You can hire the best sales and marketing talent and outfit them with state-of-the-art CRM and automation tools. You can even leverage AI to run hyper-efficient, personalized plays. But without high-quality data, those efforts always fall short.
As a data professional, I’ve seen this thread run through every stage of my career — from sales and sales management, to CRM consulting, and back to B2B data. If you’ve spent any time in this world, you’ve experienced it, too.
And you’ve probably received internal pushback from colleagues when you’ve advocated for tools and processes to combat bad data. These folks are far from the front lines, so they don’t fully understand the scope and depth of this issue — or the kind of damage it can wreak upon a GTM org.
They may have even said, How bad can it get?
If they need proof, share this post with them.
Here’s how bad data can wreck a company’s GTM efforts —with real examples of how data problems can cost big time — and how diligent pros can solve for it.
Defining Poor Data Quality
First, let’s level-set. When we talk about data in this article, we mean information about people and companies that you can use for B2B sales and marketing: contact information, business size, tech stack information, and a lot more.
Generally speaking, poor-quality data is inaccurate data in your CRM. These errors take several forms:
- Missing fields (phone numbers, emails, company revenue, industry, etc.)
- Outdated information (job titles, changes from mergers and acquisitions, etc.)
- Data entered in the wrong field
- Duplicate entries
- Misspellings, typos, and spelling variations
- Non-normalized data (inconsistent or unstandardized formats, like differing date formats or inconsistent company naming)
Here’s why things can go wrong in so many ways: Data decays the moment it’s collected.
B2B businesses operate faster than ever. Without continuously maintaining data to keep up with every change as it happens — from personnel changes to product offerings — your data becomes stagnant, quick. That means it’s inaccurate. Databases must be cleaned, maintained, and appended regularly to ensure GTM teams operate with the best possible data.
What’s the True Cost of Poor Data Quality?
According to Gartner’s estimates, dirty data costs companies an average of $15 million annually. Another study suggests that bad data may cost companies up to 25% of their potential revenue. It gets worse: In that same Gartner survey, they found that 60% of companies don’t even measure these costs, meaning they have no idea how much GTM waste they’re generating while using this bad data.
Could the news get worse still? Maybe. Every company I’ve talked to has had dirty data. And that means every company is wasting time, effort and money every day.
Breaking Down the True Costs of Bad Data
Dirty data disrupts every GTM motion, from lead-generation targeting and sales prospecting to lead nurture, account prioritization, and customer growth.
Naturally, clean data enables your GTM org to take focused action based on information that helps pinpoint opportunities. And the more complete and accurate the data, the more likely your marketing and sales efforts align with your target customers’ needs.
Here are seven ways dirty data can drain money and sanity:
1. Wasted Time
When a sales rep dials a wrong number or emails an outdated account, they’re wasting time that could’ve been spent selling. Sources estimate that SDRs waste an average of 27% of potential selling time following bad data.
These roadblocks reveal themselves in countless ways: Maybe it’s a target account list with incomplete buying committees, or contacts that are too low-level to make a buying decision. Maybe account records are incomplete or outdated, offering SDRs and AEs an inaccurate picture of the prospect’s challenges and current solutions.
This all adds up to a loss of valuable time in a market where efficiency is at a premium and reps can’t afford to miss sales goals.
2. Low Morale
When sales teams can’t hit their numbers and they know it’s because of bad data, they’ll find somewhere else to work, fast.
Left unchecked, your bad data problem will continue to wear away at top sellers’ morale until they leave. And word spreads: Just as sales professionals know which companies and teams have good product-market fit and ample runway, they’ll also find out where selling is not fruitful, tarnishing your employer brand.
You can’t bounce back from a radioactive reputation.
3. Wrecked Credibility
And speaking of reputation: Bad data leads to misspelled names, undelivered messages, account or contact mix-ups, duplicate communications, inaccurate cold calls, and more. It’s easy to rationalize these as minor errors or the cost of doing business. But at scale, they can severely impact your brand’s reputation with the market.
In the worst cases, your audience might experience several of these issues at once, leaving them with a negative impression of your brand. In the long run, this effect of poor data quality can even cause churn and result in lost deals due to a loss of trust.
4. Missed Opportunities
Bad data also causes companies to miss key buying signals like champion moves, funding rounds, changes to the buying group, and hiring plans. These oversights prevent sales and marketing teams from engaging with high-potential prospects at the right time.
If high-value signals aren’t paired with comprehensive, up-to-date B2B company and contact data, they’ll quickly become background noise that doesn’t help your team take action.
And when your team can’t react quickly to key signals — such as executive appointments or funding announcements — competitors can easily seize the opportunity first, eroding your market share, even if you have a superior offering.
5. Inaccurate Planning
Unreliable data can lead to inaccurate forecasts and flawed territory planning, which is especially costly during annual planning cycles or when launching new products and deploying new teams.
Put plainly, inaccurate data wildly misguides strategic directions before anyone can stop them. This not only frustrates sales reps who struggle to hit their targets, but is another factor that increases employee churn and erodes brand reputation in the marketplace.
6. Poor Email Deliverability
We all know email marketing is easy to use, cost-effective and impactful. Sellers also rely heavily on emails as part of crucial multi-threaded outreach.
However, leveraging bad data in your email lists increases the risk of higher bounce rates, spam reporting, spam traps, and other pitfalls that harm sender reputation.
Too many trips into spam traps or excessive numbers of bounced emails can get you blacklisted by your email service provider, which damages your organization’s sending reputation and can even get your email account suspended — all of which blocks your company from doing important business.
7. Wasted Budgets
Many GTM tools base their prices on how much data you store in their system. For example, a lot of marketing automation platforms are basically priced as email repositories that charge you based on the number of email addresses you have. If your data assets are filled with old, inaccurate emails, you’re paying more for your marketing automation tool than you need to.
Five Steps to Improve Data Quality
Even if you’re working with a trusted vendor like ZoomInfo and get the most up-to-date GTM information, you still have data flowing in that’s not coming from a trusted source: first-party web form fills, purchased lead lists from other vendors, and more.
Here are some critical steps that your team can and should take to improve and maintain the quality of your B2B data:
Step 1: Build a Data Governance Team
GTM teams often shy away from data governance or think it’s an IT function, but it needs to be owned by the business and driven cross-functionally so the right rules and processes are in place to serve a business’ overall needs.
IT absolutely plays an important role in data governance, but those colleagues don’t know the rules around territories or sales stages and which data points you need when, or how you segment for marketing. Therefore, your data squad needs representation from every function affected by data.
Step 2: Prioritize Critical Data Elements
Identify the data elements that are most critical for leads, contacts, accounts, opportunities, and billing, and focus your initial efforts on them first.
With proper prioritization, you’ll get those small wins that are important for continued buy-in. Start with the data your GTM relies on most, and work from there.
Step 3: Partner With Trusted Data Vendors
Cleaning the data you already have plays a big part in data governance and database maintenance. In many cases, this means calling in a vendor to help update and enrich your database.
Work closely with a reputable data provider like ZoomInfo to fix any gaps or inconsistencies in your sales and marketing contact database and ensure your data has the consistency needed for success.
And be sure to vet every provider on the accuracy, coverage, and consistency of their data. Reference our guide to evaluating global data providers to learn more about what you should look for in a data provider.
Step 4: Assess And Improve Data Collection
Examine every way data enters your business systems, and remain committed to continuously improving your data health.
- Enrich web forms: Web forms help you capture first-party data — your most valuable business data — but can also push bad data into your system. Use a tool like ZoomInfo’s FormComplete, which uses our reliable data to capture more data through streamlined forms.
- Assess the quality of second-party or third-party data sources: This data is often purchased and should be thoroughly vetted before being entered into your systems.
- Minimize manual data entry: Automation tools help reduce human errors and free up sales and marketing teams to focus on higher-value activities.
While no database is ever perfectly accurate, consistently cleaning, appending, and updating your data builds trust and reliability within your teams.
Step 5: Allow For Flexibility
Databases don’t exist in a vacuum, so data governance plans must strike a balance between flexibility and control.
While manual data entry can introduce errors, it’s often necessary for sales teams to have permissions to update key fields. To maintain data quality, use automated enrichment to manage most fields while appointing data stewards to oversee and correct any inconsistencies.
This approach requires more work behind the scenes but allows the flexibility your team needs to stay effective.
Data Quality’s Role in Successful AI Use
Businesses are rapidly jumping to adopt new AI tools and implement generative AI into GTM workflows. It’s important to remember that the data quality discussion is relevant here, too.
In fact, fulfilling the promise of AI rests on the quality of your data. AI learns and acts based on the data that you have, so if your data is wrong, the AI can’t help you — in fact, it can actively harm your business by driving poor decisions and misdirected actions at a massive scale, too fast for human quality control to catch.
Case Studies: Real-Life Examples of Bad Data Costs
Let’s explore two real-world examples that highlight the costly impact of bad data — and how one company managed to turn things around.
An Astonishing Loss, Caused by a Single Error
In 2018, a simple error in data entry ended up costing Samsung Securities nearly $200 million. Employees who were part of the South Korean company’s stock ownership scheme were set to receive a dividend of 1,000 won ($0.94) per share. But when an employee entered “won” instead of “shares,” the company deposited 2.8 billion shares — over 30 times the more than their existing shares — into those employee accounts. In the 37 minutes it took to fix the error, employees sold $187 million worth of the nonexistent shares.
Tradeshift’s Unusable Leads and Inefficiencies
Tradeshift, a leader in supply chain payments and marketplaces, helps buyers and suppliers digitize their trade transactions. Its large database contained many duplicates, which posed a compliance risk because it’s hard to manage compliance obligations like opt-outs when more than one record of a company exists.
On top of data duplication, Tradeshift also struggled with incomplete lead records from events or compiled lead lists. That meant the company lacked the data needed for critical GTM functions like segmenting, scoring, and prioritizing.
These lead-quality issues culminated in day-to-day frustrations and inefficiencies with the sales team — Tradeshift was simply buying leads that it couldn’t do anything with.
Thankfully, the story has a happy ending: Tradeshift used ZoomInfo Operations to cleanse, deduplicate, and enrich its data assets. With a new strategy backed by ZoomInfo’s industry-leading data, Tradeshift solved its compliance concerns and used resources 10 times more cost-effectively.
Drive Better Business Outcomes With High-Quality Data
High-quality data is the difference between driving optimal sales and marketing that connects with your audience and alienating potential customers.
So while investing in high-quality data has costs, the cost of having bad data running through your systems is much higher. Don’t simply look at the price tag. Consider the return on investment in data — and the time and money you could lose by making the wrong choice.