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Using data analytics helps solve important problems

Leilani C. Ramirez-Layug Deals and Corporate Finance Director, PwC Philippines 02 Nov 2018

With the holiday rush coming up, it is likely that the government will once again adopt immediate solutions to alleviate the traffic situation in Metro Manila.

Just in recent months, they have tried to implement the single-driver and provincial bus bans along EDSA. These were then met with backlash as the public felt they were not carefully thought of. The government quoted studies never showed nor presented to the public.

Did they really use analytics to plan out these policies? How about for the number coding scheme and the additional excise tax on automobiles brought about by the Tax Reform for Acceleration and Inclusion or Train Law? Were they able to use statistical models to verify if these were effective or can predict favorable or unfavorable outcomes?

The use of data analytics tools can actually uncover hidden insights and eventually solve important problems such as those mentioned.

Data analytics has been used since time immemorial. Our early ancestors drew pictures or symbols on cave walls based on their day-to-day experiences. Rulers of ancient civilizations knew how much grain they had in their inventory and whether it was enough until the next harvest. Furthermore, census, taxes, property information, and other data were consistently being tallied and analyzed.

However, with the explosion of data in the recent decade, the Internet of Things and the increasing societal problems, it is imperative for big data analytics to evolve.

Science and medicine

Between 1831 and 1854, doctors and scientists believed that the cholera outbreak in London was airborne. But Dr. John Snow, one of the founders of modern epidemiology, used an early form of data analytics to uncover that the cholera was actually caused by water contamination from a frequented pump on Broad Street.

This was through the analysis of information from hospital and public records as well as a geographical grid to chart cases and deaths.

Just two years ago, Microsoft and a Columbia University graduate, John Paparrizos, analyzed large samples of queries of certain symptoms felt entered in the search engine Bing. Through the use of data analytics, Paparrizos tried to identify Internet users who suffer from pancreatic cancer even before they are diagnosed.

Better service delivery

Early this year, the Department of Foreign Affairs was under fire for the limited slots in their online passport appointment system. An in-depth analysis of daily transactions such as online bookings, walk-ins, cancellations, peak periods, or a link between other agencies (say for the Philippine Statistics Office, the number of birth certificate requests for passport application) could have prevented this dilemma.

Monte Carlo Simulation is another mathematical model used to assess risk and probability of varied outcomes. It is widely used in financial services, energy (utilities, oil and gas), engineering and construction, as well as project management.

However, it can also be used by technology start-ups and in ecommerce to determine customer lifetime value (CLV). This statistical model can also be used by various businesses in customer relationship management (CRM) such as queuing and scheduling, push-marketing, rewards, increasing conversion rates, as well as consumer sentiment analysis.

A glimmer of hope

In the last two years, the Metro Manila Development Authority (MMDA) and Thinking Machines have teamed up to use Waze data to assess crucial choke points, crash-prone areas, traffic peak hours, etc.

The next questions however would be: What were the insights derived? How were the results used in crafting their traffic policies? What could happen next?

Historical data could actually be used for predictive analytics. Regression tools such as Autoregressive Integrated Moving Average Models (ARIMA) used in time-series forecasting may be helpful in analyzing whether planned policies could potentially be effective in the future.

Omni-channel world of commerce

With the varied retail channels, consumers now go through a wide range of purchasing options, and each individual journey is unique. A customer could either check out a product in the physical store or website, search online for reviews, purchase through the app, or pick up the item at a different location. The possibilities are just endless.

Data analytics could optimize on each customer’s unique purchasing journey in real time. It could be used to offer options at every interaction point to maximize value. Algorithms are embedded to showcase what potential products a customer could be interested in.

Discount coupons could also pop up using geo-fencing methods whenever a customer is within a certain radius. At the back end, data analytics also helps manage costs and efficiency in certain areas of manufacturing, logistics, finance, and human resources.

Through data analytics, we can become data-driven problem solvers of today’s increasingly complex world. Borrowing the words of Steph Stephens-Davidowtz, from his book Everybody Lies, “It aims to provide the missing evidence of what can be done with Big Data – how we can find the needles, if you will, in those larger haystacks”.

This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors.

Contact us

Leilani C. Ramirez-Layug

Deals and Corporate Finance Director, PwC Philippines

Tel: +63 (2) 8459 3142

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