Alltake's Data Cleaning

1. Alltake’s Data Cleaning When it comes to cleaning your data, Alltake can help you by suggesting you use these basic steps to create a framework for your organization based on the type of data you store. However, you may need to adjust these steps depending on the kind of data you store. Observations that are duplicates or irrelevant should be removed. If there are duplicate observations or irrelevant observations in your dataset, these should be removed from the dataset as well. During data collection, it is most likely that duplicate observations will occur. In reality, there are many occasions when you may create duplicate data in data sets that are compiled from multiple sources, scraped from other sources, or obtained from multiple departments/clients. It should be noted that during the optimization process, reduplication is one of the most important factors. Observations that are irrelevant in the context of analyzing a problem that you are concerned with, are those that do not seem to fit within that problem. The data from a dataset that contains older generations may not be relevant to what you are analyzing, for instance, if you wish to analyze data about millennial customers. Creating a more manageable and more performant dataset can facilitate analysis and minimize distraction. Fixing structural errors is the second step in the process. There are some examples of structural errors when you are measuring or transferring data and you identify strange naming conventions, grammatical errors, or incorrect capitalization. This may result in a mislabeling of categories or classes due to a lack of consistency. As a result, you may notice that “Not Applicable” and “N/A” appear both at the same time, but these two categories should be analyzed as one. Identify and remove outliers that shouldn't be there. A one-off observation can sometimes appear to be out of place with the others - at first glance, they may not make sense with the rest of the data that you are analyzing. It is beneficial, if there is a legitimate reason for removing an outlier, for example, a thing you have entered incorrectly so that it will improve the efficiency of the data you are working with. On the other hand, the appearance of an outlier can sometimes provide the proof you need to support a theory you have been pursuing. Taking into consideration that an outsider does not necessarily indicate if that it is incorrect just

2. because it exists. The validity of that number can only be determined by this step. It is important to remove any outliers from the analysis if they prove to be irrelevant or are simply errors. Make sure all data is present and correct. Many algorithms will not accept missing values because we do not know what will happen if these missing values are not there. The problem of missing data can be dealt with in a few different ways. However, it would be foolish not to consider both options, even though neither is ideal. If missing values are observed, you can drop them, but this will mean losing data, so be careful. Another option you have is to enter the missing values based on assumptions that were made instead of actual observations; again, this may result in the data being distorted because you could be working from assumptions instead of actual observations. Alternatively, you might change the way the data is used in order to enable the data to navigate effectively when null values are present. Validation and quality assurance. In order to validate the data that has been cleaned, you must be able to answer the following questions at the end of the cleaning process: Can the data be interpreted in a reasonable way? What is the appropriate way to treat the data in the field it belongs to? Are you able to draw any insight from it, or can you determine if it proves or disproves your working theory? Would the data help you form a new theory or can it be a tool you can use to help you form a new theory? Are there any data quality issues for this to be the case? Bad business strategy and poor decision-making are often the results of unreliable data or the creation of false conclusions. If you make false conclusions and realize your data does not hold up to a rigorous analysis in a reporting meeting, you may be faced with an embarrassing moment. Nevertheless, before you get there, it is imperative to commit to creating an organization that fosters a culture of quality data. Despite all the problems you are facing in cleaning data, Alltake assures you to help you in creating a data quality culture that starts with documenting which tools you can use to facilitate this and

3. what you value in data quality.