Data Hygiene: Best practices, FAQs, and How to Improve Database Hygiene

By 2025, it’s estimated that the total data volume of connected devices worldwide will reach 79.4 zettabytes. And the more data businesses have to process, the more difficult it becomes to manage and ensure its accuracy. This is why you need to have the proper protocols in place to protect data hygiene. 

Table of contents:

• What is data hygiene?

• Date hygiene benefits

• Best practices for database hygiene

• How to clean and create a shared data dictionary with Twilio Segment

• FAQs on data hygiene

What is data hygiene?

Data hygiene is the ongoing process of cleaning the data you collect to maintain its integrity and accuracy (e.g., removing duplicate entries, standardizing naming conventions, etc.).

Why is data hygiene important?

Data hygiene ensures your data is accurate. Without effective data hygiene procedures, you’ll be working with dirty data that impairs your organization’s ability to make strategic and well-informed decisions. 

Data decay is the gradual process of data losing its value, either by being lost entirely (e.g., accidentally deleted) or as a result of the data entry becoming outdated and irrelevant. And data decays at a rate of about 30% each year for the average business.

We know that data is what fuels top-tier customer experiences, product development, strategizing, machine learning – you name it. So, it makes sense that businesses should be placing a huge priority on protecting the integrity of their data, rather than allowing one-third of it to become essentially useless every year. 

Data hygiene is also integral when it comes to adhering to privacy standards and protecting customer data – something that’s not only important from an ethical standpoint, but a legal one as well.

5 benefits of proper data hygiene 

Data hygiene helps you make accurate, data-driven decisions that promote everything from increased revenue to stronger customer satisfaction rates. We’ve listed a few benefits below.

1. Greater success with lead generation 

Leveraging accurate data is the key to creating better customer experiences that convert. It’s also essential for boosting your ROI. For example, say a marketing team wants to run an email campaign for recent cart abandoners, but their audience list includes email addresses that have been deactivated or misspelled. The likelihood of making a sale in those instances drops to zero. 

Or, on the flip side, say the marketing team reaches a customer who recently bought the product they were trying to promote. That’s also money down the drain, in trying to convert a customer they’ve already won. 

2. Faster lead tracking

Personalizing interactions with prospective customers based on their funnel stage is a tried and true way of ushering users through the funnel (and closing a deal). But if you’re working with outdated data, it can be impossible to precisely target these communications. Data hygiene ensures that you understand where a person currently is in the funnel, what information they need to move forward, their preferred channel of communicating, and more. 

Not to mention, with accurate, up-to-date data, you could even automate some of these interactions to nurture leads at scale. 

3. Secure data

Another aspect of data hygiene is security. That is, how are you protecting customer data both internally (e.g., blocking widespread access to personally identifiable information) and externally (e.g., avoiding a data breach).  

Some security measures that we take at Segment include: 

• Data encryption at rest and protected by TLS (Transport Layer Security) in transit 

• Time-bound access to critical tools

• Controlled access to Segment Sources and Workspaces with user-based permissions

4. Accurate personalization 

Personalization and ROI go hand-in-hand – nearly half of customers said they’d make a repeat purchase after experiencing a personalized shopping experience with a retailer.

But when data is inaccurate, personalizing the customer experience devolves into a game of chance. With access to real-time data, businesses can track customer journeys as they unfold and initiate highly tailored interactions (and even do this at scale with the help of automation). 

5. Revenue protection

According to Gartner, bad data costs organizations an average of $12.9 million each year. Data hygiene helps prevent revenue losses from misguided decisions as a result of skewed and inaccurate data reporting. 

It also helps teams become more precise in their campaign planning and audience lists, meaning money isn’t thrown down the drain by trying to convert customers who aren’t interested.

Best practices for data hygiene 

Want to get it right when it comes to data hygiene? We’ve listed some best practices below. 

1. Audit your existing data

A data audit involves evaluating your organization’s data assets, systems, and sources to learn whether the data is complete, accurate, and secure. 

Check for duplicate records, spelling mistakes, multiple naming conventions, and other errors that could disrupt your operations, analyses, or campaign performance.

2. Standardize naming conventions

Standardizing naming conventions helps ensure that data entries are uniform, and that the same event isn’t being counted twice (or multiple times). Having these uniform naming conventions in place can also help businesses automatically block events that don’t adhere to their tracking plan, which helps protect data quality at scale. 

3. Understand data lifecycles

The data lifecycle refers to the journey a unit of data undergoes from its initial collection to its eventual storage or deletion. Understanding how data is collected, processed, and stored at your company is essential for maintaining data hygiene. For one, it prevents silos from cropping up and causing fragmentation across your data sets. Second, it helps ensure data security by understanding who is able to access what data (e.g., preventing a leak in personally identifiable information), and how that data is protected at rest. 

Data mapping can be helpful for understanding the data lifecycle. Here's a guide on how to do it.

4. Choose the right analytics database

An analytics database is a data management platform that stores and organizes data. It specializes in scalability and quickly returning queries, and is usually part of a broader data warehouse or data lake. An analytics database gives you the ability to quickly analyze large volumes of data and easily spot issues or trends at a faster rate than combing through manually.

Clean and create a shared data dictionary with Twilio Segment

A customer data platform (CDP) like Twilio Segment helps you collect, clean, consolidate and protect your data at scale.

Using Protocols, businesses can create a shared data dictionary that’s automatically enforced to protect data integrity. It helps establish a universal tracking plan, standard naming conventions, automated QA checks, and more. 

Replace spreadsheets with tracking plans

A tracking plan in Protocols outlines the events and properties you want to collect. This helps establish a single source of truth within the organization, and create internal alignment.

This tracking plan template is useful if you don’t want to create your own from scratch or just need some ideas on where to start.

Integrate with APIs & Typewriter

These tools reduce implementation errors by generating Segment analytics libraries based on your tracking plan.

Application programming interfaces (APIs) help you manage your Segment workspaces and the resources that come with them. Typewriter takes an event from your tracking plan and uses it to generate a typed analytics call in different languages. This reduces or entirely eliminates incorrect instrumentations in your production environments. 

The more extensible documentation you have, the more it can be used to improve business strategies. 

Automate data validation

With Protocols’ automatic data validation, you can quickly audit your implementation and cut down on missed inaccuracies. Automated alerts and reports help you diagnose data quality issues.

Human error is inevitable when manually validating information, but it’s often too late when the mistake is realized. Protocols detects mistakes before they impact production or other strategies.