I have been asked how I got into data science, considering my previous field of expertise in the water industry. I was a regulatory and compliance officer. Based on the job description, I should have dealt with mostly words—from laws, regulations, filing of permits and petitions, hearing transcripts, and other documentation. But as an engineer, a job title couldn’t stop me from working with numbers.
Last September 26, 2019, I spoke in the first-ever Kwentubig, a storytelling event about water and the environment. The event was organized by the Philippine Young Water Professionals (PYWP) and the 2030 Youth Force in the Philippines.
That night, I told the story of how the water industry led me to data science.
It was my first time to speak, monologue-style, without the help of visuals. I was lucky to be mentored by Yang Villa, founder of PYWP and Head of Isle Utilities Philippines.
Below is the transcript of my speech:
Early March this year, tens of thousands of households were affected by the Metro Manila water shortage. People were caught off guard and were not prepared at all. Many of them voiced their rage on the internet and that is how I knew about the situation.
I was relieved that my apartment still had water. After all, my place is only five kilometers away from the Balara treatment plants. I also live near government offices and public hospitals. Because of this, I thought our area will be spared. I was wrong.
When our water supply was cut off, I knew then that the problem is serious. On my way to work, I would see people lining up for water from tankers, fire trucks, and even refilling stations where the water is a hundred times more expensive.
As a water professional, I was frustrated. My job was to collect numbers from the team and report performance metrics regularly. My job was to monitor the water supply in different subdivisions outside Metro Manila, making sure that our facilities will be enough to provide for all the demand.
I did not anticipate that I would ever experience intermittent water supply for weeks. At least not this year. There was a time when even our office bathroom in UP Town Center had no water. While my customers in South Luzon were enjoying 24 by 7 water, I come home to an apartment where I have to fetch water in pails. I compute million liters of water on a daily basis and I go home to none.
It came to a point where even employees from the subsidiaries, like me, were needed to volunteer. One night, I was assigned to the data center. We were encoding flow rates, reservoir levels, and discharge pressures from different facilities hourly. For every facility, there is a target. If the value is below that target, we’d mark it red; if not, blue. From a point of view of a volunteer, it felt like we were merely monitoring. It appears to me that the data we were gathering was not being maximized to its full potential. I knew we could do more, I just did not know how.
This motivated me to learn Data Science. Data Science has been the talk of the town in recent years for a reason. Tech companies like Facebook and Google use our digital footprint for recommendation engines and targeted advertising; banks utilize it for fraud detection; the healthcare sector uses it for medical image analysis to detect illnesses. A lot of industries continue to benefit from the efficiency that data science brings. Except for the local water industry.
I joined the data science bootcamp of Eskwelabs knowing that I would choose a project related to water. I learned to create machine learning models, which basically means, training your machine to identify patterns by feeding it a bunch of data. Through machine learning, I was able to illustrate that water turbidity can be predicted. Predictions like this would be beneficial in making operational decisions. Effective forecasting would help us avoid situations that will take us by surprise, just like the water shortage.
Water utilities abroad use data science for extending equipment asset life, studying water consumption patterns, reducing non-revenue water, and more. We can also do the same.
This is why we should push for and support digital transformation in our own organizations. The more data we gather, the more possibilities for data science applications we can consider. However, data is like water. No matter how much we have, if we do not how to manage it wisely, how to store and clean it properly, we cannot use it.
When the water shortage happened, I was a mere data point. I felt helpless, but I decided to act on it. Today, I am still in the process of learning the ropes of data science. But, I aspire that one day, I can contribute to the water industry. Until then, I urge everyone to develop a data-driven mindset because it will take us closer to achieving digital water in our country.
I compute million liters of water on a daily basis and I go home to none.