Page 228 - Contributed Paper Session (CPS) - Volume 7

CPS2069 Pamela Kaye A. T.

Tagged and numbered: Big data and central
banking
Pamela Kaye A. Tuazon
1
Bangko Sentral ng Pilipinas

Abstract
Big Data is a current global buzzword which gained traction due to its novel
sources, processing, and applications. Highfalutin words associated with Big
Data include algorithms, machine learning, artificial intelligence, coding and
programming. However, the true value of Big Data is not the deluge of
information but rather the ability of the end-user to calibrate and harness
insights to guide future strategies and policies. Central banks worldwide are
incorporating Big Data as an unconventional supplement to official statistics
to produce leading and near-real time economic and financial indicators. The
paper is organized as follows: Section I provides an overview of Big Data,
Section II explores Big Data in the Philippines, Section III elaborates on the
potential applications of Big Data in Central Banking, and Section IV concludes
with the challenges ahead and recommends routes for the Big Data pilot
project in the Philippine central bank.

Keywords
Smart Data; Nowcasting; Digitalization; Real-time market surveillance;
Leading indicators

1. World Digitalization and Big Data
Imagine a world tagged in numbers – every footstep, every tap of your
transportation card in the bus and train stations, every email sent, every tweet,
every swipe of your credit card, or even just your mere location – every tiny
deed leaves digital crumbs that allow analysts to predict behavior and
recommend future strategies.
Indeed, the emergence of the knowledge economy rests upon the
digitalization of operations, physical infrastructures, and even human
interactions resulting into information-rich digital footprints. These datasets
when harnessed and analyzed, easily provides both policy-makers and
businesses with ample real-time insights. Big Data, therefore, became the

Ms. Pamela Kaye A. Tuazon (Bank Officer IV) from the Department of Economic Statistics
1
from the Bangko Sentral ng Pilipinas and is part of the Department’s ad hoc and interim Big
Data Team. Errors and omissions are the sole responsibility of the author and do not
necessarily reflect that of the BSP.
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