The most recurring issue in the data community is inaccurate data. When data is not accurate, users are less likely to trust it — meaning no one will use it in decision-making 😟. But what, exactly, does inaccurate data look like? It is data that contains errors — whether the information is outdated, duplicated, or even nonexistent in some cases.
To improve the data quality within your organization, practicing data hygiene is a must, as the sheer volume of data across organizations increases over time 👈. This guide will bolster your understanding of data hygiene and provide you with some best practices to follow when implementing data hygiene across your organization.
What is data hygiene?
Data hygiene is the process of maintaining and cleaning your data to ensure that your organization is working with accurate and complete data.
What do we mean when we say “clean” data? We are referring to data that, for the most part, is error-free. Cleaning your data can be as simple as removing duplicates from your database and ensuring data is in a standardized format across the board 🧽.
A variety of factors can lead to your organization working with data that contains errors. It is quite common for data quality errors to occur at any stage in the data life cycle, which is why your organization needs to maintain its data hygiene to improve the quality of data.
Why does data hygiene matter?
No one likes working with poor-quality data. The continuous use of poor-quality data leads to bad decision-making down the line because users don’t trust it. Over time, poor-quality data costs your organization time and money — costing businesses in the U.S. more than $3 trillion per year, and data workers have to use 51% of their precious time collecting, labeling cleaning, and organizing data 😤.
One of our clients, Chameleon, experienced firsthand the consequences of working with poor-quality data. Before coming to Iteratively, Chameleon lacked a single source of truth for its event tracking. They were utilizing Google Sheets to manage their tracking plan, but the sheet was not kept up to date, and there was no way of ensuring that tracking was implemented accurately in the product. Because of this, data became messy to work with, trust eroded over time, and the team could not rely on data for decision-making.
– Pulkit Agrawal, Co-Founder and CEO at Chameleon
Nowadays, you can’t afford to rely on data that is only 90% accurate, as data is most companies most valuable business asset and differentiates them from their competitors.
Good data hygiene practices often lead to working with higher-quality data. With that said, let’s dive into some best practices for data hygiene that your organization can implement today 🤿.
5 best practices to prioritize data hygiene in your organization
Implementation of data hygiene in your organization will differ depending on your company's size, the resources available to your data team, and your company's culture around data. However, the best practices below apply to any company, regardless of its size or industry.
1. Perform an audit
Before getting started with data hygiene, it is best to complete an audit of your systems. During the audit, you should evaluate all the systems your company uses when dealing with customer information. When assessing each system, you should determine which data sets are necessary for your business and which ones are not. We also recommend mapping out your data dependencies, so you know which systems downstream will be impacted by a change.
To cut down on unnecessary data, you should evaluate your input fields to ensure they lead toward collecting relevant information for your business.
2. Prioritize data based on its value to the business
Cleaning up your data sets can be a lengthy process, especially when working with a high volume of data flowing in from a variety of sources. When most organizations first get started with data cleansing, they are usually unsure of where to start — especially since it can feel a little overwhelming at times 😭.
When cleaning your data, it’s best to start with data that is most valuable for your business. For example, a company in the ecommerce industry might start with cleaning up their customer email list, removing duplicates, and determining if the email address is real or fake. Typically, the more valuable the data set is to your organization, the higher it should be prioritized when you start cleaning up your data.
3. Create a culture where data hygiene is a priority
Data hygiene is a must rather than a nice-to-have when dealing with data. Customers expect you to have updated information on them and personalized experiences when you’re working with them. That is why data hygiene is a collaborative effort and requires input from everyone in the organization. From salespeople who collect data on clients to your chief financial officer — everyone should be on board to make sure data is up to date.
To create a data hygiene culture, it is best to assign someone in your organization priority over the cleanliness of data. That way, someone is responsible for data hygiene and can help develop a data quality plan for your organization.
4. Create a uniform template for data entry
The point where data enters your customer relationship management (CRM) system is usually the first cause of data that contains errors. To ensure that data entering your CRM is high quality, it is recommended that you check data on the client side to make sure that all information is standardized in a consumable format.
When creating a uniform template for data entry, you should create a standard operating procedure. This will help your team establish consistency when cleaning data and, over time, catch data quality issues at the source, preventing those errors from entering production.
5. Validate the accuracy of your behavioral data
Validating your data's accuracy will aid your organization in ensuring that your data is accurate and complete. However, some data teams struggle with data validation as it's often deprioritized or not easy to implement due to lack of tooling and processes.
To aid your data hygiene process, we recommend taking a proactive approach to data validation and following these data validation techniques at each step of the data pipeline 👈.
Proactively validating your data ensures that your behavioral data is accurate, complete, useful, clean, and understood throughout the organization (Iteratively is great at helping you with this 🤩).
Data quality matters
Over time, good data hygiene practices will result in high-quality data your teams can lean on to make strategic business decisions.
Following these best practices can ensure that you provide useful and accurate insights on your customers to stakeholders.
“Since adopting Iteratively, we’ve been able to leverage our analytics much more because of the improvement in the quality of our data.”
– Pulkit Agrawal, Co-Founder and CEO at Chameleon
Iteratively can play a part in supporting your company’s journey to improving its data quality. If you are interested in trying out Iteratively, create an account today, or book a demo with our team to learn more.