According to a Gartner report on data quality, poor quality data weakens an organization's competitive standing and undermines critical business objectives. Missing, inconsistent, or incorrect data can lead to false conclusions and therefore ineffective decisions. And in the business world, that can be costly. Data sets picked up from disparate sources could have a number of issues, including missing values, inaccuracies, duplicates, incorrect or missing delimiters, inconsistent records, and insufficient parameters. In some cases, data can be corrected manually or automatically with the help of data wrangling tools and scripts, but if it cannot be repaired, it must be removed from the dataset. Although the terms Data Cleaning and Data Wrangling are sometimes used interchangeably, it is important to keep in mind that data cleaning is only a subset of the entire Data Wrangling process. Data Cleaning forms a very significant and integral part of the Transformation phase in a data wrangling workflow. A typical data cleaning workflow includes: Inspection, Cleaning, and Verification. The first step in the data cleaning workflow is to detect the different types of issues and errors that your dataset may have. You can use scripts and tools that allow you to define specific rules and constraints and validate your data against these rules and constraints. You can also use data profiling and data visualization tools for inspection. Data profiling helps you to inspect the source data to understand the structure, content, and interrelationships in your data. It uncovers anomalies and data quality issues. For example, blank or null values, duplicate data, or whether the value of a field falls within the expected range. Visualizing the data using statistical methods can help you to spot outliers. For example, plotting the average income in a demographic dataset can help you spot outliers. That brings us to the actual cleaning of the data. The techniques you apply for cleaning your dataset will depend on your use case and the type of issues you encounter. Let’s look at some of the more common data issues. Let’s start with missing values. Missing values are very important to deal with as they can cause unexpected or biased results. You can choose to filter out the records with missing values or find a way to source that information in case it is intrinsic to your use case. For example, missing age data from a demographics study. A third option is a method known as imputation, which calculates the missing value based on statistical values. Your decision on the course of action you choose needs to be anchored in what’s best for your use case. You may also come across duplicate data, data points that are repeated in your dataset. These need to be removed. Another type of issue you may encounter is that of irrelevant data. Data that does not fit within the context of your use case can be considered irrelevant data. For example, if you are analyzing data about the general health of a segment of the population, their contact numbers may not be relevant for you. Cleaning can involve data type conversion as well. This is needed to ensure that values in a field are stored as the data type of that field—for example, numbers stored as numerical data type or date stored as a date data type. You may also need to clean your data in order to standardize it. For example, for strings, you may want all values to be in lower case. Similarly, date formats and units of measurement need to be standardized. Then there are syntax errors. For example, white spaces, or extra spaces at the beginning or end of a string is a syntax error that needs to be rectified. This can also include fixing typos or format, for example, the state name being entered as a full form such as New York versus an abbreviated form such as NY in some records. Data can also have outliers, or values that are vastly different from other observations in the dataset. Outliers may, or may not, be incorrect. For example, when an age field in a voters database has the value 5, you know it is incorrect data and needs to be corrected. Now let’s consider a group of people where the annual income is in the range of one hundred thousand to two hundred thousand dollars—except for that one person who earns a million dollars a year. While this data point is not incorrect, it is an outlier, and needs to be looked at. Depending on your use case, you may need to decide if including this data will skew the results in a way that does not serve your use case. This brings us to the next step in the data cleaning workflow—Verification. In this step, you inspect the results to establish effectiveness and accuracy achieved as a result of the data cleaning operation. You need to re-inspect the data to make sure the rules and constraints applicable on the data still hold after the corrections you made. And in the end, it is important to note that all changes undertaken as part of the data cleaning operation need to be documented. Not just the changes, but also the reasons behind making those changes, and the quality of the currently stored data. Reporting how healthy the data is, is a very crucial step.