Industry models pave the fast track to success with financial services data
- Philip Paz of Zennify outlines how a new approach to data modeling can speed up innovation and unlock better value for financial services (finserv) organizations.
Despite being recognized as the currency of modern business, data is still one of the most underutilized assets in companies today, even in the financial services industry. And with the increased velocity of data coming from a variety of internal and external sources, investing in a data-driven strategy focused on data architecture, governance, and advisory — either internally or with a partner — is critical to keeping up with these data streams and utilizing them to deliver essential business insights and facilitate better customer experiences.
In financial services, it’s often an M&A transaction or a banking core migration that brings home to business leaders the importance of better data management. Acquiring a new business requires integrating its data and systems with your own without an interruption in service and support on either end. Once successfully completed, it still requires massive strategic coordination and organization to use every data source effectively and without building silos.
Organizations experiencing a banking core migration face similar data architecture, integration, and governance issues, plus the complexity of supporting the current operational solution while preparing for a banking core transition. A new banking core system is one of the most challenging milestones a bank can undergo but is sometimes necessary to create next-level operational efficiency.
The recipe for finserv success in data management, analytics, and AI capabilities is for a business to have a deep knowledge of its tech stack and how each product and service works independently and in collaboration to support operations and achieve business outcomes. Further, the architecture and governance model must be capable of translating business needs into specific data and system requirements and the frameworks to build effective solutions across the enterprise. Problem is, this ideal often seems out of practical reach for many organizations.
The reality of our data-driven world is that innovation is relentless. It doesn’t care if some organizations are more prepared than others; progress marches on. To keep up, organizations need to marry their business and data strategy to stay informed and agile. They need a logical, framework-based approach that can help define and solve their specific data problems while building a flexible foundation and opening up new industry and customer engagement opportunities.
The perfect solution doesn’t exist
Business leaders can’t afford to wait for the 'perfect solution' because it doesn’t exist – and even if it did, it wouldn’t stand still. Instead, they must consider how to craft a data strategy, architecture, and tech stack that can grow and evolve. Designing this strategy doesn’t have to begin with traditional discovery. In fact, I would argue that traditional discovery leads to unnecessary project inflation and can overlook the core elements of effective data solutions.
Think about it. Internal innovation teams and outside consultants often spend months gathering data to report on the right solution for managing data. During that time, even more data piles up, potentially clouding or complicating the team’s recommendations, leading to project expansion and lengthening the solution timeline. Meanwhile, the continued lack of a coherent, end-to-end solution impacts employees, customers, and the bottom line.
A better solution? Start with an industry model
An industry model is a pre-built model of a specific business domain — this could be a credit union, commercial bank, wealth management firm, or insurance agency. It looks at everything representing a customer across multiple business verticals, such as Loans, Wealth, or Deposits, focused on a single customer aggregate view. An industry model bypasses the need for intensive discovery. Instead, companies can speed up the strategy and design phases at the start of the engagement to quickly gain insight into their data architecture, quality, strategy, and more.
The beauty of an industry model is its speed, versatility, and comprehensive data fields that power data-driven decisions. Siloed business vertical data prevents an organization’s capability to evolve business offerings by blocking visibility into actionable insights that may be common across verticals. The model remains the solution’s core and provides a unique management capability to curate and distribute portions of data with the relevant apps, systems, and processes supporting the business. Models can be governed as source code and support semantic versioning for source control and revision history, supporting backwards compatibility for associated API-led connectivity.
Further, once the model aggregates and organizes your customer data, it shifts the organization from the old siloed business structures and creates 360-degree customer profiles that contain all relevant data — what we call The Golden Record. You can then take the model and turn it into technology assets that support and build upon that Golden Record. This ensures end-to-end data visibility and access to empower the functions and responsibilities of bankers, wealth managers, insurance agents, and anyone else in the organization who depends on data to connect with and serve customers.
Accelerate your data roadmap
Ultimately, every good data strategy ties directly to a company’s desired business outcomes, which are shaped in turn by the specific market and industry. Businesses that use an industry model to manage a complex project like a banking core migration or a new acquisition can avoid getting boxed in by rigid architecture, nebulous governance, and unfathomable, stagnant data lakes. Instead they can build a data foundation that can thrive and adapt quickly to new challenges and opportunities. By adopting an industry framework that helps predefine key needs, parameters, and risks, organizations can accelerate their data and innovation roadmaps to take action and evolve faster than their competitors.
It’s not all just theory. These thoughts reflect our experience as an organization that leverages models in our go-to-market, data-driven finserv strategy. We feel that with all the growth and evolution in this space, leading with a model enables you to streamline processes without the investment it requires to get there doing it yourself. Our customers are feeling the same, saving organizational time spent on processes that can be addressed with models and finding speed to market as a significant benefit.