What is Data Quality? Common Issues and How to Fix Them

What is Data Quality? Common Issues and How to Fix Them

As a startup, data is one of your most valuable assets. It helps you understand your customers, track key metrics, and make informed decisions. But getting reliable, accurate insights from your data will be an uphill battle if your data is a mess. In this blog post, we’ll discuss some of the most common data issues SaaS startups face and suggest ways to fix them.

It’s important to note that while following these best practices will make it easier for your team to understand your data more clearly, the unfortunate reality is that your data will never be perfect. Set this expectation with your teams while stressing the value of intentionally configuring systems, paying off data quality debt consistently, and prioritizing based on business value.

Duplicate Data

This one shouldn’t surprise anyone working at a scrappy startup. Duplicate records can plague your systems if you don’t have proper controls.

Duplicate data is most often the result of a lack of unique identifiers. Make sure you’re referencing unique, system-generated IDs instead of user-submitted strings wherever possible, and create rules that prevent the creation of new duplicate records.

If you’re looking to clean up your CRM or marketing automation tools, check out Hubspot’s native dedupe feature or Dedupely for cleaning up tools like Salesforce, Pipedrive, and Mailchimp data.

If you see duplicates in your product data, ask your engineer about implementing unique key constraints.

Lack of Data Validation

Whether you’re creating surveys in Typeform or custom fields in your CRM, it’s crucial that you format and validate inputs consistently. Without validation, you’ll end up with unstructured data that becomes extremely difficult to report.

To prevent this from happening, make sure you document the data you want to capture and the format in which you’ll need that data in the short vs. long term. It always helps to share this doc with your team, who can help you identify use cases or dependencies you might have missed.

Also, try to structure your inputs using single-select options whenever possible, as it will save you from having to flatten multi-select submissions or parse free text submissions for reporting purposes.

As a general rule of thumb, you should automate as much as possible to limit error-prone human touchpoints, especially during data entry. Tools like Zapier can help non-technical people limit these manual touchpoints.

Data Inconsistencies between Tools

As your startup grows, you’ll inevitably collect data across dozens of SaaS tools. However, it can be difficult to get a complete picture of your business can be difficult if you don't integrate these tools well.

Once you’ve sufficiently fixed data quality inputs in your respective SaaS tools, you should look to centralize data from your tools using a data integration tool. Once centralized, you can then combine data using universal identifiers.

Traditionally, you can do this by building your own data stack, which consists of

  1. A data warehouse like Snowflake, BigQuery, or Redshift to store your data for reporting purposes
  2. An Extract and Load tool like Fivetran or Stytch to send data from your SaaS tools to your data warehouse
  3. A Transformation tool like dbt to apply your unique business logic to raw data so that your teams can reliably explore data models and metrics

If you don’t have the time or technical know-how to build and manage your own data stack, Canvas helps you combine, explore, and present data from your SaaS tools and database without code, for a fraction of the cost of hiring a data team or consultants.

Poorly Documented Data

Even if you’ve cleaned and centralized your data, your team members might still be unable to analyze it without your help. For example, they might not know how metrics are calculated, where the inputs are coming from, or even what a particular column name means.

At a minimum, you’ll need a standardized way to communicate data questions and issues with your teams via a Slack channel or issue-tracking project. As you start to see repetitive themes emerge, try to document FAQs or create a data catalog that documents your most important tables and columns.

These options typically aren’t great solutions, as your teams likely won’t remember to refer to these docs when exploring data. If you still see repetitive questions, consider embedding table and column descriptions directly in the analysis tool. Some BI tools will natively offer this functionality, while others will integrate with popular data transformation and documentation tools like dbt or with data catalog tools like Atlan.

Improve your data quality with Canvas

You’re not alone if you suffer from any of these data quality issues, but following these best practices will help your team analyze and report more confidently.

If you still need help getting your data house in order, Canvas can centralize and model data from your SaaS tools and database without SQL or engineering.

We provide templates for common startup operational use cases (ARR cohorts, LTV/CAC, NPS, Churn), can model your custom business logic, and clean up your data quality issues.

Get started by signing up for a 14-day free trial or email me at [email protected].