The Philippine analytics industry is still in it’s infancy. There is a demand for the skills NOW, and this demand will grow even more in the coming years.
This is the key takeaway from the Big Data Analytics Conference 2016, held at Enderun Tent last 15 November 2016. It was the first conference of it’s kind and scale in the Philippines, gathering participants from the IT, business, academic, and health industries.
As someone considering a possible career shift, I wanted to find out if there will be a market to shift to, and what are the kind of skills they’re looking for.
Below are my notes from the event, along with some insights.
Keynote 1: Making Big Data & Analytics Work For You
by Isaac Reyes, Data Seer
In his keynote talk, Isaac explains why 55% of big data projects fail and makes recommendations on how not to.
He calls the data scientist a unicorn: difficult to find and even harder to keep. For most businesses starting out with analytics, investing in a data scientist will be too much of an overhead. Instead, he recommends to build out a data science team with distributed data science skills. e.g., Team members would include a statistics expert, a communicator, a programmer, a visualizer, etc.
This lessens the risk as even if a member were to leave, the team will still continue to exist and deliver. He recommends to take it a step further by building out the team from existing staff to ensure domain expertise, rather than hiring someone new.
He advocates Data Storytelling as the most valuable skill a data scientist can have. He recommends Knaflic’s book, “Storytelling with Data” as a must-read. And of course, plugged his own company’s training for data storytelling.
Insight: Isaac’s distributed model of data science resonates with my own interpretation of what the future of data science is like. I figure that with people being diverse (e.g., I’m better at communicating and visualizing than with traditional sciences like statistics), it would only be natural to collaborate and specialize.
I’m also excited to hear that data storytelling is his favored skill. It’s what got me interested in data science in the first place! His book recommendation is definitely making it into my Christmas list.
Panel 1: The Relevance and Impact of Big Data and Analytics in Philippines’ Business, Government, and Academic Sectors
by Monchito Ibrahim, Department of Information and Communications Technology (DICT)
Alejandro Melchor, REGENESYS Biotech Solutions Systems, Inc.
JP Domingo, Nielsen
Brenda Quismorio, University of Asia & the Pacific
In this panel they discussed the gap between the demand and supply of analytics professionals in the Philippines.
The demand is definitely there. The government is building out it’s analytics with a particular focus in data privacy and integrating multiple departments’ repositories.
More and more industries are discovering the need for analytics, not just IT. Some examples given were television (What are my viewers watching?), smart cities (Should I invest in IoT for my gadget?), and even sports (What’s the likelihood that my team will win?).
However in terms of education, universities are behind in being able to supply talent to meet these demands.
That isn’t to say there hasn’t been movement. IBM for example, has invested in UA&P to help them establish their Business Analytics course, only 30 students young so far.
But for an emerging market like the Philippines there needs to be more sponsors. The movement is there, but they aren’t moving fast enough.
Recommendations on how to address the gap:
- There are willing students, but no equipped teachers. Invest in faculty to teach the needed data science skills.
- Businesses should open up and become more involved in the school curriculum, allow for internships, and provide realistic test data for students to play around with.
- Do not limit the market to students, particularly statistics. Traits like being analytical and having attention to detail can be found across all backgrounds.
Insight: A topic close to my heart as my educational background is neither in statistics nor programming. My friends in the academe have said they’re trying to build out their analytics and data science curriculums, however in large (and archaic) institutions such as state universities making changes like these take time–and by then, it will be a missed opportunity.
I strongly agree with the third recommendation. That is, businesses should be more broad-minded when hiring for an analytics role. The title of “data scientist” only emerged recently, and yet the skills needed (analyzing, programming, visualizing, etc) have long been practiced.
This ties in nicely with Isaac’s recommendation of building out a distributed data science team. Instead of investing in highly-educated unicorns, businesses should hire people with the natural aptitude towards analytics and preferably with business context. No fancy titles required.
Breakout Session 1: Big Data and Analytics Tools
by Reinabelle Reyes, Data Scientist, Astrophysicist, Lecturer
Arturo Alcantara, Philippine Health Insurance Corporation (PhilHealth)
The key takeaway from this session is that the use case drives the choice of tools, not the other way around.
Doc Art walked the audience through the analytics journey of PhilHealth which had started almost a decade back. They made mistakes along the way, the key one being they invested in the tools first then retrofitted their people’s skills to fit.
This turned out to be a costly mistake as in the end, they decided to switch to a more use case-appropriate tool rendering the skill training useless.
He also recommended businesses to invest in data management and governance earlier rather than later.
Coming from a astrophysics background, Reinabelle is the quintessential data scientist. After all, what is big data if not the cosmos? It was no surprise that she had a lot to say about the pros and cons of the tools available, and which tools she personally uses and recommends.
To summarize: She favors open-source tools, and insists that any data scientist should know at least one of, but preferably both, Python and R.
Insight: Key takeaway would be that there’s no getting around it: I will definitely have to learn to code. I know a little of it, particularly in UNIX, but it’s not something that had interested me in the past. I recall writing scripts out of frustration as I didn’t like doing repetitive tasks!
Breakout Session 2B: Exploring Career Paths in Analytics
In general, Accenture’s data science department(?) has three major teams:
- Engineering – Or their technology team. This team creates and manages the tools and scripts used by the other teams.
- Operations – Or their data analytics team. They use the tools and scripts delivered by the Engineering team to process and analyze their clients’ data.
- Client-facing – Once processed, this team presents the analyses to their client along with some recommendations. Among all the teams, this is the team with domain expertise and specializes in visualization.
Insight: During the Q&A portion, a participant asked how a technical guy like himself could shift into an analytics career. The panel immediately responded with the question, “What’s your course?”
This made me uncomfortable. Were they implying that a course chosen in high school played a more important role than years of hands-on experience in the industry?
With such an intense focus on educational background, I don’t think Accenture is a good cultural fit for me. But it was nice to know that there was at least one big company in the Philippines that had a mature analytics strategy.
Oh and in case you’re interested, the answer was for the guy to try out the engineering career path as it would make use of his database skills. Also to not worry too much about the tools as those can be taught along the way.
And that he should apply to Accenture.
Panel 3: The Future of Big Data and Analytics in the Philippines
by Daniel Meyer, DMAIPH and Sonic Analytics
Dominic Ligot, Teradata
Toti Casino, PICAB, UA&P
Sherwin Pelayo, Pointwest
Derya Tanghe, NXTLVL Academy
The Philippines is expected to have a shortage of IT graduates by 2019, due to the K-12 transition a few years back. How then are we expected to meet the increasing demand for analytics?
A few recommendations were thrown about, but one that stood out for me was that key skills such as critical thinking, analysis, and communication are much more valuable than the technical skills taught in university.
Another was to not to get caught up with all the job titles and certifications (such as the glorification of the data scientist). We would be in danger of repeating what happened to six sigma a few years back: making it elitist and silo’d such that the general public found it inaccessible.
The biggest challenge now would be to get stakeholders, the Philippine market, to invest in big data.
Insight: This was a great conclusion to the conference as it tied in topics from all the previous sessions.
Again, we see the Philippines’ obsession with the college degree; something I don’t think will be going away any time soon. However, it’s nice to hear that there are people in the industry who aren’t as obsessed.
I’m hoping that devil’s advocate is a good representation of the market as that’s definitely the kind of encouragement I need in order to continue with the DS Program.
Networking & Conclusion
One of the key reasons for holding conferences and meetups like this is for people in the industry to mingle and network.
Sharing stories and backgrounds with these new people made the whole conference experience much more memorable because aside from the sessions, I walked away with personal stories I could relate to (names changed to respect their privacy):
- Shae is a working mom helping with her daughter’s emerging career. The daughter had written a fiction book which they were looking to self-publish and sell online. Shae attended the conference to gain some insight on how they can use analytics in online publishing.
- Joey is an analytics professional who attended the conference to see if there was anything new in the industry. Coincidentally enough, Joey and I graduated from the same university at around the same time, but our paths diverged from there. Unlike my own rocky entry to the workforce, Joey was recruited for an analytics role while she was still in uni.
- Dora and I had a lot in common. We’re both in the tech industry (she’s a software QA engineer), and both attended the conference out of curiosity: Is this something we can shift our careers to? In Dora’s case, she was interested in the security and privacy aspects of big data.
Overall, I’m quite happy to have attended the conference. I learned a lot, and gained insights I would probably not have gained elsewhere. I now know there is a market out there for big data analytics, and that I definitely have to skill up for it.
I’m also happy to learn that it’s okay for me to love the data visualization aspects of big data over the programming. There will still be a role for me too.
For more info, you can visit bigdataconferenceph.com. Or download the app available in both iOS and Android.
Update: The presentation slides from the conference are now available for download at http://www.bigdataconferenceph.com/slides.
Image: Solar system and comets in relation to other solar systems, about 1780. Ref F2776.