To maintain momentum in any business, mission-critical data has to be constantly managed. For RevOps leaders, the challenge of data management lies in ensuring that critical data is not only accurate and reliable but also seamlessly accessible to all relevant teams.
To understand how data management acts as the vascular system that keeps RevOps alive, you first need to understand the advantages of a well-structured data management strategy, best practices, and how to make the most of your data assets.
What is Data Management?
Data management refers to the strategies and processes used to collect, organize, store, and analyze data for business purposes. A data management program should typically aim to ensure data accuracy, security, and accessibility to support informed decision-making and drive sustainable growth.
With a robust data management practice, data leaders can execute valuable initiatives such as product performance analysis, customer behavior profiling, and enhanced customer experiences. When done right, data management also allows businesses to lower costs by reducing errors that would otherwise arise from working with poorly managed data.
The cost of doing nothing is extremely high. In one global survey, CRM users estimated 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.
Types of Data Management
Data management is an umbrella term for several types of ways businesses can manage the lifecycle of their data. Each management type serves an important purpose as part of a company’s daily operations.
Data Warehousing
A data warehouse (not to be confused with a data lake) is either hardware or a cloud storage service that houses important business data. Within a data warehouse, data scientists centralize large volumes of data from multiple data-as-a-service (DaaS) providers into one single repository that can support business intelligence and decision-making. This approach helps address various data as a service challenges by providing a unified and organized data structure.
Unlike transaction-focused traditional databases, cloud data warehouses are optimized for querying and analysis. As the volume and value of data grows, data warehousing continues to be a must-have tool for businesses that want to remain competitive with accurate business intelligence.
Data Privacy and Security
Effective data management safeguards against data breaches and unauthorized access. Data privacy and security management practices improve access control measures and enable encryption. Additionally, data management plays a vital role in ensuring compliance with data privacy laws such as GDPR and CCPA.
Data Integration
Data integration ensures a seamless flow of information across different systems. It helps tackle challenges like data silos by enabling consistent data availability across platforms. This is a key component for teams like sales and marketing that rely heavily on customer relationship management (CRM) and marketing automation tools (MAT). Automation helps data integration happen — it unifies existing and incoming data so it’s easier for teams to use.
Data Governance
Data governance helps efficiently and responsibly manage data use within a business. It tackles challenges like inconsistent data usage, data quality issues, and compliance with regulatory standards.
By establishing clear policies and guidelines for data access, usage, and security, data governance promotes consistency and clarity across all levels of the business. This systematic approach is fundamental in building a reliable and compliant data-driven culture.
Data Management Best Practices
Before data leaders can reap the benefits of data management, several challenges need to be addressed — preferably with a data management strategy. However, following a few best practices can help teams achieve data maturity faster, including:
- Structuring data storage: Organize data in a systematic and accessible way. This means categorizing and tagging data properly, making it easier to retrieve and analyze.
- Training employees on data literacy: Educate teams across the business about the importance of data management practices. Regular training ensures everyone understands how to handle data responsibly and efficiently.
- Integrating data management software: Use reliable and advanced tools for data collection, storage, and analysis to help teams combat data silos. Quality tools streamline database management system processes and offer better insights.
- Maintaining data accuracy: For best outcomes, data needs to be kept accurate and up-to-date by routinely removing outdated records, enriching missing fields, and deduplicating repeat records. This improves data quality and reliability for analysis and overall business intelligence.
- Running anomaly detection: Use statistical analysis and machine learning algorithms to understand data patterns, distributions, and identify outliers or anomalies in datasets. It’s a proactive way for data leaders to ensure the quality and consistency of data, which is crucial for reliable model training and analysis.
Data Management Processes and Plans
Setting the foundation for sound data management starts with introducing a system that unifies your processes. A key step in master data management is creating a single set of data to ensure parity across a company’s tech stack. An end-to-end management solution helps teams create and maintain a master data set that’s enriched and automatically kept up to date.
Consider the following data processes and plans to begin managing your data:
1. Define Your Data Assets
The first step to effective data management is identifying your data assets. 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 deliver far better results.”
When purchasing data, teams should look beyond the volume of records being provided. The dynamic nature of modern go-to-market strategies 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.
A few key steps in defining your data assets include:
- 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: Document clear 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, cleaning dirty data, 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 puts sales, marketing, and operations teams in a position to act quickly, confidently, and efficiently.
“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 your company’s specific data management needs, including data integration, cleansing, and 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.
A Comprehensive Solution For Data Management
Quality data and effective data management form 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.