Make your data cleansing steps fit your 2021 data quality resolutions.
You will want to take stock of your data quality in 2021, your organization needs high quality data in order to keep seeing growth in your online business. Towards this end, you need to constantly be revisiting and improving processes to achieve the best data quality. Ensure you are including these five vita data cleansing steps to higher quality data.
Messy data clogs up online transactions, frustrating workers and customers while putting your business at risk for failure. Using a high quality data cleansing solution and following through with data cleansing steps from your 2021 strategy —part of your data quality action plan — gets you and your company usable information.
Read on to learn the five most important data cleansing steps that will improve your business in 2021.
What is data cleansing?
Data cleansing standardizes data already inputted by a person or computer by correcting errors — from duplications, omissions, incorrect information to misspellings. When done well, data cleansing results in actionable information that makes sense to another data system or person.
Great data cleansing comes from data quality cleaning strategies strategies that align with business goals.
Data cleansing is done through software tools and manual processes. Automation takes care of the routine data cleansing, while workers take care of trickier tasks, like teaching a system what to look for and clean.
Why IS data CLEANSING Important?
Cleansing data helps your business:
- Adapt to marketplace changes: You may wish to sell new or different products and services. Customers’ demands shift, and you want to respond. To make these new goals successful, you need to refit your information refit for the new context
- Migrate to a new data system: Organizations and businesses upgrade older data systems to make their transactions more efficient. Successfully migrating data from one database to another requires cleansing the data to match the new system’s data model.
- Integrate data systems: Company data exists in various data systems that need to be merged, in one place, to complete work tasks. Data from the older data platforms need to match the newer ones.
- Gain business insights: Clean high quality data allows data scientists to gain new insights into future products and services. The combined data retrieved by the data scientist needs cleaning to see upcoming marketplace patterns and trends.
- Deal with data entry mistakes: Workers and systems occasionally add incorrect data, accidentally. These errors accumulate to a significant point in the future, which will require cleaning.
What are the best practices for data cleaning?
Create a data cleaning processes strategy
The first step to a successful data cleaning strategy is to ensure your data cleaning choices align with your business and data management plans. A data cleansing process strategy gives you the means to achieve your data quality plans by knowing what data sets to clean, when, why, how, and where.
You can create a data cleansing strategy from your user stories. That way, you do not spend too much time planning your strategy and can fix potential problems before customers find them.
Use data governance to direct data cleansing choices
Data governance, a formal collection of practices and processes, clarifies data cleansing responsibilities, and techniques. Through sound data governance decisions, you know who handles what kind of data cleaning and how. Also, you will see where to prioritize data cleansing with the other data management activities.
Related: 2021 Data Management Dictionary
Understand your data systems architecture
You will want to tailor your data cleansing strategy and processes to your data architecture.
Say you have duplicate records that you wish to fix as a single record on a new system. The system assigns a unique id for each record in the old system. You need to know all the places in your old system that refer to each duplicate’s record id; this is so you do not miss any data to transform into that single standard record.
You will want to check your cleaned data matches your expected results periodically. Doing data profiling reviews the cleaned data for quality and content for patterns and how they match expectations upon completing all five steps.
Think of data cleaning as a lifecycle:
Data cleaning steps have to adapt to changing business and technical contexts and may not necessarily work two years from now—plan on changing your processes to stay agile.
Related: 2021 Master Data Management Guide
Five Data cleansing Steps
Step 1: Identify the data to clean
Use your data cleansing strategy and data governance processes to identify data sets for cleaning. Your data stewards, individuals responsible for the quality of data sets assigned to them, should keep track of bad data. Also, doing data profiling to view unique entries will help you see dirty data patterns to clean.
As you identify messy data, have a picture of how you want your clean data to look. Use this image to check your targeted data sets for cleansing. It does not hurt to get feedback from your data scientists about what data sets they clean to free up their time analyzing patterns.
Step 2: Solidify data cleansing techniques
Once you know what data to clean, you should create and solidify your business’ data cleansing techniques. To do this, you need to identify company wide rules that transform useless data into a cleansed state. The patterns tell you what tasks you should automate, and if any should be manual. Automation depends on multiple algorithms correcting this information quickly.
Step 3: Implement processes & techniques
The next step requires the actual implementation of your new data cleansing process, you and your employees carry out the steps , and you will run your data cleansing automation. You know who and what resources you need to set up and run the data cleansing software from your previous steps.
Step 4: Check your data cleaning results
Go back and profile your data. Then test to see that the cleansed data matches the results you anticipated in step one. Be sure to repeat your test a couple of times to get a better handle on the results. To save time, you can automate test runs and data profiling tools.
Once you become confident in your testing methodology and runs, your results will tell you the success of the process and whether you need to make adjustments from what you did in step two.
Step 5: Reevaluate your processes and techniques
Expect to periodically reevaluate your data cleansing processes that you run in 2021. As you acquire other businesses, add new data systems, and redesign your services, your data needs will change.
You will want to repeat steps one through four to get your existing data cleaned to fit your new goals while ensuring the best data cleansing processes remain unchanged. At this time, update your strategy and get your data governance involved.
Data Cleansing Example: The Luton Borough Council
Let’s use Luton Borough Council as a case study, examining how efficient processes and techniques improve data quality.
Like many organizations, the Luton Borough Council, a local UK government council, put through a new management system. Luton Borough wanted all of its housing information in this Corporate Gazetteer (LLPG). That meant integrating data from many older applications with unstandardized data due to “free text” fields.
Alan Kirk and his team took charge of getting usable data in the new housing system. They came up with an efficient process including all five steps to extract all of Luton Borough property data, de-duplicate these records, and match them to the new LLPG.
Using the WinPure™ data cleaning solution, they cleansed a large dataset with over 21,000 properties in under 30 seconds. As a result of their data quality efforts, they had the best, trustworthy property data source for all of Luton Borough.
Learn more about data cleansing:
Further reading to understand data cleansing steps for your 2021 business goals:
- The US. Department of Energy
- Medium article on the benefits and advantages of data cleansing techniques