Data Management: Removing Barriers to Effective GTM Motions

Today’s most dynamic companies rely on an intricate series of data-driven processes to stay competitive. From customer insights and lead capture to business intelligence and beyond, a high-quality data flywheel is the essential system that makes efficient growth possible.

To keep that flywheel turning, revenue operations (RevOps) leaders have to ensure that their mission-critical data is accurate, reliable, and accessible across teams. How do they do it? With a comprehensive data management system.

To understand how data management acts as the underlying vascular system that keeps RevOps alive, you first need to understand the advantages of a data management strategy, best practices for organizing your data, and how to make the most of your data assets with automation. 

What is Data Management?

Data management is the practice of collecting, storing, organizing, and using data. Teams use an array of tools, processes, and technologies to manage data effectively. A strong data management framework combines data governance, quality control, security, analysis and automation to make sure data is both accurate and reliable. 

A key task of master data management (MDM) is setting up a single set of data to ensure data parity across a business’ tech stack. A comprehensive data management solution enables teams to create and maintain a master data set that’s enriched from multiple data sources and kept up-to-date automatically.

These outcomes are essential to the life of a business. Consider this: in a recent global survey, CRM users estimated that their data decays by 34% annually, with 44% of respondents estimating their companies lose more than 10% in annual revenue because of poor data quality. 

Data Management Best Practices

A lot of best practices for data management initiatives hinge on working with a system that unifies your processes. 

In addition, consider these best practices can ensure businesses make the most of their data management efforts: 

1. Define Your Data Assets

Identifying your data assets is the first step to effective data management. It involves understanding the different types of data you have, where it’s stored, and who is responsible for it. 

“Your third-party data sources are equally as important as the tools you use to manage your data,” ZoomInfo product marketing manager Neha Nirkondar says. “Ensuring you’re sourcing your data assets from providers that offer plenty of data depth and breadth will make for better outcomes.”

When purchasing data, teams should look beyond the volume of records being provided. The dynamic nature of business requires an equally dynamic solution for data management — seek a provider who can deliver accurate, current, and reliable data across your entire business and help you manage it in real time.

Once defined, your data assets can help inform a comprehensive data governance framework that ensures data accuracy, reliability, and accessibility.

Defining your data assets involves a few key steps:

  • Conducting a data inventory: Identify all the different types of data your company collects and stores, including customer data, entity data, product data, and even contact records. 
  • Determining data ownership: Define who is responsible for each data asset, including data stewards and data owners.
  • Documenting data management: Note the origin and transformation of each data asset, including data sources, cleansing initiatives, and data warehouse unification. 

2. Develop a Data Governance Framework

A data governance framework defines who can access data, what types of data are available, and how that data is accessed. But you can’t manage what you don’t measure. 

Consider these steps when deciding how you’ll run data governance initiatives: 

  • Establishing data quality metrics: Define data quality metrics and establish processes for monitoring and reporting on data quality.
  • Creating a data governance team: Form a team responsible for developing and enforcing data governance policies.
  • Defining data governance policies: Define and document policies for data security, data quality, and data access.

3. Harness Platforms With Machine Learning and Automation

Machine learning algorithms analyze large datasets to find patterns that offer valuable insights. Without these algorithms, finding insights would be a time-consuming task filled with mistakes.  

“Managing all your data manually is very difficult to do. You can’t possibly catch every single lead coming into your CRM manually — it’s too resource-intensive,” Nirkondar says. 

Machine learning and automation make data management a much easier process for RevOps teams. In this case, using machine learning and automation involves:

  • Identifying opportunities for automation: Examples include data profiling, data cleansing, and data validation.
  • Implementing machine learning algorithms: Analyze large datasets and identify patterns and insights.
  • Using automation tools: Automate routine data management tasks, such as data cleansing and data validation.

Multi-vendor enrichment automatically fills in missing data from the best possible source, field-by-field.

4. Invest in a Data Management Solution

“Ditch spreadsheets when you’re doing data management. They’re error-prone, inefficient, and will not save you money in the long run,” Nirkondar says. 

A comprehensive data management solution that brings together and orchestrates all your data management needs enhances a business’ go-to-market campaigns and sets them up to make decisions on business intelligence with a higher rate of accuracy. 

“Let’s say someone fills out a demo form on your website that’s connected to your internal sales system. How do you make sure that any new data you’re ingesting is formatted correctly, validated, and up to date?” Nirkondar says. “This is where solutions like RingLead play a key role.” 

Investing in a data management solution involves a few steps: 

  • Evaluate your business needs: Identify the specific data management needs of your company, such as data integration, data cleansing, or business intelligence.
  • Choose the right platform: Evaluate several data management solutions and choose the one that best meets your business’ needs.
  • Implement the solution: Stitch your solution into your workflows and customize it as needed.

The Challenges Data Management Addresses

Without a data management strategy that accounts for the tools, processes, and technologies that enable the free flow of quality data, businesses suffer from operational bottlenecks and missed opportunities. 

Effective data management addresses key areas of data quality, security, integration, and overall data governance.

Challenges solved with data management: data quality, data privacy & security, data integration, and data governance

Data Quality

For RevOps teams specifically, data quality is one of the most important components of a data management strategy because it feeds critical components of a revenue team including lead routing, prospect outreach, digital marketing, and territory planning. 

“Your data needs to be high-quality. For instance, CRM adoption is a big challenge lots of sales and marketing teams deal with because they don’t trust the data to be reliable and up to date,” Nirkondar says. 

Consider the following data quality challenges and how costly they can be without proper data management:  

Manual processes: The resources required to process and analyze data manually are exorbitant, which leaves very little time for businesses to derive insights in time to be valuable. Automating data management enables RevOps teams to draw more accurate insights. 

Siloed data: When different teams maintain databases that live in separate applications, it becomes that much harder to see the entire picture. Applying data integration strategies that bring together disparate sources into a single platform helps with consolidation.

Disjointed point solutions: Data fragmentation and costly inconsistencies are a result of different teams using different solutions to manage their data. However, an integrated data management platform ensures every team is working from one common data set. 

Incorrect data: Errors and inconsistencies in your data lead to bad business decisions. Bad data is caused by a slew of things — data entry errors, duplicate records, and data decay all play a part in undermining your data quality. Management strategies as well as automated data cleansing tools to minimize bad data can work to keep data fresh. 

Unifying your data efforts: Unifying data efforts is a challenge when data is collected and managed differently by different teams. With an enterprise-wide data strategy and a database management system (DBMS), teams can make sure data is consistent and easily shareable whenever and wherever they need it. 

Data Privacy and Security

Data breaches and unauthorized access to sensitive information are significant pain points addressed by data management. A company can suffer financial losses, reputational damage, or legal consequences if its data security is compromised. 

Data management tackles this by implementing access controls, encryption, and other vital security measures to protect from data breaches.

Data privacy initiatives are also a high priority to ensure compliance with the growing body of data privacy laws around the world. The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are two examples. 

Data Integration

Without a comprehensive tech stack that manages and hands off data seamlessly, teams can quickly run into data integration problems. 

“You want to make sure your data isn’t siloed and that there’s parity across your systems. A data management system helps you do that in an automated way,” Nirkondar. 

For sales and marketing teams, data primarily lives in CRM and MAT tools. It’s up to revenue operations teams to find a way to unify and standardize the existing data as well as any new data coming in. 

Data Governance

Data governance is a practice area dedicated to addressing several interconnected roadblocks: inconsistent data usage, data quality issues, regulatory non-compliance, and even a lack of clarity about the data asset itself. 

Data governance defines clear data usage policies, procedures, and the guidelines that dictate data access and security. In other words, data governance ensures consistency across the company — a key ingredient in the data-driven business recipe. 

Gain a Comprehensive Solution For Data Management With ZoomInfo

High quality data and the right data management tools are the foundation for companies to unlock the true potential of their data. 

Discover how ZoomInfo OperationsOS can help your company manage its data more effectively.