It’s a problem that not everyone talks about, but that everyone should: poor data quality. Why? The impact of poor data quality is significant — in fact, Forrester reported that 21% of marketing budgets are wasted by bad data. What’s more, factors like increasing competition and evolving buyer needs continue to make data health even more important. 

Conversely, improved data quality boasts serious benefits, including stretching your marketing dollars further, enabling operational efficiency, and acting as a strategic growth lever. 

These are just a few of the reasons why our co-founder, Brad Smith, and Tim Liu, Head of Product at Hull, co-hosted a webinar on November 17 entitled Good Guys vs. Bad Data: How to Be a Data Quality Hero. Throughout the webinar, Brad and Tim (who, collectively, have spent their entire careers working in data integration and operations) covered everything from the data quality nightmares they’ve personally dealt with to proactive strategies for maintaining healthy data. 

Didn’t make it to the webinar? Here’s a recap of what you missed and how you, too, can become a data quality hero. 

First, what do we mean by “bad data?”

Bad data is any type of data that contains errors or inaccuracies. Some examples of bad data include: incomplete contact and account profiles, unstandardized data, duplicate data records, typos, legacy fields that you’ve inherited, data flows gone wrong, and records that were merged incorrectly.

“There are a lot of issues that we’re pretty used to dealing with…duplicate or unstandardized data, typos, legacy fields,” Tim explained, “and some of those issues are more challenging than others. But you’re not alone. We see these issues constantly throughout the industry.”

The Downstream Impact of Bad Data

We use data every single day to make business decisions, so unsurprisingly, bad data can have a whole slew of negative impacts that affect everything from targeting to performance reporting.

“This is where we start to feel the ‘people pain’ and the real impact of bad data,” Brad said. “Real people use this data to make business decisions, and we experience the consequences — the pain — that it causes.”

Some of the negative impacts of bad data include:

  • Inaccurate or suboptimal targeting
  • Missed sales opportunities
  • Loss of customers
  • Reduced productivity
  • Poor customer experience
  • Inaccurate performance reporting

All of these downstream consequences boil down to one major impact for businesses: loss of money.

“People are recognizing more and more that bad data wastes money,” said Tim. “Whether it’s retargeting budgets or prospecting budgets, bad data can really impact your bottom line.” 

When Bad Data Strikes

Where, when, and how does bad data strike? More often than not, it boils down to a misalignment of people, processes, and technology. You could, for example, find yourself battling it out with bad data when departments merge or expand — particularly during restructuring or mergers and acquisitions. But bad data doesn’t just happen from big events. Other common scenarios that are breeding grounds for bad data include:  

  • Executing too quickly on growth initiatives, i.e., with new funding rounds
  • Onboarding new tech and data sources
  • The status quo isn’t working anymore

“One of the best examples I can think of was an acquisition that occurred while I was working in a previous role,” Brad shared. “I was a one-person operations shop managing a complicated tech stack, and we acquired another tech stack…and there are business decisions to be made there. I had to consider the data quality and decide which system of record to use, and which one had better data quality.”

Proven Strategies for Getting Bad Data Under Control

Even if your data quality needs help, it doesn’t mean it’s a lost cause. There are, in fact, proven strategies for mitigating bad data. Here are a few that Brad and Tim covered:

  1. Begin with the end in mind. You can’t solve everything all at once, so start by figuring out where you want to go. Determine your ideal outcomes, but also acknowledge the unknown complexity and try to minimize risks upfront. Planning ahead can go a long way when minimizing risk.
  2. Gain visibility into your data. Putting all of your data in one place can provide valuable insight. Customer data platforms are designed to unify and centralize, so use them to your advantage to identify duplicate data and any gaps that need filling.  And tools like Sonar help you understand the dependencies between your data, so you can easily fill gaps without breaking things across your systems.
  3. Architect and execute your strategy. Identity resolution is the process of taking the data points that define an entity from different online or offline systems and merge them to create a single, consolidated, and consistent record of that individual or company. Through that process, you can build a data model that defines how entities relate to one another, i.e., Leads and Contacts associated with Accounts. From there, data transformation tools, like the Hull Processor, can help with data cleansing and standardization.
  4. Measure and maintain success. Define what success looks like for your company, paying attention to holistic revenue goals. Be mindful about setting aside vanity metrics, and know what data points you need to be collecting in order to gauge momentum and progress towards your goals.

Want the full recap? Watch the webinar recording here.