Abhi Yadav, Co-Founder & CTO, Zylotech
The data used throughout your business must remain high quality and in compliance, especially when it comes to revenue operations (RevOps) and marketing operations (MO). Maintaining compliance is a challenging task considering the average enterprise uses 1,295 cloud services, with customer data scattered across many of them. With so many cloud services available today, RevOps and MO team managers must regularly reevaluate which combination of tools will offer the best business outcomes. And, as sales and marketing teams use more tools and data sources, they are starting to share more responsibility when it comes to data governance. However, many companies still take a traditional approach to data governance, which is broken in a number of ways:
- Data lakes, data warehouses, and master data management (MDM) are typically managed by an IT manager or a chief data officer (CDO) who doesn’t understand all the sources of data and data fields, such as individual line of businesses (LOBs).
- Companies often have neither documentation nor an enterprise data dictionary available at a level a LOB team member can understand and use.
- Traditional data governance platforms are not business or Ops friendly. They typically don’t offer a unified data model, domain driven design or interoperability, e.g. SAP, IBM, or Informatica,
- Integrations and data workflow automation create a tangled mess with no control or governance.
- “Single source of truth (SSOT)” is often applied to only one system instead of across all systems.
In addition, many traditional data governance platforms use outdated technologies that cannot meet modern security and privacy standards—a serious problem if your company handles customer data that must comply with current regulations, such as GDPR and CCPA.
You Need a Robust Data Governance Strategy
Thanks to machine learning, cloud services, and APIs, integration has become a commodity, especially in marketing. Analytics once pulled only weekly are now accessible by the hour. And for many companies, increasing the speed of data activation is critical to business survival.
Data infrastructure and business intelligence tools have advanced in recent years to support rapid innovation. However, most DataOps tools have lagged, unable to handle the requirements of modern data and data governance. Most traditional DataOps tools lack advanced automation and intuitive user interfaces (UIs), making them one-dimensional, unscalable, and not business-friendly.
To reach company revenue and customer experience goals, the RevOps team (Marketing, Sales & Customer Ops) needs the ability to see them:
- Data across salestech and martech systems.
- Number of unique people or companies that are present across systems.
- Quality of the data (high or low).
- Interoperability among systems.
- Incomplete or dirty data or data debt.
This ability is also required when implementing data governance. Considering that many countries around the world have implemented complex privacy and security regulations, businesses operating globally must have modern and effective DataOps tools and a robust data governance strategy. RevOps teams must see, discuss, and understand everything involving customer data.
Instead of keeping these conversations among a small and siloed team of experts, enterprise leaders including CTOs, CDOs, and VPs need a cross-functional data committee that sets security and privacy KPIs. This committee must also keep the entire organization accountable, setting the standards for data governance and choosing modern tools that will meet them. The committee must pave the way for trusted data governance.
Make Way for Trusted Data Governance
If you search the web for the term “data governance,” you’ll find thousands of pages with different definitions and varying interpretations.
At Zylotech, we prefer the term “trusted data governance,” which we define as:
A set of modern principles and practices that ensure data remains high quality and in compliance throughout its entire life-cycle and in the customer journey – both pre and post sale.
Our approach to trusted data governance is based on five key pillars:
- Coverage: You need to look at the volume and completeness of your data and the level of unique identities e.g. individuals vs. companies, target personas, and ideal customer profiles (ICPs).
- Accuracy: How healthy is your data at the field level? Is it within expected ranges? You must make sure that attributes, such as a person, location, and company ID remain consistent and accurate across your systems. This is especially important when you merge data fields from multiple sources into one centralized system.
- Freshness: You need to ensure that data at the record level is as fresh as possible. Monitor your data continuously to confirm the last update of each record. Make sure company and contact information are always up to date to ensure contactability.
- Flows: You must take care when editing or enhancing your current business rules and workflows. Marketing campaigns and sales lead data flows must remain in sync. You should track errors via the lineage of data across sources, transformation, and dependencies.
- Labels and Rules: You need to standardize how your organization will visualize, describe, and tag your unified data model. For example, your organization might apply a specific tag to GDPR or privacy fields to enforce governance. Nominate data authorities to be the source of truth across your connected systems.
We believe these five pillars form the foundation for effective, trusted data governance for businesses of all sizes, especially enterprises.
For example, if you have a global marketing, sales, or customer operations team managing campaigns across multiple regions in40+ countries, the customer data driving those campaigns must adhere to the consumer privacy laws in each country. To comply with these different privacy laws, you need to have a unified data model, centralized and decentralized operations with federated governance, and consistent data practices. The five pillars of trusted data governance help you do that.
In CX first world, it’s not about one ‘system of record’ mentality, but interoperability across tech stacks and data sources and connected Governance is key.
Data Enablement, Machine learning, Automation, and next-gen DataOps are foundational to innovation. The convergence of these trends will drive the emergence of trusted data governance.