5 Data Cleaning Techniques

Editor’s Note: This post was originally published in October 2023. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Summary:

• Data cleaning is crucial for improving the quality, integrity, and consistency of your data.
• Common data cleansing techniques include standardizing formats, filling in missing values, eliminating duplicates, correcting typos, and filtering outliers.
• Tamr's AI-native master data management (MDM) technology helps organizations automate and streamline the data cleansing process, enabling it to scale as data continues to grow.

Data cleaning is the process of identifying, correcting, and removing errors, inconsistencies, and inaccuracies in datasets. By scrubbing and refining the data, organizations can improve its quality, integrity, and consistency. 

Unlike data validation which verifies data accuracy and integrity, data cleansing involves filling in missing data values, standardizing formats, eliminating duplicates, correcting inaccuracies, and resolving inconsistencies. It can be a time-consuming process, one that accounts for 80% to 90% of the work of data scientists, making it the most time-consuming, and arguably the least rewarding, data science task. With a solution like Tamr, however, this work can be drastically reduced by applying AI to automate over 90% of the work involved with large dataset cleaning projects. And, with real-time APIs, organizations can then ensure their data is kept clean on a continuous basis. 

5 Data Cleaning Techniques

When companies prioritize cleaning up their data, a good first step is to conduct an assessment to identify the data quality issues hidden deep within their data. With this information in hand, the next step is to begin improving the quality of the data. There are many data cleansing techniques organizations can employ to improve data quality. Below we explore five of the most common approaches.

1. Standardize formats

Inconsistent formats across datasets make it difficult to curate and master data into golden records. Take date formats as an example. Date formats can vary widely from source to source. In some systems, the year may be four digits, while in others it may be two digits. Some systems may capture month first, while others start with day. Even though these differences seem insignificant, when each system tracks data differently, it’s challenging to integrate the data and create a golden record. Further, using tools like AI-native MDM, organizations can standardize formats at scale across systems and data sets so they can eliminate inconsistencies and enable more accurate data analysis. 

2. Fill in missing values

Identifying missing or null values, and filling them in with accurate information, is another technique used to clean data. Many times, data enters a system of record with some (or many!) fields blank. Manually completing these records is tedious and time consuming. And with data volume and complexity growing at an exponential rate, these manual processes are simply not sustainable. That’s why many organizations today rely on data enrichment. Using data enrichment, companies can tap into external sources to complete blank fields or add new columns to the data to make it more accurate and complete. Tamr takes data enrichment one step further by using ML-driven referential matching to identify matches and relationships that are impossible to spot without external data, helping organizations to gain the best, most complete version of their data.

3. Eliminate duplicates

Deduplication is a critical step in the data cleaning process. Not only can duplicate records skew analysis, but they can also cause poor decisions and misleading results. AI-native MDM helps organizations to identify and eliminate duplicate data. These solutions match records using persistent IDs, AI-driven referential matching, and semantic comparison with LLMs so your business can standardize data and eliminate duplicates across large volumes of business entity data.

4. Correct typos and inconsistencies

Fat-fingered data entries are a common cause of typos and inconsistencies in data sets. But identifying and correcting these errors is critical, as incorrect data may obscure insights and lead to faulty decision-making. Using rich data quality capabilities like those delivered by Tamr, you can spot inaccurate and inconsistent data and quickly see where it is incorrect, incomplete, or duplicative. Then, you can quickly take action and correct the data to deliver the golden records needed for better decision-making and improved business operations.

5. Filter and remove outliers

Outliers are values that deviate significantly from the rest of the data set and skew results. Handling outliers involves more than just identification. It also means deciding if you should remove them, transform them, or analyze them separately. Using advanced AI, Tamr can spot outliers in the data so you can effectively determine how best to deal with them.Done manually, data cleaning is a never-ending task. The sheer volume and variety of internal and external data make it extremely difficult – if not impossible – to manage with manual processes or even legacy, rules-based MDM tools. And as data continues its exponential rise in volume and complexity, the task becomes considerably more demanding – and more critical.

Tamr’s AI-native MDM is at the forefront of data innovation, enabling organizations worldwide to shift their approach to data cleansing. Instead of using a patchwork of manual processes and rules-based technologies, organizations using Tamr are embracing AI as a transformative way to create golden records that deliver valuable insight, enable increased flexibility and scalability, drive confident data-driven decision-making, and support seamless ongoing operations. To learn how AI-native MDM can help your organization automate data cleaning, download our ebook Golden Records 2.0: The AI-Native MDM Advantage.

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