What are the most common techniques for cleansing customer data?

2 Standardize and Normalize Data Formats

Another common problem with customer data is inconsistency in data formats, which occurs when the same type of data is stored or displayed in different ways. For example, dates can be written as MM/DD/YYYY, DD/MM/YYYY, or YYYY-MM-DD, or phone numbers can have different separators, prefixes, or country codes. Inconsistency in data formats can cause errors, confusion, and misinterpretation of your data. To standardize and normalize data formats, you need to define a consistent format for each type of data and apply it to your entire dataset. You can use tools or functions to convert, parse, or format your data accordingly. For example, you can use the Excel function Text to Columns or the Python library Datetime to perform this task.

3 Validate and Correct Data Values

A third common problem with customer data is inaccuracy in data values, which occurs when the data contains errors, typos, outliers, or missing values. Inaccuracy in data values can reduce the validity and reliability of your analysis and lead to wrong conclusions or recommendations. To validate and correct data values, you need to define the expected range, distribution, or pattern for each type of data and check if your data meets those criteria. You can use tools or functions to identify, flag, or replace the invalid or incorrect values. For example, you can use the Excel function IFERROR or the Python library Numpy to perform this task.

4 Enrich and Enhance Data Attributes

A fourth common problem with customer data is incompleteness in data attributes, which occurs when the data lacks some information or details that could be useful for your analysis. For example, you might have the customer's name and email address, but not their location, age, or gender. Incompleteness in data attributes can limit the scope and depth of your analysis and prevent you from segmenting, targeting, or personalizing your marketing campaigns. To enrich and enhance data attributes, you need to find and add relevant information or details to your data from external or internal sources. You can use tools or functions to append, join, or merge your data with other datasets. For example, you can use the Excel function VLOOKUP or the Python library Pandas to perform this task.

6 Document and Maintain Data Quality

A final common problem with customer data is deterioration in data quality, which occurs when the data becomes outdated, irrelevant, or inconsistent over time. Deterioration in data quality can affect the accuracy and relevance of your analysis and lead to poor decisions or outcomes. To document and maintain data quality, you need to establish a data quality framework and process that defines the standards, metrics, roles, and responsibilities for ensuring and improving your data quality. You also need to monitor, audit, and update your data regularly and report any issues or changes. You can use tools or functions to track, measure, or communicate your data quality. For example, you can use the Excel function COUNTIF or the Python library Matplotlib to perform this task.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?