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.
Data cleaning involves filling in missing values, standardizing formats, eliminating duplicates, correcting inaccuracies, and resolving inconsistencies. It’s 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.
5 Data Cleaning Techniques When companies prioritize cleaning up their data, a good first step is to conduct a data quality analysis. That way, they can identify the data quality issues hidden deep within their data. Next, they’ll begin cleaning the data. There are many techniques organizations can employ to clean their data. Below we explore five of the most common approaches.
Standardize formats Inconsistent formats across datasets make it difficult to curate and master data into a holistic view. For example, date formats can vary widely. In some systems, the year may be 4-digits, while in others it may be 2-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 master view. Standardizing formats across systems and data sets using data products eliminates inconsistencies and enables more accurate data analysis.
Fill in missing values Identifying missing or null values, and filling them in with accurate information, is another data cleaning technique. Many times, data enters a system of record with some or many fields blank. Manually completing these records is tedious at best. That’s why many organizations today rely on data enrichment. Using data enrichment, companies can tap into external sources to fill blank fields or add new columns to the data to make it more accurate and complete. Tamr data products take 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.
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 lead to misleading results. Identifying and eliminating duplicate records requires data products to help you standardize data, match records using persistent IDs and AI-driven referential matching, and semantic comparison with LLMs.
Correct typos and inconsistencies Fat-fingered data entries are a common cause of typos and inconsistencies in data sets. Identifying and correcting these errors is critical though, as incorrect data may obscure insights and lead to faulty decision-making. Data products employ built-in data quality capabilities to spot inaccurate and inconsistent data, identifying where it is incorrect, incomplete, or duplicative so you can quickly take action and correct the data.
Filter and remove outliers Outliers are values that deviate significantly from the rest of the data set and skew results. Handling outliers involves not just identifying outliers, but also deciding if you should remove them, transform them, or analyze them separately. Using advanced AI, data products 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 master data management tools.
That’s why organizations today are shifting their approach to data cleaning. They’re implementing data product strategies executed through the design and use of data products. Data products are the perfect synergy of AI and human intelligence, making it simple for businesses to access, connect, organize, and enrich data. They also create consistency in the data so it’s easier to curate and update it. As a result, organizations can uncover insights previously-hidden in the data so they can work smarter.
To learn how Tamr can help your organization improve data quality using data products, please book a demo.