8 Best Practices for Data Cleaning

Data is what drives the world today. Having the right data at the right time is the factor that companies are investing in nowadays. Every decision that a business makes can be decisive to its failure and success. However, it goes without saying that data is what makes or breaks a business.

Reports reveal that around 94% of B2B companies suspect that they have inaccurate data in their database. This begs the question, how confident are you regarding the quality of the data in your database? Have you ever thought of cleaning the data?

Let’s pause for a moment: if the data in your CRM is outdated or irrelevant, what ROI, marketing and sales are you missing out on?

Why is Data Cleansing Important?

Data cleaning is important for companies as it improves data quality. In doing so it increases the overall efficiency of the company. When you choose to clean your data you are making sure that your team has no outdated or incorrect information, leaving you with the best quality information available in your CRM. The in-turn ensures that your team does not have to work their way through countless outdated documents, which allows your staff to make the most of their time.

When you have the right information, it reduces some of the unnecessary and unexpected costs. For example, you could end up printing incorrect information onto a company’s letterhead. Having consistent issues can also harm your company’s reputation.

So without any further ado let’s check out 8 best practices for data cleaning.

1. Knowing your goals
It is vital for businesses to set expectations for their data. One can start by analyzing and visualizing how your data should look at the end-stage before starting any data processing activities. So it is advised that businesses should establish objectives for their analysis pipeline and list the requirement of information.

It is equally important to know how the raw data will look upfront. It is really frustrating when unexpected surprises arise during preprocessing when you realize that you need to make another exception or a parsing function to deal with the fluke in the dataset. To negate this situation it is recommended that companies should carry out a small reconnaissance analysis of the data and list the possible anomalies and data types — and then plan strategies accordingly.

2. Establishing quality criteria
Once all the objectives are set for data processing, you will now need to ensure that you’re going the right way with data cleansing. It can be done by creating data quality key performance indicators (KPIs).

This will help you in developing the further steps in your strategy. The focus should be on how to meet with these strategies, track the health of the data and maintain healthy data on an ongoing basis.

At this point, you should be aware of where most of the data errors occur. It is vital to identify faulty data and then take the necessary steps, like understanding the root cause of data issues in your organization. This helps your data team to develop a plan for ensuring the health of your data.

3. Developing a workflow
Data cleaning is a complex process and needs a robust and well-designed workflow. One of the best practices to create a workflow is to compartmentalize your workflow into independent blocks, each with its set of individual functionality.

To help you understand better, here’s an example:

Step 1: Get raw data from a query to a data warehouse.

Step 2: Conduct basic alterations on the data like string cleaning, recording of categoricals, and other simple cleanup tasks.

Step 3: Use a first-level aggregation function to aggregate data and perform more transformations at that level.

Step 4: Start a higher-level aggregation where you take data from the first-level aggregate and aggregate it to a higher level, perform some conversions and return data.

4. Standardizing data
Another important data cleaning procedure is standardization. Standardization is basically a process where you create a protocol to follow the guidelines of how each field/column/parameter must look like (or what is expected). This is done so that you can comply with the data processing pipeline.

5. Validating data
When you are cleaning up the existing database, it is also advised to set up a real-time validation system This is where data experts come into play. Equipped with the right data cleaning tool they can clean and verify multiple data points.

To make the most of it, you need to create guidelines for data cleaning. Here are some example constraints businesses use when cleaning their existing databases:

Necessary constraints – required fields that can’t be left empty.
Data-type constraints – values in a column must be of a specific type (numeric, date, text, etc.)
Range constraints – minimum and maximum constraints placed on data.
Unique constraints – data that can’t be repeated and requires unique values (for example, social security numbers).
Set-membership constraints – data that must be chosen from a pre-existing list of options.
Regular expression patterns – applies to data that has a specific pattern in the way it’s displayed (for example, phone numbers).
Cross-field validation – when the sum of data parts must equal a whole.

6. Removing duplicate records
Duplicate records are another menacing factor for businesses. Businesses end up spending money on general maintenance and can cause reporting inaccuracies. Avoiding duplicates is important in the data cleaning process. To ensure that, businesses must need to validate the data and then scrub it to locate any duplicate records and erase them.

7. Combining data
After the data is standardized, validated, and scrubbed for duplicates, you’re ready to aggregate to. Here’s where you can hire data experts. Data experts can capture data directly from first-party sites, which is then cleaned and compiled to provide the information you can use in your business intelligence and analytics.

8. Reviewing the process
It is also important to keep track of the cleaning activities that you perform so that the process can be easily modified and repeated or remove specific activities that are not necessary. Various tools are used to monitor the actions and help us keep track of them easily for increasing the performance of your team.

Also Read: The 5 Most Common Types of Dirty Data (and how to clean them)

Conclusion
Keeping data clean is indeed challenging, especially if you are forced to make manual changes to data points. Look at the bigger picture before implementing your data cleaning strategy and define your objectives.

If you are looking for a team of data experts to help you clean and maintain your database? Get in touch with us for a no-obligation-free consultation with our experts.