Table of Contents
  • Low-quality analytics: Poor data quality may result in false positives. Good data hygiene ensures faster, more accurate insights. 
  • Reduced productivity: All departments rely on quality data. If the data is incomplete or inaccurate, it may affect productivity.
  • Cost overheads: Storing and processing data costs money. Eliminating low-quality data will reduce your bill.  
  • Customer frustration: Bad customer data can lead to reputation-damaging errors. By following data hygiene best practices, you'll be able to provide a consistent level of service.  
  • Trouble scaling up: Data grows exponentially over time, which means that problems associated with data will also grow exponentially. Good data hygiene practices will allow you to scale up without any problems.   
  • Compliance issues: Data hygiene will help you stay compliant with international data protection laws, such as GDPR and CCPA.

When dealing with Big Data, bigger isn't always better. Yes, volume is important, but your analytics efforts will fall apart if your data quality isn't good enough.

You can save your company time—and save your analytics team a lot of frustration—by applying data hygiene best practices early in the process. Let's take a look at what that means and what implementation looks like.  

  1. What is Data Hygiene?
  2. 8 Data Hygiene Best Practices to Live By
  3. The Secret of Data Hygiene

What is Data Hygiene?

Data hygiene describes all of the processes you have in place to ensure consistently high-quality data. Like other forms of hygiene, you see the best results if you plan ahead. The right tools and protocols will cut down on the data cleansing work required further down the line.

Hygiene is what you do; cleanliness is the result. Restaurants have strict kitchen hygiene protocols, with checklists about when to wash, scrub or disinfect things. The result is a clean kitchen that never fails health inspections.

Data hygiene best practices have the same effect on your data workflows. By putting the right data scrubbing rules in place, you can eliminate any corrupt, inaccurate, and poor-quality information. The result of data hygiene is a consistently high level of data cleanliness.

Why Is Data Cleansing Important?

Data cleansing is probably the least favorite task of any data scientist. It's tedious, and it typically occupies around 60 percent of their time.

But dirty data can create all kinds of problems for companies, like:  

Bottom line: Data quality impacts pretty much everything. By eliminating dirty data, you make life easier for everyone.

8 Data Hygiene Best Practices to Live By

Data hygiene is all about best practices. So, which practices should you follow?

1) Get Rid of Useless or Bad Data

In most data sets, you'll find low-hanging fruit that's ripe for a data scrub. Examples of this include:

 Corrupted data that you can't repair

  • Blank or null cells
  • Relational database rows that lack primary or foreign key information
  • Outdated data
  • Anything manually marked for deletion
  • Non-compliant PII, such as customer data or employee contact information 

If one of your data sources regularly produces bad data, you may need to look downstream and see if there's an issue. For example, some APIs may have problems with character encoding, which can lead to corrupt cells and potential data loss.

2) Consolidate Duplicate Data

In the event of duplicate records, you have three options:

  • Flag as duplicates
  • Merge both items into one
  • Delete one copy and leave the other intact 

Your choice depends on the nature of the duplication. Sometimes, a system error may create a duplicate, in which case you can simply delete one version.

Duplicate records could be the result of two different processes. If so, then deleting one could create an issue. This is why it's important to track data lineage and to understand where all data comes from. Good data management practices can help to reduce data scrubbing work in the future. 

3) Identify & Scrub Structural Errors 

Duplicate data can indicate a structural database error. Relational databases should be fully normalized, which means creating a table and key structure that avoid duplication.

Other common errors include:

  • Poor validation: The data entry tool isn't applying the correct rules. For example, a webform might allow users to enter letters in a date field.
  • Bad table structure: The underlying database might have structural issues, which could impact data quality
  • Mis-configured integration: Problems often arise when data is flowing from one place to another. If you haven't configured the integration correctly, you may see poor-quality data leaking through.
  • Incorrect format: Another integration issue, this is when two systems use different formats. For example, System A might delimit values with a comma, while System B uses semicolons.
  • Wrong encoding: Character encoding can cause issues when moving from one system to another. Often, this error may not be apparent until you use special characters. 

If you discover problems like this, it's a data hygiene best practice to fix them at source and ensure error-free data in the future. 

4) Identify What Data is Missing

Missing data can be as troublesome as inaccurate data. As part of your validation process, check which values are empty and try to fill them. There are several ways you can do this, including:

  • Flag: You can flag up missing values. The analytics algorithm will distinguish between missing data and a null value.  
  • Impute: You may be able to infer some data from other values. For example, with partial contact data, you could work out a customer's area code from their zip code.
  • Drop: If missing values are likely to cause errors, it may be better to drop the record entirely.

If you’re missing a substantial amount of data, you may need to look at how you gather information. For example, if data originates from a web form, then you can change which fields are mandatory.

5) Standardize Your Data

You should aim to use a standard format for data where possible. This might involve things like capitalizing all strings in one column, converting phone numbers to international formats, or applying a MM/DD/YYYY format to all dates.

Some standardization actions may require the use of a lookup table. State names are one example: you can create a lookup table of common state abbreviations so that CA, Cali, and Calif are all replaced with California. 

Other types of standardization involve a degree of computation. For instance, you may need to convert currencies or temperatures to the local values. This isn't difficult as long as your raw data is correctly tagged. So, for example, if you're converting to Fahrenheit, you have to be sure that the original value is Celsius.

6) Validate Your Data Cleansing

Perform a trial run on a subsection of data and make sure that the outputs are as expected. Things to watch out for include:

  • Duplicate data
  • Missing data
  • Corrupted values
  • Correct data type in all fields
  • Incorrect data values
  • Values outside the constraints of the field
  • All primary key and foreign key relationships intact
  • Outputs from BI and analytics tools pass a sense check

7) Follow Standard Practices When Adding New Data

You’ll need to look ahead and think about how to scale up your data hygiene practices over time. Usually, this means that you’ll need to automate data standardization using the right tools. A data pipeline powered by ETL (Extract, Transform, Load) will apply consistency to data transactions such as warehousing, even as your data volumes increase over time.  

If you get these things right, then you won’t have to worry about quality during the data lifecycle Instead, you can rely on clean data for analytics, and stable B2B data for your marketing automation platform.

8) Use Third-Party Tools to Integrate Your Data

Cloud-based ETL platforms such as Integrate.io can help you to apply data hygiene best practices at scale. In the ETL model, all data passes through a transformation layer prior to ingestion. This transformation layer gives you a chance to remove corrupt or duplicate values, to flag up missing values, and to integrate with data from other sources.

ETL acts as a kind of filter that protects your data warehouse from potential contamination. You can configure your data stack so that everything passes through ETL, which means that only high-quality data reaches your warehouse. The result is consistently clean big data, which enables the deliverability or real-time analytics, and eliminates time-consuming manual work for your team.

The Secret of Data Hygiene

It's impossible to manage data hygiene manually, especially at scale. People and processes are fundamental to getting it right, but ultimately you need automated tools to get things consistently right.

Want to see how Integrate.io can keep your data squeaky clean? Contact us to set up a demo and a free trial of the Integrate.io platform.