The Essential Data Cleansing Checklist
This article covers data quality issues, such as missing, duplicate, or inaccurate values, which cause headaches. Creating a suitable data cleansing checklist makes it ideal to use in systems.
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Data quality issues, such as missing, duplicate, inaccurate, valid, and inconsistent values, cause headaches in finding and using data sets. Having a suitable data cleansing procedure handles this bad data and makes it suitable for other people and systems.
A helpful data cleansing process standardizes data, fixes, or removes erroneous values, and formats records to be readable. You get these adequate results from data cleansing when you know your data’s original purpose and visualize the good data you require to meet new goals. You need to create a good foundation and run through the essential data cleansing checklist in this article to achieve your objectives.
Recognize When Your Data Needs Change From Its Original Purpose
Clean your data sets any time you use them for a different business purpose or context than from the data’s creation. At the start of the data lifecycle, you create and obtain data for some reason and within explicit and implicit circumstances, such as customer preferences for online shopping or technologies available at that time. Recognize this data’s original purpose.
Expect that over time and with a better understanding of a problem, your needs with this data set will change from its original purpose. To adapt, you may need to migrate data from one system to another or integrate data from multiple systems to achieve our new business objective. Perhaps you will end up transforming that data to fit a new business problem and its situation. We need to revisit our structures supporting data cleansing and rerun through our essential data cleansing checklist in any of these cases.
Create a Good Foundation
Any data cleaning project succeeds with a good foundation. Like cleaning old files from a system or comments from code you wish to commit, you need an underlying plan, processes, and tools to tackle data cleaning.
To get this strong foundation for your essential data cleansing checklist:
Form a Data Cleansing Strategy: A data cleansing strategy, based on a larger holistic data strategy, informs what data sets to clean and prioritize. You can develop such a plan from your user stories or requirements documentation.
Follow Company-Wide Data Governance Directives When Cleaning Data: Data governance policies and practices formally guide data cleansing activities. Follow data governance guidance in determining your role in cleaning data sets and cleansing outcomes.
Tailor Your Data Cleansing Activities to Your Data Architecture: Data cleansing activities will differ with data technologies. For example, to move data into a data warehouse, you need to massage the migrating data to the data warehouse schema. On the other hand, if you load data from a data lake, you do many different data cleansing iterations. Be sure you know what data technologies require from data cleansing activities.
The Essential Data Cleansing Checklist
Now that you have a good data cleansing structure to work from, you can proceed with the essential data cleansing checklist.
Step 1: Identify Data Sets Requiring Cleansing
Identifying data to clean can be tricky. Use your data cleansing strategy, data governance directives, and system architecture to identify data sets to clean. Also, you need to understand the root causes behind the dirty data.
Sometimes an additional or another fix may be required to get good data. For example, a program executed data extraction, transformation, and load (ETL) improperly. In this case, the data processing algorithm needs repair, and not necessarily the data source cleansed.
Step 2: Consolidate Data Cleansing Techniques Based on Business Rules
After identifying the data to clean, you need to apply business rules to cleanse that data. You do this by finding patterns in the data where an entity can be consolidated, eliminating repetition, and corrected. Automation and data profiling helps here.
Data profiling, done well, checks how your data’s current condition matches your expectations and informs you about business rules to apply when using a data cleansing tool. By laying out these data cleansing steps, you can get a sense of any needed manual processes. For example, you may need someone in a different department to download the data from a source before cleaning that data.
Step 3: Run Your Data Cleansing Processes
Now that you know what data to clean and what defines good data, you run your data cleansing processes. Always make sure you have a backup of the original, non-cleaned data before proceeding with data cleansing. That way, if the resulting data does not meet your criteria, you can reset back to the original data state, fix any data cleansing processes, and rerun them.
Step 4: Validate Your Results
After you complete your data cleansing processes, check your results to see you have standardized your data according to your business needs. You accomplish this by planning, creating, and executing well-thought-out tests of your cleansed data.
You will need automation and data profiling tools to verify data cleansing results quickly. Repeat your testing processes and procedures to make sure you understand your data cleansing results, change any testing steps, and gain confidence in your data cleansing validation. Based on your assessments, decide whether to keep your data cleansing results or repeat steps one through three and try again.
Step 5: Periodically Update Your Data Cleansing Foundation and Techniques
Since your business context and data usages will evolve over the data lifecycle, you want to update your data cleansing strategy, data governance, and data architecture; so, your data cleansing infrastructure continues to function. Also, you want to repeat steps one through four, refreshing any data cleansing activities to meet any new business requirements.
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