Somewhere in the heart of the bustling and crowded Manila, a pioneering startup is hard at work, figuring out answers to various social challenges in the Philippines. Their approach: leverage machine learning on IoT data combined with public datasets and provide innovative solutions to concerns ranging from food, waste, traffic, and governance.
The startup is CirroLytix Research Services, the brainchild of Dominic “Doc” Ligot, who founded it in 2016 to cater to the emerging data science and analytics demand in the Philippines. Doc had already built a banking career working for institutions such as HSBC and ANZ when he decided to get into IT. While working for global data warehousing and analytics company Teradata, he saw what he felt was a glaring gap.
“Those who had the data didn’t know how to generate insights; those who had insights didn’t know how to translate them into action. IT providers were good with technology but didn’t have the business chops to dispense advice, while there were consultants who had a lot of ideas but couldn’t execute them through technology,” reveals Doc, Founder and CTO, CirroLytix.
Hybrid Design, Outcome-based Execution
Literally meaning “(Cirrus) Cloud-based Analytics”, CirroLytix pushes cloud, IoT, and machine learning solutions in a market that still uses traditional on-premises statistical solutions based on conventional structured datasets. In designing the CirroLytix strategy, Ligot drew on his nearly two decades of experience in banking and consulting across industries such as telco, retail, CPG, oil and gas, and utilities, to build a business-centric and outcome-oriented offering.
“We would approach companies as a convenient hybrid, helping business clients leverage technology, at the same time, assisting IT and technology stakeholders in translating their investments to business outcomes. Also, being a small firm, we would bring in a significant cost savings compared to our peers, and this worked out almost immediately,” says Doc.
CirroLytix evolved its business model on three verticals:
• Strategic Consulting, which included maturity assessments, technology roadmaps, and data discovery;
• Analytics Masterclasses, which were practical hands-on and strategic workshops targeted to business and technology executives, with the aim of bootstrapping data-driven transformation and solution prototyping for companies; and
• Data Engineering, which emphasized building end-to-end data pipelines from data sourcing and storage to machine learning, IoT, sensors and automation.
Claire Tayco was already a career banker with expertise in risk analytics and statistical modeling and was finishing grad school when Doc approached her to form CirroLytix. Claire, who heads analytics research at CirroLytix, shares how training was a fortunate turn of events.
Frances Claire Tayco , Head of Research and Analytics
“The masterclass strategy proved to be a good entry point. We saw a demand of professionals looking for data training. While we were not a school, we were happy to teach what we know, and that proved to be a refreshing change in the market for analytics training that was full of high-level fluffy offerings. We saw an opportunity to offer a masterclass that was not just focused on algorithms and visualizations, but on how to use data to answer business questions,” says Claire, head of research and analytics, CirroLytix.
Doc adds, “While the training was a great lead generator, our clients end up sticking to us because of our end-to-end approach. We could come in at any point on the data value chain from extraction to database management, from analysis to strategy. Clients saw that we could fill a gap and it is usually not one-size-fits-all.”
As CirroLytix was starting to build its analytics portfolio, the IoT landscape in the Philippines was also maturing, and Doc saw an opportunity to double-down by offering analytics solutions on IoT data.
“Most solutions providers in the IoT space were infrastructure oriented, the data feeds were incidental. However, to a data analyst, sensor data and machine logs are a rich source of event data—the ‘heartbeats’ of a network—and these could form the foundation for interesting types of analysis,” informs Doc.
Using machine learning, CirroLytix combines traditional data such as financial data and maintenance records with business events, feeds from sensors, and time-series information from machine logs to create a path to an outcome from minutia and sequences of events. Doc shares that they are able to bring sophisticated analytics use-cases to their clients such as Predictive Maintenance pioneered by Siemens, and next-best product recommendations performed by Amazon and eBay, along with more conventional forecasting and business dashboards.
From Data Value to Social Impact
Having built a successful record in commercial projects, Doc’s team has now set its eyes on social value projects.
According to Claire, “Using analytics for social impact has always been what we have wanted to do since CirroLytix was formed, but we felt the market was not ready when we started. Now we see social impact as an exciting place because the data is available, and to our pleasant surprise, we found a high willingness of public sector and non-profit partners to explore new technologies.”
Using machine learning on IoT data, CirroLytix is helping public sector, social enterprises, and non-profit organizations tackle challenges pertaining to food distribution, waste management, smart cities, smart governance, transportation and mobility.
The pivot to social value has highlighted another market gap that Doc sees will be a major contention in the years to come: the ethical use of data.
“In many cases, data rights are now synonymous to human rights. Today, everyone is concerned about data privacy, but we feel the real problems are on the inappropriate use of data as seen in the bias in algorithms, data-discrimination, and liabilities from data automation. They should be top of the agenda. We have always had an ethical lens underpinning our practice, and we think this will be our competitive advantage in the era of the Fourth Industrial Revolution,” concludes Doc.