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Banks are sitting on a gold mine of diverse data that is becoming bigger and bigger every day. Collecting it has never been easier — the challenge is using it efficiently and changing behaviors to get real business results.
Craig Turrell, Head of Plan to Perform (P2P) Data Strategy & Delivery at Standard Chartered Bank, knows this better than anyone. In his financial planning and analysis (FP&A) division alone, there are more than 400 people producing over 5,000 pieces of information every month, 90% of which can be automated. This story uncovers how Craig and team transformed FP&A’s ability to answer more questions with data, faster.
The Challenge: From 10 to 400 Million Rows of Data
FP&A works on a jigsaw puzzle of major core financial statements and structure systems of the bank. They need to be able to look five years back and five years forward to identify abnormalities and trends, do balance sheet analytics, and conduct cost analysis to answer complex questions around how and where the bank is making profit, how the bank behaves, who should be hired and where they should be placed as related to cost profiles, etc.
Of course, they had the systems in place to do all of this for many years, but analysis was limited to 10 million rows of data. While it sounds like a lot, the reality is that teams could provide one or two levels of detail for the 10 core products of the bank or core primary country markets and look at basic account structure over about three months, and even at those dimensions, they had to start splitting analysis in pieces — they were exceeding 10 million rows really fast.
“It’s simple math — take two account structures x two currencies x 150 countries x 1,000 cost centers… we are responsible for managing problems with 500 million moving pieces, and at any given time, we could only see about 10% of it.”
Ultimately at the time, they designed a system that was intended for the top of the house (i.e., they digitized the reports and data that the core CFOs looked at), but Craig realized that in reality, this approach wasn’t going to be able to influence the behaviors of the bank. He needed to find a way to impact the day-to-day work of financial analysts, making them more efficient and effective.