Bridging the Gap Between First and Third-Party Data in Your Cloud Data Warehouse

Go-to-market (GTM) tech stacks are complicated — and for good reason. Modern sales and marketing teams need various tools to help them do their jobs. With the right tools you can create complete, 360-degree customer views that make running targeted campaigns and personalized sales motions easier. 

Unfortunately, problems arise when too many apps produce, use, and store disparate data points on the same leads and accounts. The result? A sea of duplicate records sprawled across your tech stack, disrupting the chance of a smooth, customer-centered sales journey.

Centrally managing multiple data sources — both first- and third-party — is necessary to build a results-driven GTM strategy. It’s also critical that data administrators optimize their data foundation, to be able to serve up useful data intelligence. It all begins with fixing, centralizing, and adding an intelligence layer to manage your dirty, conflicting data. 

The What and How of Dirty Data

Dirty data is like an unpolished gemstone. It’s valuable — but it needs precise cleaning, cutting, and polishing to really unlock its potential.

Dirty data is faulty, disjointed information that’s inconsistent, outdated, missing entries, full of duplicates, and often siloed in different applications. Today, about 54% of B2B businesses say poor data quality is their biggest challenge. 

So what’s the solution? It starts with unearthing the four common roadblocks that stand in the way of creating a centralized GTM data warehouse.  

The Four Challenges to Building a Unified Cloud Data Warehouse

Building a truly functional centralized data warehouse for creating a complete customer view doesn’t happen without first solving a few common challenges that stand in the way. These are:  

  • Cleaning dirty data
  • Unifying data
  • Applying data intelligence
  • Operationalizing cloud data

Steps to Building a Unified Cloud Data Warehouse

Building a unified cloud data warehouse starts with focusing on the most important component — the data. Here are the steps involved in creating a centralized data warehouse for GTM success:

1. Augment Your First-Party Data

Your first-party data isn’t enough for executing actionable insights. While data warehouse providers cover the infrastructure needs of a serverless repository of expanded data, they often don’t solve incorrect and inconsistent data problems. According to Gartner, companies estimate they lose on average about $13 million per year because of bad data. 

With ZoomInfo’s recent partnerships with Snowflake, Amazon Web Services, and Google BigQuery, getting access to reliable third-party B2B data becomes a streamlined process. Data is readily provided within a data management system that enables teams to access, organize, and parse a unified customer view from all data sources.

2. Improve Data Quality With Normalization and Enrichment

Getting your hands on quality business data is only the beginning. If the data isn’t formatted correctly and missing gaps are not filled, your whole GTM data strategy can get thrown off. 

Data normalization and enrichment are critical for creating an up-to-date, reliable data warehouse with the highest quality data possible. Data quality is determined by two characteristics:  

  • How complete the data is, measured by match and fill rate
  • How accurate the data is, determined by the confidence in your match and fill rate

Ensuring the completeness and accuracy of your data is vital for better segmentation and targeting. 

What’s the solution? Normalization ensures semantic consistency for your GTM data, while enrichment ensures you’re getting better data reliability as it pulls and completes missing data points from multiple sources. 

Figure: Side by Side comparison of Traditional vs. Multi-Vendor Enrichment Techniques

3. Build Account Intelligence

Creating GTM success through data-driven decision-making takes robust account intelligence capabilities. 

With a clean and complete data foundation, you can leverage analytics and modeling to understand your Ideal Customer Profile (ICP), along with best-fit and lookalike prospects. Additionally, layering in intent and scoops to optimize targeting ensures that you are not wasting time and resources on leads and accounts that won’t turn into revenue or that aren’t at the right time in their buying cycle.

4. Operationalize Cloud Data

The new world of insight sales is setting the standard for a complete, unified database. To make it a reality, operationalizing your cloud data strategy must be at the forefront of your GTM initiatives. 

Cloud data warehouses are powerful, efficient, and fast. But marketing, sales, and operations teams don’t work in cloud data warehouses. They work in CRMs and MAPs — the places where they need data and insights. 

Easy-to-use UI-based data orchestration workflows can bridge the gap between IT/data teams and revenue teams. Automated workflows can rapidly pipe data from CDWs into CRMs and MAPs, while also enriching and improving the data. They can also set event-based triggers to act on data changes to make effective and rapid decisions.