Data Principles

Pete Stiglich, Founder and Principal Architect

Data Principles, a data management and analytics consulting firm, takes a business-driven approach to help organizations design, modernize and optimize data environments to drive meaningful business outcomes.

It starts with a Business Concept Model that captures how a client’s business operates and uses it to guide architectural governance. It identifies key business concepts, defines how they relate to one another (including the cardinality governing those relationships). This prevents systemic application, integration, and reporting errors and ensures analytics solutions, data warehouses, mergers, or modernization initiatives align with clients’ core operational realities.

“Operational performance gains begin not with code, but with clarity about how our client’s business and data work,” says Pete Stiglich, founder and principal architect.

The impact of this approach was evident in its work with the Arizona Lottery. Data Principles re-architected alignment between business logic and data structures. It analyzed the business rules embedded in transactions and financial flows, modernized the environment, migrated data and processing to the cloud, integrated disconnected systems and implemented a modern BI platform. It also established auditing and financial controls to ensure data reconciled with source systems and remained accurate.

With timely, reliable data available with self-service visualizations, leadership gained clearer operational visibility. Sales doubled and profitability increased by approximately 30 percent, reflecting stronger alignment between business rules, system design and reporting outputs. The Arizona Lottery was recognized as a finalist for Qlik’s Global Transformation Award for Analytics in 2025.

Turning Complex Data into Actionable Insight

How does the firm reconcile legacy data systems with future-state analytics and business goals?

Many organizations operate with systems built decades ago. Over time, modifications accumulate, documentation is lost and original architects move on. Business rules become embedded in code rather than in clearly defined models. Data structures drift from how the organization actually functions.

Operational performance gains begin not with code, but with clarity about how our client’s business and data work.

In parallel, Data Principles conducts a practical examination of data and systems that already exist. It profiles datasets, analyzes current data models, evaluates system architecture and examines key integrations.

The dual perspective ensures the business vision aligns with realities. If the required data does not exist, it helps design systems to capture it. If analytics ambitions exceed available information, it defines staged capability growth, outlining what must be cleaned, integrated or restructured before advanced reporting becomes viable.
Why is structured communication and oversight important for maintaining alignment during data projects?

Regular client communication, proactive follow-ups, and structured oversight (including training delivery) ensure that projects remain aligned and potential issues are addressed early.

The execution discipline translates into tangible outcomes across industries. For a nonprofit organization, Data Principles transformed a web-based podcast platform into a mobile app, dramatically improving accessibility and user engagement.

It also helped a college gain granular visibility into credit card expenditures. By integrating detailed transaction-level merchant data, Data Principles enabled real-time insight into actual purchases, bringing transparency and accountability to financial oversight.

Delivering Results through Execution Excellence

In what way does data preparation influence the effectiveness of artificial intelligence and analytics initiatives?

The firm’s delivery model is led by senior practitioners with over two decades of experience. Certifications are a key hiring criterion, ensuring that team members possess verified expertise in relevant tools and methodologies. Ongoing professional development through participation in conferences and industry associations such as DAMA (Data Management Association) and the Society for Information Management keeps it at the forefront of evolving best practices.

That depth of experience becomes particularly critical as organizations accelerate AI adoption. While organizations rush to deploy advanced models and large language technologies, AI is only as strong as the data that fuels it. Data scientists generally spend 80 percent of their time locating, cleaning and understanding disparate data sources before they can begin meaningful modeling. Data Principles prepares and integrates data so that data scientists can focus on modeling and predictive work.

Even as AI capabilities advance, the firm’s role remains grounded in fundamentals: understanding the business and building architectures that reflect operational realities.