Diving Deeper

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.