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Data wrangling is the process of transforming and mapping data from one raw data form into another format in order to increase the value of your data and prepare it so it can be more effectively integrated with other types of data. Better data integration improves the odds that your analytics queries will be accurate because the data that these queries use will be more standardized.

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Cultivating expert data wranglers in your organization can be very important to your analytics—especially for small- to medium-sized companies that can’t afford to hire high-priced chief data officers or data scientists. You don’t want your very expensive database administrator doing data wrangling, either.

So where do you turn to develop a data wrangler who can prep more of your data for analytics? A majority of organizations look to junior data analysts to do this tedious but very essential work.

Using data mapping tools, data analysts study different data in their raw forms that exist in disparate systems. The data is extracted and then parsed into a single, uniform data structure that exists in a central data repository, which will be used for analytic queries. Once data is converted into the same, single form, data quality and accuracy are improved because you’re no longer referring to the same piece of data under multiple names.

Data wrangling builds data skills

Although much of data wrangling is exactly that—tousling with data in grueling, manual work—an accumulation of data wrangling experience can yield benefits for career growth and also for skills expansion in your data group.

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For example, if an organization hires a student summer intern or uses a junior data analyst for data wrangling—the same person picks up invaluable knowledge about the disparate systems and data that are at work in the company. He or she begins to build a knowledge of underlying data and where the information is. This data knowledge can make an intern worth hiring permanently or a junior data analyst a budding prospect for more senior DBA tasks, such as designing and implementing databases or even working on overall data architecture. Small- to mid-sized organizations can build their data skills this way, while also offering career paths for their best data wranglers.

As a subpart to this, it’s important to mention that not all data wrangling has to be done by hand. A plethora of data wrangling tools are available for discovering, structuring, cleaning, enriching, validating and publishing data, and some of them are free. Experience with any of these tools furthers knowledge of automation in data preparation.