The question of whether IoT can be used to drive social impact has been revolving around for a long time. However, the answer arrived from a pioneering startup named CirroLytix Research Services, which is leveraging machine learning on IoT data combined with public datasets to provide innovative solutions to concerns ranging from food, waste, traffic, and governance.
Dominic “Doc” Ligot founded CirroLytix Research Services back in 2016 to address a glaring gap he saw while working as an IT consultant.
“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.”
Ligot had already built a banking career working for institutions like HSBC and ANZ before deciding to get into IT. While working for global data warehousing and analytics company Teradata, he found the inspiration for 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.
“We were a hybrid – helping businesses leverage technology, and at the same time, helping IT and technology stakeholders translate their investments to business outcomes. As a small firm, we were usually a significant cost savings company compared to our peers, and the market welcomed us,” said Doc.
In designing the CirroLytix strategy, Ligot drew from his nearly two decades of experience working in banking and consulting experience across industries like telco, retail, CPG, oil and gas, and utilities, to build a business-centric and outcome-oriented offering based on three verticals:
- Strategic Consulting: It included maturity assessments, technology roadmaps, and data discovery;
- Business Analytics Masterclasses: These 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: This emphasized on building end-to-end data pipelines from data sourcing, to the storage, to machine learning, to IoT, sensors, and automation.
“The masterclass strategy proved to be a good entry point. We saw a demand of professionals looking for data training. While we weren’t a school, we were happy to teach what we know, and that proved to be a refreshing change in the market that was full of high-level fluffy analytics training. 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,” said Claire Tayco, Head of Analytics Research of CirroLytix.
Sensor Data
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.”
Using machine learning, CirroLytix combines traditional data such as financial data and maintenance records with business events, with feeds from sensors, and with time-series information from machine logs to create a path to an outcome from minutia and sequences of events.
Ligot shares: “The fundamental concept behind Predictive Maintenance, which Siemens pioneered, or next-best-product, which companies like eBay and Amazon are known for, is your ability to join together data on target events and sensor and machine logs that provide sequences of triggers that lead to those events. At CirroLytix, we are able to bring these fancy strategies within the reach and budget of even small and medium enterprises.”
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. Using machine learning on IoT data, CirroLytix is helping the public sector, social enterprises and non-profits tackle challenges such as food distribution, waste management, smart cities and governance, transportation, and mobility.
According to Claire, “Using analytics for social impact has always been what we’ve 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.”
Data Ethics
In the pivot to social value sees another industry gap that will be a major contention in the years to come: the ethical use of data.
“In many cases, data rights are now synonymous with human rights. Today, everyone is concerned about data privacy, but we feel the real problems are on how data is used, as seen in bias in algorithms, data-discrimination, and liabilities from data automation, and they should be top of the agenda. We’ve 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.”