The ultimate guide to CRM Data Cleaning and Cleansing
Definition, Benefits, Components, and how to clean your data in BULKLast modified by Ben Ford, on 26/Feb/2023
What is CRM data cleaning?
What is the difference between CRM data cleaning and data transformation?CRM Data cleaning is the process that removes data that does not belong in your CRM. Data transformation is the process of converting data from one format or structure into another. Transformation processes can also be referred to as data wrangling, or data munging. This article focuses on the processes of cleaning your exitsing CRM data and on raw data that you plan to import into our CRM.
How do you clean data?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.
Step 1: Remove duplicate or irrelevant rows
Remove unwanted rows from your dataset, including duplicate rows or irrelevant data. Duplicate rows will happen most often during data import or ingerations with our systems. When you import data from multiple places, scrape data, or receive data from clients or multiple departments, there are opportunities to create duplicate data. De-duplication is one of the largest areas to be considered in this process.
Step 2: Fix structural errorsStructural errors are when you measure or transfer data and notice strange naming conventions, typos, or incorrect capitalization. These inconsistencies can cause mislabeled categories or classes. For example, you may find “N/A” and “Not Applicable” both appear, but they should be analyzed as the same category.
Step 3: Filter unwanted dataOften, before you import data into your crm you will find taht somes of the rows have irrlenat data that you can't use e.g. leads without email. Filter those rows prior to the import.
Step 4: Handle missing dataYou can’t ignore missing data because many fields will not accept missing values. There are a couple of ways to deal with missing data. Neither is optimal, but both can be considered.
- As a first option, you can drop rows that have missing values, but doing this will drop or lose information, so be mindful of this before you remove it.
- As a second option, you can input missing values based on other rows; again, there is an opportunity to lose integrity of the data because you may be operating from assumptions and not actual observations.
- As a third option, you might alter the way the data is used to effectively navigate null values.
Step 5: Validate and QA
At the end of the data cleaning process, you should be able to answer these questions as a part of basic validation:
Does the data make sense?
- Does the data follow the appropriate rules for its field?
- Does it prove or disprove your working theory, or bring any insight to light?
- Can you find trends in the data to help you form your next theory?
- If not, is that because of a data quality issue?
False conclusions because of incorrect or “dirty” data can inform poor business strategy and decision-making. False conclusions can lead to an embarrassing moment in a reporting meeting when you realize your data doesn’t stand up to scrutiny. Before you get there, it is important to create a culture of quality data in your organization. To do this, you should document the tools you might use to create this culture and what data quality means to you.
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Components of quality data
Determining the quality of data requires an examination of its characteristics, then weighing those characteristics according to what is most important to your organization and the application(s) for which they will be used.
5 characteristics of quality data
- Validity. The degree to which your data conforms to defined business rules or constraints.
- Accuracy. Ensure your data is close to the true values.
- Completeness. The degree to which all required data is known.
- Consistency. Ensure your data is consistent within the same dataset and/or across multiple data sets.
- Uniformity. The degree to which the data is specified using the same unit of measure.
Benefits of data cleaning
Having clean data will ultimately increase overall productivity and allow for the highest quality information in your decision-making. Benefits include:
- Removal of errors when multiple sources of data are at play.
- Fewer errors make for happier clients and less-frustrated employees.
- Ability to map the different functions and what your data is intended to do.
- Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
- Using tools for data cleaning will make for more efficient business practices and quicker decision-making.
Data cleaning tools and software for efficiency
Software like ZaapIT can help you drive a quality data culture by providing visual and direct ways to combine and clean your data. ZaapIT has two products: Dedup-manager for cleaning duplicates and Smart-mass-update for managing any type CRM data (update/create/convert/delete/import/etc). Using ZaapIT's tools can save a database administrator a significant amount of time by helping analysts or administrators start their analyses faster and have more confidence in the data. Understanding data quality and the tools you need to create, manage, and transform data is an important step toward making efficient and effective business decisions. This crucial process will further develop a data culture in your organization.