Companies Waste Millions on Low-Quality Data. Here’s How to Stop It

For today’s sales and marketing teams, turning raw data into actionable insights is the key to unlocking high performance. And that competitive edge only grows larger as customer expectations evolve and go-to-market strategies become more complex.

The first step, however, is making sure your data is reliable — a job that requires continuous cleaning and correcting. Scott Taylor, a consultant and author known as the Data Whisperer, puts it this way: “Good decisions made on bad data are just bad decisions you don’t know about yet.”

For modern data teams, the answer lies in enriching their databases with the most reliable sources, and preventing future errors from entering the system in the first place. Here’s how to ensure your go-to-market teams have high-quality, reliable data that sets them apart from the competition.

The Three I’s of Bad Data Quality

Bad data causes big problems, and those problems cost businesses a lot of money. According to Gartner, the missed opportunities and added technical hurdles of low data quality cost an average company nearly $15 million per year. What’s more, data scientists spend up to 40% of their time manually cleaning datasets.

To get the most out of critical business data, people and processes must be able to act on it quickly. But it’s impossible to transform data into actionable insights when three quality problems are present:

  1. Incomplete Data: Company, contact, or behavioral information is missing.
  2. Inaccurate Data: Information is wrong or outdated, including inconsistent, unstructured, or non-standardized data.
  3. Ineffective Data: Ineffective or non-actionable data, including inconsistent, missing, or siloed data points across records.

The Recipe for Better GTM Data Orchestration

Data quality is based on the percentage of records that are correctly matched. By multiplying the characteristics of data quality (match rate, fill rate, match confidence, and fill confidence) you can calculate a dataset’s reliability rating, or how trustworthy it is for performing consistently well.

Data quality characteristics formula.

“You don’t want a bunch of missing, poor, or inaccurate information — it leads to low match rates,” says John Kosturos, senior vice president of global partnerships at ZoomInfo. “You want the most accurate information across industries and fields to get the highest fill rates.”

There are three clear steps to improving GTM data quality: 

1. Cleanse: A data normalization (or standardization) process is necessary for data that enters a system through various touchpoints. 

This process groups similar values into one common value for more streamlined processing, distributing, and analyzing. Data normalization is necessary because it ensures semantic consistency across your GTM systems. 

2. Enrich: Enriching data ensures that each field has the best, most reliable information available, and expands the information you can access, which enables more sophisticated segmentation. 

Multi-vendor enrichment enables you to source thousands of data points from a variety of third-party vendors. No data provider has the perfect database, so consider sourcing from multiple data providers to build a single source of truth. 

3. Prevent: Frameworks like “1–10–100 rule” explain that it costs 10 times more to correct an error than prevent one, and 100 times more if an error is not fixed. This adage illustrates the need to prevent data errors from the beginning. 

Proper data orchestration can automatically merge or convert leads, contacts, and accounts based on matching rules that you define at the source of the original data itself. 

More Sophisticated, Targeted Sales and Marketing with Multi-Vendor Enrichment

At the enterprise level, organizations need access to many data sources to perform the targeted sales and marketing activities that drive revenue and growth. In fact, the typical organization uses over a dozen different data sources. 

Onboarding this amount of data is a complex, time-consuming operation, in part because vendors sell their data in varying formats, each with their own taxonomies. Multi-vendor enrichment is a critical element for larger data quality management strategies to handle this variation. It enables organizations to easily find, integrate, and orchestrate data from multiple third-party data providers into Salesforce and other go-to-market tools. 

Through flexible rules-based logic, multi-vendor enrichment ensures that data is standardized and segmented according to a group’s unique business requirements. Organizations can complement ZoomInfo data and other third-party vendor data with more niche data sets, to enable far more granular, targeted audience building to cover every inch of their TAM.

Infographic showing the process of multi-vendor data enrichment

Integrated Data Quality Management  

Most data quality management solutions require multiple tools to clean, normalize, transfer, enrich, and match data — each one adding complexity to the process. A centralized solution decreases the need for multiple, disparate tools, and simplifies the effort required to maintain reliable go-to-market data. 

ZoomInfo OperationsOS delivers an integrated data quality management system that eliminates the time-consuming and expensive task of manually managing multiple data sets for cleansing, enrichment, and activation. 

With multi-vendor enrichment, revenue operations teams can build engagement-ready data with the click of a button, rather than hours spent in spreadsheets. Whether through our UI-based enrichment tools or a more tailored option, ZoomInfo provides seamless and complete data enrichment capabilities that curate the world’s best business data for your team.