By following a series of tactics as you apply EDA, you’re able to build predictive models you can trust. Along the way, you begin to understand your data in the context of the problem you’re trying solve. You also improve the predictive capacity of your models and generate insights that strengthen your business. When your EDA is done well, there are no surprises.
Knowing and understanding each piece of data you have is key to producing meaningful results that have practical application. Exploratory data analysis, often the first step in data analysis and modeling, is an investigative process that gives you a feel for data sets, enabling you to see patterns, spot anomalies, test hypotheses, and check assumptions.
When an information services company discovered its model monitoring process was fraught with inefficiencies and roadblocks, they needed to redesign the platform to support automation, provide confidence in results, and facilitate widespread adoption throughout the organization.
The graphical representation of data translates information from a computer readable format into a dynamic story, providing users with key insights that help them understand trends, recognize patterns, identify outliers, and, ultimately, make business decisions.
Having already invested heavily to create a robust authorization platform, the bank was prepared to increase its stake in technology with an additional $1.2M. After investigating the problem, Flying Phase provided the bank with a roadmap to save between $500K and $1M in fraud costs and credit-risk approvals by rebuilding the back-up decision logic found within the third-party processor.
A Data Management Playbook for Success – Your bank’s data can help you enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships. But it has to be expertly leveraged. For that, you need the expertise of experienced data practitioners and a focus on outcomes. “Helping Financial Services Organizations Leverage Data to Gain Competitive Advantage” explains how to transform vision into action as you develop and implement a maximum impact, cost-effective data management strategy for your organization.
In the third and final installment of “Making Big Data Smart Data” we provide banking leaders with a practical guide that identifies key milestones, along with several best practices, as they drive toward a smart data management strategy.
When the COVID-19 pandemic slowed the world’s economy to a crawl, many banks relied on models and forecasting to anticipate customer delinquencies and hardship and to efficiently staff its call centers.