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Data Cleaning Techniques Make Databases Sparkle

June 23, 2016

Without data cleaning techniques, your data can get ugly, and your analysis might be incomplete. “Dirty” data is sometimes hard to find and eradicate, but data cleaning techniques are the elbow grease that ensures spotless analysis. Let’s take a look at what data cleaning is, why it’s important, and how data cleaning techniques can deliver actionable business intelligence to your enterprise in efficient ways.

What is data cleaning?

Also known as data scrubbing, data cleaning is the procedure of removing or correcting any records that are missing, inaccurate or corrupt from a database, table, or set of records. The best data cleaning techniques delete redundant or irrelevant data, correct inaccurate or outdated data, fill in or modify missing or incomplete data, and detect and modify invalid characters. Finally, any data technique process should include a clean up of the data to make sure it’s consistent with a common set of rules. Data cleaning can be done manually, utilizing several data checking operations; or with software and tools that automate these data cleaning techniques, such as Trifacta.

Why does everyone need to implement data cleaning techniques?

Big data is constantly changing and updating the information it provides. Inaccurate, redundant, or outdated information in your data environment directly impacts the quality of your analysis.  So implementing data cleaning techniques is indeed a must do for today’s data analysts, on an ongoing basis. With the right tools, employing sound data cleaning techniques is easier than ever. Critical business data is made flawless with data cleaning techniques so you can have a complete and accurate picture upon which to base decision making.

How else can data cleaning techniques help my company?

Another critical benefit in data cleaning, especially in companies with multiple sources of data, is merging different datasets that otherwise would be spread out throughout your organization with no integration and communication between them. When this happens, each data set is incomplete, and thus decisions or interpretations are also incomplete. When data cleaning techniques that compile all separate sets into one cohesive conglomeration are employed, your company will get an integrated set of complete data across the board, on an ongoing and repeatable basis. The best data preparation tools incorporate intuitive, smart data preparation techniques and pre-visualization. The result: transformational insights can be drawn on a newly visible holistic view of the data, using a cross-functional team—in about 10% of the time with traditional data analysis methods.

When you implement routine data cleaning techniques a priority in your business, you can trust the information enough to return fast, quality business intelligence that is actionable, and drives sales, as PepsiCo did with Trifacta. Here’s another example: if you’re in a direct sales organization, you probably know that up to 20% of your contact data can expire in a year. Data cleaning techniques, implemented with modern tools like Trifacta, can save you wasted time in sales efforts; and improve your speed to market against your competitors.

Deploying intuitive and systematic data cleaning techniques keep your sales, lead, customer, and operations data pristine.  Tools like Trifacta Wrangler allow you to preserve the source data while performing critical data cleaning techniques; and also give you version control, rollback and sharing tools to spread those effective data cleaning techniques throughout your organization. Just a little polish with data preparation techniques included in Trifacta’s data wrangling suite can help your data—and your analysis—shine!

To learn more about data cleaning techniques, read the O’Reilly book on data wrangling for agile analytics.

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