For today’s enterprises, mastering data is critical to spur growth and profitability. But it’s not just any data that drives competitive advantage. You need connected, accurate and reliable data to get valuable insights.
When key revenue-focused teams, such as sales and marketing, work off the same high-quality data, they can make more streamlined decisions. This enables them to focus on serving a single organizational revenue goal rather than individual departmental goals and achieve higher performance on most KPIs.1
For many large organizations, however, growth and scale have also significantly increased the complexity of their data ecosystem. As the number of tools and systems needed to collect more customer, revenue, pipeline and leads data has increased at the functional level, data has become more siloed across the organization.
This likely happens because department leaders need access to data to inform business needs. Central IT can be too busy to focus on each function’s urgent data and technology needs. They also may not understand the nuances of each function’s strategic use cases to create the right solutions.
Unable to wait, department leaders may task their own marketing or sales operations teams to find a solution. This could lead them to buy the tools they need at the departmental level and set up their own data processes and workflows. They may also put their own systems in place to manage data and mine insights. This approach may help individual functions become more agile and address many short-term tactical or campaign data needs. But, it also makes things more complicated at the strategic level when it comes to building and governing a unified data strategy.
- Instead of leveraging a common set of data to achieve a unified set of revenue goals, each revenue-facing function uses its own data for decision-making. When data sits in silos, it cannot be used to create a big picture of the business. This prevents revenue-facing functions from working together to achieve a shared revenue goal.
- As functional stacks grow, marketing and sales operations need to develop patchwork pipelines to connect new data sources or design workarounds to create the urgent campaign-level data sets business users need. Instead of working lockstep to leverage connected data, they could end up creating deeper data silos and technical debt. This can also lead to a riskier data security environment.
- When business users struggle to get the reliable, business-ready, enterprise-wide data they need to make smarter decisions, it can lead to missed opportunities, go-to-market (GTM) delays and derailed plans.
- Central IT might raise a red flag because data movement, though decentralized, is also in a riskier environment, with multiple ad-hoc data connectors and pipelines or complex vendor ecosystems exposing potential security vulnerabilities.
The Enterprise Data Management Conundrum
The gap between business needs and central IT priorities creates a data management conundrum for enterprise-scale businesses. On one hand, central IT focuses on ensuring data governance and security, but their involvement can slow things down and create bottlenecks. On the other hand, functions may want to be independent of central IT, but this disconnected approach can mean more complexity, silos, technical debt and data security hazards.
As the business scales and tech stacks / data volumes grow, the gap between data governance, a single unified source of data, and the reality of functional data silos widens.
Bridging the Gap and Elevating Data Impact with RevOps
The revenue operations (RevOps) framework is quickly emerging as a way to bridge this gap. A RevOps approach enables a unified data strategy to support a single set of revenue-growth goals across sales, marketing and customer service. At the same time, it also helps in liaising with IT to ensure the highest levels of data governance, a non-negotiable requirement for enterprise-scale businesses.
RevOps aims to create smoother and more stable data flows, reduce data complexity — including the vendor landscape — and put processes in place to get the right data to the right teams at the right time.
When RevOps works closely with central IT, it ensures that data is easy to find, understand and use at the functional level and that data quality and security adhere to enterprise governance standards.
Balancing Data Democratization and Data Governance
RevOps can help ensure that data democratization does not come at the cost of data governance. Data democratization is an ongoing process where all authorized employees, including non-technical team members, are empowered to access and use data to make more data-informed decisions. This is usually a priority for LOB and functional leaders, as they want their teams to ground strategies in credible, reliable data.
Data governance is the set of procedures and rules that govern how a business manages data in terms of collection, storage, processing, movement, sharing and deletion. It ensures stable and secure data management processes and high-quality data with impeccable data stewardship. Data governance usually sits under central IT and is a crucial C-suite priority. RevOps can enable proper access to data for all revenue-facing teams across the organization, based on a single source of connected, governed data, to meet IT needs.
Below are five steps for RevOps to drive greater alignment with IT and ensure that the unified data strategy serves both the business needs of functional users and the governance needs of central IT.
1. Develop a unified data strategy
RevOps acts as the glue between revenue-facing functions, such as sales, marketing and customer success and enterprise IT. Before getting to the data, RevOps needs to establish a shared understanding of the business goals and objectives needed to build the data strategy. IT teams have a deep understanding of the technical aspects of data management, while RevOps teams have a deep understanding of the revenue cycle, customer journey and functional priorities.
By working together, they can develop an optimal strategy to leverage data and drive CX and revenue growth. This involves defining how marketing and sales data will be collected, managed, shared and stored across the organization. They also need to establish a common data language, standardize data sources and determine clear data ownership. Finally, RevOps can document and own the data strategy roadmap, with built-in feedback loops from business users. This will ensure it continues to meet their evolving needs without compromising on enterprise data governance priorities.
2. Identify and prioritize data sources
IT teams are often responsible for managing data infrastructure, including databases, data warehouses and data lakes. RevOps teams deeply understand the business processes and workflows that generate and consume data, as well as the quality and accuracy required for each source. By working together, these teams can identify and prioritize the most critical data sources across revenue-facing functional teams. They can also build pipelines with the simplicity, scale and security needed to deliver the transformative impact of data.
3. Collaborate on the selection of critical data management tools
End-to-end data management starts with seamless data integration. This will create a single, streamlined data flow from disparate sales, marketing and customer service systems — such as marketing automation, social media, account-based marketing and customer relationship management systems — into the central enterprise data lake or warehouse. Here, the right extract, transform, load (ETL) and / or extract, load, transform (ELT) tool can dissolve data silos and transform data in different formats into clean, standardized business-ready data for a range of analytics and BI use cases. Working closely with IT to choose the ideal data integration tool helps assuage enterprise IT fears around data governance and security when data is moved across systems. At the same time, it gives business users the independence to perform day-to-day data integration, analysis and reporting without needing help from DevOps or IT.
4. Build repeatable, scalable, automated and self-serve processes
Reducing complexity in the data ecosystem means reducing the need for marketing and sales ops teams to build ad-hoc data pipelines or reporting workarounds to meet day-to-day needs. Your data integration solution should allow business users to easily find, understand and use the data they need. An AI-powered data integration tool speeds enterprise-level data design with smart recommendations and template-driven transformations. This means tech-savvy sales and marketing users can connect new data sources, create new reports and generate new insights within hours. The system should be able to onboard virtually any user, data or application with prebuilt connectors for multiple sources.
5. Develop a data governance framework
A key part of the RevOps and IT collaboration is defining the policies and procedures to manage data, including data quality, data security and data privacy. IT teams are often responsible for developing and implementing data governance frameworks, but RevOps teams can help ensure that the framework aligns with the business goals. They can also ensure that business users perceive governance protocols as enablers rather than bottlenecks. By working together, these teams can develop an effective and efficient data governance framework.
Data as a Business Asset
Data needs to be handled carefully by the right people, following the right processes. Done well, it can provide agile insights to gain a competitive advantage. Mishandled, it can lead to a loss of confidence and credibility — both internally and externally. RevOps takes a strategic approach that aligns the people, processes and technology to drive revenue growth. When RevOps liaises with central IT to unlock high-quality, unified, and governed enterprise data, it powers smarter marketing and sales decisions and enables better customer experiences and stronger revenues.
Learn more about how to empower your LOB and departmental teams to easily load, transform and integrate data at www.informatica.com/free-paygo