Neither is optimal, but both can be considered.As a first option, you can drop observations that have missing values, but doing this will drop or lose information, so be mindful of this before you remove it. As a second option, you can input missing values based on other observations again, there is an opportunity to lose integrity of the data because you may be operating from assumptions and not actual observations.As a third option, you might alter the way the data is used to effectively navigate null values.Īt the end of the data cleaning process, you should be able to answer these questions as a part of basic validation.If not, is that because of a data quality issue?įalse conclusions because of incorrect or “dirty” data can inform poor business strategy and decision-making.Can you find trends in the data to help you form your next theory?.ĭoes it prove or disprove your working theory, or bring any insight to light?.Does the data follow the appropriate rules for its field?. False conclusions can lead to an embarrassing moment in a reporting meeting when you realize your data doesn’t stand up to scrutiny. To do this, you should document the tools you might use to create this culture and what data quality means to you.ĭata cleaning tools and software for efficiency Before you get there, it is important to create a culture of quality data in your organization. Software like Tableau Prep can help you drive a quality data culture by providing visual and direct ways to combine and clean your data. Tableau Prep has two products: Tableau Prep Builder for building your data flows and Tableau Prep Conductor for scheduling, monitoring, and managing flows across your organization. Using a data scrubbing tool can save a database administrator a significant amount of time by helping analysts or administrators start their analyses faster and have more confidence in the data. Understanding data quality and the tools you need to create, manage, and transform data is an important step toward making efficient and effective business decisions.
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