Page 229 - Contributed Paper Session (CPS) - Volume 7

CPS2069 Pamela Kaye A. T.
catchword for voluminous, structured and unstructured data sets, which are
hard to process, analyze, and store using traditional means.
The International Monetary Fund (IMF)’s 2017 Staff Discussion Note on Big
2
Data posited that there is no agreed-on definition of Big Data yet. However,
it is usually characterized by the 3Vs of high volume (Exabyte data), high-
velocity (speed of data), and high-variety (mixed sources and types).
According to the United Nations Economic Commission for Europe
(UNECE), the sources of Big Data ranges from Social Networks (human-
sourced information), Traditional Business Systems (process-mediated
data), and Internet of Things (machine-generated data). Due to the complex
nature of Big Data, advanced technologies and skills set are needed to
transform these random numbers into valuable insights. Hence, Big Data
should not just be viewed simply as a large set of raw information, but should
be processed and analyzed to achieve its end-purpose of guiding decision-
3
making – towards Smart Data (Marr, 2015). The unprecedented flooding of
information from various sources may seem daunting and overwhelming, but
the value of Big Data is its capacity to produce actionable intelligence.
4

2. Big Data in the Philippines
In the 2018 Global Digital Report , the Philippines maintained its standing
5
as the population who spend the greatest amount of time on social media (i.e.
4 hours average daily time spent on social media), which provides an abundant
source of information regarding the general populace and consumer base.
Big Data for the private sector means having a more vivid view of the
customers and being able to fine tune and personalize their products and
services to maintain and attract business. For instance, Big Data captured
through point-of-sale (POS) machines collects real-time information on
operations, enabling businesses to employ targeted marketing programs. E-
commerce, led by online shopping platforms , are now heavily patronized by
6
Filipino consumers; while Financial Technology (FinTech) start-ups capture Big
Data on trade and payment statistics, and even offers the services of matching
7
borrowers and lenders.
For the public sector, the Task Force (TF) on Big Data for Official
Statistics, spearheaded by the Philippine Statistics Authority (PSA), has

2 Hammer, C. et al. (2017). “Big Data: Potential, Challenges, and Statistical Implications,”
International Monetary Fund Staff Discussion Note SDN/17/06.
3 Marr tagged this concept of transforming Big Data into useful data as “Smart Data”.
4 Source: Exist Software Labs on turning Big Data into Actionable Intelligence (A Big Data
consulting and implementing firm).
5 Source: We Are Social, retrieved from https://digitalreport.wearesocial.com/
6 Shoppee and Lazada, among others.
7 LoanSolutions.ph and LendMe.ph
216 | I S I W S C 2 0 1 9