Diving Deeper

Make Big Data Smart Data (Part 2)

While financial services organizations have spent significant time and money collecting data on products, customers, and transactions, they have put only fractional amounts of that information to work. Now is the time to make big data smart data. In this post, we provide 4 considerations to ready your organization.

Part 2: The Key to Becoming a Data-driven Enterprise Your competitors are working vigorously to execute a data management strategy that will enable them to dig deeper and pull out insights that enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships. Data management on its own is probably not enough. It’s what you can do with data once you have a handle on it. Today’s small banks and startup FinTechs are also on the move, not hamstrung by twenty years of infrastructure accumulation. They will grow quickly because they are innovative, and they are nimble. Your...

Make Big Data Smart Data (Part 1)

Enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships by transforming your bank from a legacy organization into an information-based, data-driven enterprise.

Part 1: What’s the Forecast? “Regarding the weather, so much is said about it, but so little is done.” That familiar quip comes to mind as I think about data management in today’s financial services environment. An industry statistic that has received about as much play as the comment regarding weather is startling: while 90 percent of global data was created in the past five years, only 1 percent of it has been analyzed. The rest? It’s siloed into databases and file systems, hopefully secure, holding the secrets to critical insights that could translate into more profitable products, better customer...

Leveraging Data Infrastructure and Governance to Speed Model Development and Execution

Architected data platforms provide a foundation for automated modeling and forecasting to reduce cycle times and ensure well-managed data inputs.

Case Study: Architected data platforms provide a foundation for automated modeling and forecasting to reduce cycle times and ensure well-managed data inputs. The model development team at a Top 10 bank was responsible for forecasting on a $100 billion loan portfolio, but development efforts were often stalled by the complexity in data, and created compliance issues in the process. We implemented a data platform for providing authoritative and high-quality data to streamline the model development process and ensure the bank stays compliant and well-managed. Case Study: Leveraging Data Infrastructure and Governance to Speed Model Development and Execution

Data: Quality Over Quantity

Creating a culture of data quality not only has a concrete impact on the accuracy of your findings, but also inspires greater organizational buy-in when your framework adheres to certain standards.

Summary Solution: Creating a culture of data quality not only has a concrete impact on the accuracy of your findings, but also inspires greater organizational buy-in when your framework adheres to certain standards. More data is better data. It’s a common refrain in data science and other analytical circles – and it’s one that makes sense. If you have more to scrutinize, previously undiscovered insights may be lurking in unexplored places. And while accurate for the most part, the phrase lacks a mention of one key element: quality. As the tools that analyze data grow more sophisticated, the quality of...

Leveraging Big Data for Power Analysts

In order to get accurate data into analysts’ hands, organizations should employ a combination of data lakes and data warehousing to effectively balance the need for freedom in a structured data environment.

Summary Solution: In order to get accurate data into analysts’ hands, organizations should employ a combination of data lakes and data warehousing to effectively balance the need for freedom in a structured data environment. The potential of big data is becoming undeniable. The breadth of data collected has exploded, and the tools and techniques to effectively leverage it have never been more sophisticated. From tech companies born in the cloud to fast food chains, technology-forward, data-rich organizations are winning by harnessing this power. Within the financial services industry, data streaming and machine learning are supercharging marketing and social media efforts,...

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The Process Behind Process Automation

From EUCs to structured, automated data, we walk you through the process, best practices and change management to ensure you’re leveraging analysts’ skillsets to the fullest.

Since its introduction in September 1987, Excel has been the go-to resource in an analyst’s toolkit due to its versatility and capacity to quickly translate complex formulas into illustrative tables and charts. Over the ensuing years, a typical financial services analyst would build elaborate, interwoven spreadsheets, simple models, assumptions and calculations, all in Excel, creating end-user computing (EUC) applications. And while these EUCs built the foundation for modern automation, generally speaking, they lacked traceability or documentation, making model adjustments challenging and presenting serious challenges for governance, risk and regulatory compliance functions.