Good Guys vs. Bad Data: How to Be a Data Quality Hero

Duplicate contact and account records. Missing field values. Inaccurate or outdated information. Unstandardized data. Typos upon typos. If you manage and operate sales and marketing tools, you’ve likely encountered these bad data scenarios (and others!).

We get it — data quality isn’t the sexiest topic. But the impact of poor data quality is undeniable, causing 21% of marketing budgets to be wasted, according to research from Forrester and Marketing Evolution. Furthermore, factors like increasing competition and evolving buyer needs continue to make data health even more important. Improving data quality can stretch your marketing dollars further, enable operational efficiency, and act as a strategic growth lever.

We hosted a webinar with Tim Liu (Head of Product at Hull) and Brad Smith (Co-founder & CEO at Sonar), who have spent their entire careers working in data integration and operations. In short, they’ve seen it all. In this webinar, they discussed:

  • Data quality nightmares they’ve personally dealt with
  • Common scenarios where bad data can rear its ugly head
  • Proactive strategies for getting ahead

Watch the replay or read on for the recap!

Watch the replay and view the slides

What do we mean by "bad data"?

Bad data can take on many different forms in sales and marketing. Common examples include:

  • Incomplete contact and account profiles
  • Unstandardized data
  • Duplicate data records
  • Typos
  • Legacy fields (that have not been properly sunsetted)
  • Data flows gone wrong
  • Merging records incorrectly

To hear Brad and Tim talk about these scenarios, skip to timestamp 11:55 in the replay.

The downstream impacts of working with bad data

Everyone understands that working with bad data can yield suboptimal results. Sometimes the consequences can be minimal and have very little impact, but other times, they can be a lot more visible and cause serious issues. A 2019 research study by Forrester and Marketing Evolution found that as much as 21% of marketing budgets were wasted as a result of bad data.

In addition to wasted spend, some other downstream impacts that Tim and Brad discussed on the webinar include:

  • Inaccurate or suboptimal targeting
  • Missed sales opportunities
  • Loss of customers
  • Reduced productivity
  • Poor customer experience
  • Inaccurate performance reporting
  • “Death by a thousand cuts”

Jump to timestamp 15:08 to hear more from Brad and Tim on this topic.

Proven strategies

Brad and Tim then dove into four strategies that they've deployed with customers when working towards clean data. Those strategies are:

1. Begin with the end in mind (26:27)

Any ops project, related to data or not, can be subject to scope creep and external factors that can extend timelines or worse, derail its success. It's important to keep in mind what the ultimate goals are, plan ahead to mitigate risk upfront, try not to boil the ocean all at once, but also...give yourself some grace and acknowledge that unknown complexities and may occur!

2. Gain visibility (31:29)

Brad and Tim talk through two tactics to help gain better visibility of your data:

3. Architect and execute (35:24)

In this section of the webinar, Tim shares some specific considerations to keep in mind when deploying a long-term solution for improving data quality:

  • Your identity resolution strategy
  • Your data model
  • How to cleanse and standardize your data
  • Promoting consistency for long-term success
  • Dealing with edge cases

4. Measure and maintain (48:28)

After all is said and done, and your data is finally in a good place, you're good to go, right? Not so fast! Data is a depreciating asset, so while it's tempting to sit back, kick your feet up, and think you're finished, there's more work to be done to make sure you actually are where you want to be and you can maintain good data going forward.

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Angela Sun

Angela brings over a decade of B2B technology marketing experience to her role as the Director of Marketing at Hull. Prior to Hull, she spent 5 years at utility data aggregator, Urjanet, where she held various roles in demand generation, marketing operations, and product marketing.