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 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.

Neither is optimal, but both can be considered. There are a couple of ways to deal with missing data. You can’t ignore missing data because many algorithms will not accept missing values. If an outlier proves to be irrelevant for analysis or is a mistake, consider removing it. This step is needed to determine the validity of that number. Remember: just because an outlier exists, doesn’t mean it is incorrect. However, sometimes it is the appearance of an outlier that will prove a theory you are working on. If you have a legitimate reason to remove an outlier, like improper data-entry, doing so will help the performance of the data you are working with. Often, there will be one-off observations where, at a glance, they do not appear to fit within the data you are analyzing. For example, you may find “N/A” and “Not Applicable” both appear, but they should be analyzed as the same category. These inconsistencies can cause mislabeled categories or classes. Structural errors are when you measure or transfer data and notice strange naming conventions, typos, or incorrect capitalization. This can make analysis more efficient and minimize distraction from your primary target-as well as creating a more manageable and more performant dataset.

For example, if you want to analyze data regarding millennial customers, but your dataset includes older generations, you might remove those irrelevant observations. Irrelevant observations are when you notice observations that do not fit into the specific problem you are trying to analyze. De-duplication is one of the largest areas to be considered in this process. When you combine data sets from multiple places, scrape data, or receive data from clients or multiple departments, there are opportunities to create duplicate data. Duplicate observations will happen most often during data collection. Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Step 1: Remove duplicate or irrelevant observations

While the techniques used for data cleaning may vary according to the types of data your company stores, you can follow these basic steps to map out a framework for your organization.

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