales metrics, or figures collected in studies, raw data is almost never perfect. It may contain errors, duplicates, and improperly formatted entries among other problems. Examples of issues with raw data include:

  • Duplicates– In an attempt to gather as much data as possible, some duplicates and redundant data points may make their way into your set. Scrubbing eliminates these extra points and merges redundant ones together so that they do not skew the results.
  • Inconsistencies– AI training works best when the input data is in a consistent format throughout. For example, phone numbers may be simple strings or come with dashes and parentheses. Choosing one format not only makes reading the data set easier but also prevents the model from making false inferences.
  • Typos and other errors– Even simple mistakes like typos or incorrect capitalization can unnecessarily muddle an input data set.

These issues can arise from a number of causes. Basic human error is the obvious one, but erroneous entries can also come from database merges or the use of older systems that may harbor obsolete data. A lack of company-wide standards is another common cause.

Much like how reading a poorly-written textbook can result in low test scores, poor data results in inaccurate predictions from the AI. Data scrubbing aims to prepare raw data so that it’s usable for training a machine learning algorithm. The goal is to improve the quality of the AI and its ability to make predictions from the data set.

Data Cleansing vs. Data Cleaning

Data cleansing and cleaning both aim to improve the quality of the input data set and ensure AI developers have an easier time training a new model.

However, data cleaning largely refers to fixing incorrect, duplicate, or ill-formatted data points. Cleaning is often necessary whenever data comes from multiple sources with no consistent collection procedure.

By contrast, cleansing refers to removing entire data points from the set, namely ones that are inaccurate, outdated, or otherwise unneeded. Cleaning ensures that the model learns with the data most applicable to its intended purpose.

How Do Data Scientists Scrub Data?

Data management is a crucial component of AI development, and the most successful businesses build impactful models using cleaned data. The general procedure for cleaning an input data set includes the following steps:

  • Initial audit– A data audit identifies the specific issues with your data set, which can vary depending on your industry and circumstances.
  • Standardization– Before you set all your data points in line with a consistent standard, you have to come up with that standard first.
  • Data scrubbing– At this stage, teams work on resolving the issues the audit found. Duplicates and erroneous entries are the most common problems.
  • Review and verification– A second audit occurs after scrubbing to ensure the cleanliness of the data set prior to model training.

Some challenges facing data scrubbing include agreeing upon data policies and standards. When raw data comes from various sources and business units, coming up with a consistent cleansing standard that’s compatible with all types of data takes collaboration between subject matter experts, data scientists, and management. Sufficient organizational support is necessary to break down the data silos common in many companies.

Another challenge is efficiency. Scrubbing can be a time-consuming task. Surveys show that data scientists spend 45% of their working hours preparing data, which includes data cleansing.

For this reason, data scrubbing itself is often a tool-assisted process. With especially large data sets, scrubbing can be a tedious task without data cleaning applications. Many employees would prefer businesses to use data scrubbing tools to automate much of the process, as they can spend the time much more productively elsewhere.