Most common types of dirty data

Dirty data

One of the most expensive overheads in many organizations is data cleaning. Unclean data is present in different forms. Your company might suffer in the form of omissions and errors present in the master data you need for analytical purposes. Since this data is used in important decision-making processes, the effects are costly. By understanding the different ways dirty data finds its way into your organization, you can find ways of preventing it, thereby improving the quality of data you use. 

In most instances, automation is applied in data collection. Because of this, you might experience some challenges with the quality of data collected or consistency of the same. Since some data is obtained from different sources, they must be collated into one file before processing. It is during this process that concerns as to the integrity of the data might arise. The following are some explanations as to why you have unclean data:

 Incomplete data

The problem of incomplete data is very common in most organizations. When using incomplete data, you end up with many important parts of the data blank. For example, if you are yet to categorize your customers according to the target industry, it is impossible to create a segment in your sales report according to industry classification. This is an important part of your data analysis that will be missing, hence your efforts will be futile, or expensive in terms of time and resources invested before you get the complete and appropriate data. 

Errors at input

Most of the mistakes that lead to erroneous data happen at data entry points. The individual in charge might enter the wrong data, use the wrong formula, misread the data, or innocently mistype the wrong data. In the case of an open-ended report like questionnaires, the respondents might input data with typos or use words and phrases that computers cannot decipher appropriately. Human error at input points is always the biggest challenge in data accuracy. 

Data inaccuracies

Inaccurate data is in most cases a matter of context. You could have the correct data, but for the wrong purpose. Using such data can have far-reaching effects, most of which are very costly in the long run. Think about the example of a data analyst preparing a delivery schedule for clients, but the addresses are inaccurate. The company could end up delivering products to their customers, but with the wrong address details. As a matter of context, the company does have the correct addresses for their clients, but they are not matched correctly.

  Duplicate data

In cases where you collect data from different sources, there is always a high chance of data duplication. You must have a lot of checks in place to ensure that duplicates are identified. For example, one report might list student scores under Results, while another will have them under Performance. The data under these tags will be similar, but your sensors will consider them as two independent entities. 

Problematic sensors

Unless you are using a machine that periodically checks for errors and corrects them or alerts you, it is possible to encounter errors as a result of problematic sensors. Machines can be faulty or breakdown too, which increases the likelihood of a problematic data entry.

 Incorrect data

entries An incorrect entry will always deliver the wrong result. Incorrect entry happens when your dataset includes entries that are not within the acceptable range. For example, data for the month of February should range from 1 to 28 or 29. If you have data for February ranging up to 31, there is definitely an error in your entries.

 Data mungling

If at your data entry point you use a machine with problematic sensors, it is possible to record erroneous values. You might be recording people’s ages, and the machine inputs a negative figure. In some cases, the machine could actually record correct data, but between the input point and the data collection point, the data might be mungled, hence the erroneous results. If you are accessing data from a public internet connection, a network outage during data transmission might also affect the integrity of the data.

  Standardization concerns

For data obtained from different sources, one of the concerns is often how to standardize the data. You should have a system or method in place to identify similar data and represent them accordingly. Unfortunately, it is not easy to manage this level of standardization. As a result, you end up with erroneous entries. Apart from data obtained from multiple sources, you can also experience challenges dealing with data obtained from the same source. Everyone inputs data uniquely, and this might pose a challenge at data analysis.

How to Clean Data

 Having gone through the procedures described above and identified unclean data, your next challenge is how to clean it and use accurate data for analysis. You have five possible alternatives for handling such a situation: 

Data imputation

 If you are unable to find the necessary values, you can impute them by filling in the gaps for the inaccurate values. The closest explanation for imputation is that it is a clever way of guessing the missing values, but through a data-driven scientific procedure. Some of the techniques you can use to impute missing data include stratification and statistical indicators like mode, mean and median. 

If you have studied the data and identified unique patterns, you can stratify the missing values based on the trend identified. For example, men are generally taller than women. You can use this presumption to fill in missing values based on the data you already have. 

Some datasets are very critical, and imputing might introduce a personal bias which eventually affects the outcome. 

Data scaling 

Data scaling is a process where you change the data range so that you have a reasonable range. Without this, some values that might appear larger than others might be given prominence by some algorithms. Some algorithms will give the population priority overage, and might ignore the age variable altogether. 

By scaling such entries, you maintain a proportional relationship between different variables, ensuring that they are within a similar range. A simple way of doing this is to use a baseline for the large values, or use percentage values for the variables.

  Correcting data 

Correcting data is a far better alternative than removing data. This involves intuition and clarification. If you are concerned about the accuracy of some data, getting clarification can help allay your fears. With the new information, you can fix the problems you identified and use data you are confident about in your analysis. 

Data removal 

One of the first things you could think about is to eliminate the missing entries from your dataset. Before you do this, it is advisable that you investigate to determine why the entries are missing. In some cases, the best option is to remove the data from your analysis altogether. If, for example, more than 80% of entries in a row is missing and you cannot replace them from any other source, that row will not be useful to your analysis. It makes sense to remove it.

Data removal comes with caveats. If you have to eliminate any data from your analysis, you must give a reason for this decision in a report accompanying your analysis. This is important so as to safeguard yourself from claims of data manipulation or doctoring data to suit a narrative. 

Some types of data are irreplaceable, so you must consult experts in the associated fields before you remove them. Most of the time, data removal is applied when you identify duplicates in the data, especially if removing the duplicates does not affect the outcome of your analysis.

 Flagging data 

There are situations where you have columns missing some values, but you cannot afford to eliminate all of them. If you are working with numeric data, a reprieve would be to introduce a new column where you indicate all the missing values. The algorithm you are using should identify these values as such. In case the flagged values are necessary in your analysis, you can impute them or find a better way to correct them then use them in your analysis. In case this is not possible, make sure you highlight this in your report. 

Cleaning erroneous data can be a difficult process. A lot of data scientists generally hope to avoid it, especially since it is time-consuming. However, it is a necessary process that will bring you closer to using appropriate data for the objective is to use clean data that will give you the closest reflection of the true picture of events.