In a recent interview, A&G Managing Editor Holt Hackney interviewed erwin, Inc.’s CEO Adam Famularo about why business process (BP) and enterprise architecture (EA) have become the foundation for good data governance.
Q: erwin has been a standalone company now for two years. Can you tell me about the company’s transformation?
erwin has been the data modeling leader for 30 years with a tremendous global user base. Based on industry needs for data visibility and value, we knew erwin could be the cornerstone technology to a standalone data management company. So, after divesting erwin from CA Technologies, we began acquiring companies [Corso and Casewise] to build out a software platform with solutions for enterprise architecture (EA) and business process modeling. In late 2017, with significant customer input, we decided to make data governance (DG) the hub for all these domains. From our perspective, data governance is the driving principle for mitigating risk, improving operational performance, and accelerating growth because it enables you to discover, understand, govern, and socialize your data assets—to see all your mission-critical information in context. With such data visibility, control, and collaboration, you can produce more results—from ensuring regulatory compliance and security to generating more top-line revenue.
Q: How do enterprise architecture and business process modeling relate to data governance?
For years, data governance was the volleyball passed back and forth over the net between IT and the business, with neither side truly owning it. Once an organization understands that IT and the business are both responsible for data, it needs to develop a comprehensive, holistic strategy for data governance that is capable of four things:
1. Reaching every stakeholder in the process
2. Providing a platform for understanding and governing trusted data assets
3. Delivering the greatest benefit from data wherever it lives, while minimizing risk
4. Helping users understand the impact of changes made to a specific data element across the enterprise
To accomplish this, a modern data governance strategy needs to be interdisciplinary with traditional silos broken down. Enterprise architecture is important be-cause it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions they enable. A business process and analysis component is also vital. It defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible. Enterprises can clearly define, map, and analyze workflows and build models to drive process improvement as well as identify business practices susceptible to the greatest security, compliance, or other risks and where controls are most needed to mitigate exposures.
Q: What are the challenges/opportunities facing the industry in terms of enterprise data management/governance?
Realizing that data isn’t just for the “data people.” We’re all data people with the opportunity to impact the business. Rarely have BP and EA been so strategic within an organization than at this point in time—with data governance raising the visibility and value of the functions. Clearly, the European Union’s General Data Protection Regulation (GDPR) has spotlighted the relevance of data governance. But we see the need for DG for a number of reasons beyond regulatory compliance—customer satisfaction/trust, reputation management, analytics/business intelligence, and, of course, data security and privacy. Look at the Facebook debacle and the subsequent data drama/trauma. Additionally, an assessment of the data breaches that crop up like weeds each year supports the conclusion that companies, absent data governance, wind up building security architectures strictly from a technical perspective. They don’t gain visibility into the full data landscape—linkages, processes, people, and so on. In sum, they lack the ability to connect the dots across the data trinity—governance, security, and privacy—and to act accordingly. We’re giving people the tools to connect those dots—to understand what data exists and its context as it relates to processes, systems, organizational units, and technology in use.