Here’s a fact you might not know – about 30% of your database goes bad each year. Even worse, 10%-25% of databases have errors that prevent you from doing your job effectively.
This is a big problem for marketers and CMO’s. They spend tons of time, funds and resources on their marketing automation program, only to find errors in their database preventing them from routing or effectively targeting audience. Not to mention they’re paying for those bad records.
What is coarse data?
The process of data cleansing involves uncovering and correcting errors and inconsistencies in data gathered from various sources. This process is also known as data scrubbing. Mistakes or incomplete data can have multiple causes, such as user error, accidental deletion, or outdated information.
This kind of data is called coarse data, and it qualifies as such when parts of it are inaccurate, incomplete, inconsistent, or irrelevant. Once coarse data has been identified, the data cleansing process includes deleting, correcting, modifying, and updating each entry as needed.
Data cleaning tools
Available software tools for data cleansing are known as data wrangling tools, and they’re included with high-quality customer relationship management software. Data wrangling is accomplished through algorithms programmed to detect inconsistencies and errors.
- Depending on the software’s design, data cleansing can be done manually, set to run automatically at regular intervals, or both. During this process, sets of discovered coarse data are standardized so they match the quality of the rest of the data that doesn’t have these kinds of errors. This phase of data cleansing is sometimes called data harmonizing.
- Standardization and harmonization of data is an essential part of data mining and data augmentation for marketing and promotional purposes. Incomplete and incorrect data can cost businesses revenue as well as productivity, which makes data cleansing essential.
Keeping your data clean
These tools allow for easy updates and corrections, as well as the addition of missing information. Cross-checking and scanning of data sets can be customized for a wide variety of data types on existing customer profiles.
This aspect of maintaining data quality will continue to grow in importance, as digital marketing continues to rely on customer and sales lead data as the engine that drives effective marketing efforts. The quality and integrity of these data sets can be the determining factors in how well a business is able to maintain enough competitiveness over time.
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