by Jayne M. Consolacion

The need to explore and discover the potential of data science and artificial intelligence emerged long before the COVID-19 pandemic necessitated certain changes in the past year. The world’s fast-paced technological advancement and increasing access to data ushered in an opportunity for businesses to evolve—a specific pursuit for excellence that SN Aboitiz Power (SNAP) Group is now aiming for with its data science and AI enablement program, DARnA.

DARna or Data Acquisition, Reporting, and Analytics is SNAP’s vehicle to power its analytics capability further. Assistant Vice President and Chief Information Officer Mitch Cabigon elaborates that system “fuels SNAP’s data-driven vision of accelerating meaningful value creation for our business and people by transforming the way we think and operating through data-driven, smart, and innovative solutions.”

Through DARnA, SNAP strives for the following transformational targets: increase revenues through improvements in trading efficiency and commercial availability; create new products, services, and complementary business models; minimize opportunity losses; minimize the cost of asset life cycles; establish streamlined processes; and improve the overall quality of processing data for operational analysis and reports, which affords the company more time for value-adding analysis.

Among the DARnA Program’s major initiatives is the Data Science Upskilling Program (DSUP), which is offered to SNAP team members who want to up their game. DSUP aims to unify the company’s direction on its data science vision, strategy, and technical roadmap, thus future-proofing the business and its operations by making direct, hands-on participants out of the very people that make it up.

Mitch adds that instead of nominating potential candidates who may only feel “compelled” by it, one of the tactical strategies made was to encourage people to apply voluntarily. “This way, we get applicants who are really interested, excited, and psychologically ready to take on new challenges and learn new concepts to upskill themselves. Managing change for the contingent then is much easier than when you compel them to join.”

Since September 2020, 15 participants have signed up, passed the rigorous selection process, and are now undergoing the program. Their classes cover essential data science and AI subjects such as math and statistics, programming, data processing, machine learning, deep learning, AI research, communication, and visualization, and are taught by Aboitiz Data Science and AI mentors. The mentors have been guiding the contingent toward incorporating data science to reinforce their business acumen and eventually toward implementing their capstone projects that will focus on specific SNAP functions.

Just like the normal course of any major undertakings, the project had to overcome some hurdles. John Christian Lequiron, Data Science Senior Specialist, says, “I believe the main challenge of the project now is the needed man-hours for the contingent to attend the lectures, since they still need to do their assigned operational tasks. We had to do multiple revisions on the timeline so that we can accommodate requests and lessen the burden for everyone involved. We conduct training sessions once a week, every Monday, which corresponds to 20% of their work hours.”

The DARnA team takes much to pride on the program planning behind DSUP, citing little to no impact from the pandemic that upturned a lot of affairs last year. The successful project implementation is also strongly attributed to excellent change management, which was led by AboitizPower’s Change Manager Aileen Nagpala.

We can use data science to understand better our customers...and this then allows us to develop new product-service bundles, such as energy management contracts and smart agreements that allow us to break out of that commodity-price-volume game that we see out in the market. You can see the potential that data science holds inside SNAP, and how data science skills can become a very important competency in our organization.

Mitch likewise commends the dedication of the program’s first batch of participants. There is a difference, she notes, in having an interested, excited contingent who are psychologically ready to take on new challenges and learn new concepts to upskill themselves, from merely nominating and compelling people to join. Managing change for these contingents is much easier, and having team members who are willing to spend personal time to upskill in this particular field is an achievement in itself. She says that this dynamism from all levels in the organization is integral in keeping any program effective. “We want this upskilling—this change—to be sustainable.”

More than just improving its processes and exploring solutions, SNAP envisions a positive transformation that will spring from its first fifteen contingents to a possible inter-business unit data science network. It hopes to lay the groundwork for a data-driven interconnection of pooled resources, standardized training plans, and access to the most competent data platforms.

SNAP President and CEO Joseph Yu has also expressed his utmost support for the program. “Data science, artificial intelligence, and neural networks can help us improve short term and long term demand forecasting for electricity. You can see how much impact that can have on trading, production planning, contracting, and business development. For our asset management, data science can help us more optimally schedule our maintenance and downtime. We can use data science to understand better our customers—their needs, requirements, and preferences—and this then allows us to develop new product-service bundles, such as energy management contracts and smart agreements that allow us to break out of that commodity-price-volume game that we see out in the market. You can see the potential that data science holds inside SNAP, and how data science skills can become a very important competency in our organization.”

The program workshops of the first batch of DSUP will continue until May, at which point qualified trainees will get the chance to develop, test, and operationalize data science projects relevant to SNAP’s business operations. Much like the alluded superheroine, DARnA, even at its early stages, is already poised to be as iconic and pivotal in SNAP’s achievement of its vision.