CHALLENGES

Unique Challenges Drive Development of Framework

PatternBuilders’ Domain-Driven Analytics (DDA) methodology and Analytic Framework were developed to address the unique challenges of advanced analytic application development:

• Size shouldn’t matter. Whatever the size of your data, the analytics solution  must meet optimal storage and performance standards.
• Real-time analytics. Analytic calculations must be returned in real time and support easy ad-hoc analysis and most importantly, automatically delivered alerts—no matter the size of data involved in the calculation.
• Global- to granular-views.  Enterprises today need analytics on both the macro- and the micro-level to succeed and unsophisticated users must be able to slice and dice data at the level appropriate for their responsibilities.
• Worldwide, “anytime” access. A diverse set of users (such as executives, regulators, managers, and individual contributors representing a number of different functions as well as partners) must be able to access the application from different geographic locations and/or different platforms (such as a laptop, cell phone, or PDA) securely and with real-time performance.
• Ease of use. The user interface must use the language of the industry and be able to support a diverse set of users with different areas of expertise and roles (internal and external to a company), and offer self-service capabilities that empower the end user to create their own analytics as well as automate business processes to make them more effective and efficient.
• Immediate, quantifiable results. Analytic applications must demonstrate immediate, quantifiable results from both a development and end user perspective. Traditional data warehouse-based approaches require extensive training and with any analytic calculation more complex than sums, averages, or means, additional development effort as well as testing is required.  As a result, costs can increase exponentially over the life of an analytics project in terms of resources and ultimately, time-to-market, with little benefit to the end user. Thus, the development environment itself must be flexible so that analytic applications are able to quickly reflect changes in industry processes, terminology, rules, and regulations while the end user must be able to easily incorporate those changes in his or her daily work environment.